diff --git "a/buster/data/document_embeddings.csv" "b/buster/data/document_embeddings.csv"
--- "a/buster/data/document_embeddings.csv"
+++ "b/buster/data/document_embeddings.csv"
@@ -1,11 +1,38 @@
-,documents,n_tokens,embedding
-0,"Login nodes. To connect to these, you would typically
+name,url,text,n_tokens,embedding
+AI tooling and methodology handbook,https://docs.mila.quebec/Handbook.html#ai-tooling-and-methodology-handbook,"AI tooling and methodology handbook
+This section seeks to provide researchers with insightful articles pertaining to
+aspects of methodology in their work.
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+What is a computer cluster?,https://docs.mila.quebec/Theory_cluster.html#what-is-a-computer-cluster,"What is a computer cluster?
+A computer cluster is a set
+of loosely or tightly connected computers that work together so that, in many
+respects, they can be viewed as a single system.
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+Parts of a computing cluster,https://docs.mila.quebec/Theory_cluster.html#parts-of-a-computing-cluster,"Parts of a computing cluster
+To provide high performance computation capabilities, clusters can
+combine hundreds to thousands of computers, called nodes, which are all
+inter-connected with a high-performance communication network. Most nodes are
+designed for high-performance computations, but clusters can also use
+specialized nodes to offer parallel file systems, databases, login nodes and
+even the cluster scheduling functionality as pictured in the image below.
+
+We will overview the different types of nodes which you can encounter on a
+typical cluster.
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+The login nodes,https://docs.mila.quebec/Theory_cluster.html#the-login-nodes,"The login nodes
+To execute computing processes on a cluster, you must first connect to a
+cluster and this is accomplished through a login node. These so-called
+login nodes are the entry point to most clusters.
+Another entry point to some clusters such as the Mila cluster is the JupyterHub
+web interface, but we’ll read about that later. For now let’s return to the
+subject of this section; Login nodes. To connect to these, you would typically
 use a remote shell connection. The most usual tool to do so is SSH. You’ll hear
 and read a lot about this tool. Imagine it as a very long (and somewhat
 magical) extension cord which connects the computer you are using now, such as
 your laptop, to a remote computer’s terminal shell. You might already know what
-a terminal shell is if you ever used the command line.",96,"[-0.004753311164677143, -0.005505518522113562, 0.023699335753917694, -0.01755364052951336, -0.024416334927082062, -0.005828807596117258, -0.01752803474664688, -0.01659337617456913, 0.004583664238452911, -0.015133773908019066, 0.016644591465592384, 0.01134392898529768, -0.03477438911795616, 0.008943266235291958, -0.010601324029266834, 0.006824281997978687, 0.025709491223096848, -0.015018542297184467, 0.026144810020923615, -0.020472846925258636, -0.019435759633779526, -0.009871522895991802, 0.0011699226452037692, -0.01728476770222187, 0.009128917939960957, 0.00809183157980442, 0.011696025729179382, -0.018501102924346924, 0.008495142683386803, -0.014135098084807396, 0.012080132029950619, -0.004452427849173546, -0.009769094176590443, -0.015953199937939644, -0.005732781253755093, 0.038922734558582306, 0.012349005788564682, -0.02998586930334568, -0.042610153555870056, -0.017924943938851357, 0.023763353005051613, 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-1,"The compute nodes
+a terminal shell is if you ever used the command line.
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+The compute nodes,https://docs.mila.quebec/Theory_cluster.html#the-compute-nodes,"The compute nodes
 In the field of artificial intelligence, you will usually be on the hunt for
 GPUs. In most clusters, the compute nodes are the ones with GPU capacity.
 While there is a general paradigm to tend towards a homogeneous configuration
@@ -15,12 +42,14 @@ on. Hence, you will often read about computational node classes. Some of which
 might have different GPU models or even no GPU at all. For the Mila cluster you
 will find this information in the Node profile description section. For
 now, you should note that is important to keep in mind that you should be aware
-of which nodes your code is running on.  More on that later.",165,"[0.0021230564452707767, -0.008092104457318783, -0.0010986083652824163, -0.04652442783117294, -0.008209381252527237, 0.02624242566525936, -0.031375013291835785, 0.005184327717870474, -0.00741603784263134, -0.03333422914147377, 0.01724660024046898, 0.012500335462391376, -0.0043288967572152615, -0.011375857517123222, -0.008802664466202259, 0.00437373761087656, 0.014983845874667168, 0.0020178521517664194, 0.014638913795351982, -0.016377370804548264, -0.02333120070397854, 0.0018816040828824043, -0.000400336692109704, -0.02219982258975506, -0.02362094260752201, 0.010575614869594574, 0.029029477387666702, -0.01619800738990307, -0.000224421382881701, -0.0017082757549360394, 0.01216920092701912, -0.016901668161153793, -0.003552799578756094, -0.02219982258975506, -0.010734283365309238, 0.01741216704249382, 0.03115425817668438, -0.02511104941368103, -0.007588503882288933, -0.008195584639906883, 0.03449320048093796, 0.004866990260779858, 0.0011063694255426526, 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-2,"The storage nodes
+of which nodes your code is running on.  More on that later.
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+The storage nodes,https://docs.mila.quebec/Theory_cluster.html#the-storage-nodes,"The storage nodes
 Some computers on a cluster function to only store and serve files.  While the
 name of these computers might matter to some, as a user, you’ll only be
-concerned about the path to the data. More on that in the Processing data section.",60,"[-0.010172166861593723, -0.0063959332183003426, 0.02501046471297741, -0.03330284729599953, -0.009625562466681004, 0.028396742418408394, -0.01505160704255104, -0.013618438504636288, 0.013298475183546543, -0.02485048398375511, 0.00816573016345501, 0.012411910109221935, -0.009952192194759846, 0.011172052472829819, -0.008592347614467144, 0.011105393059551716, 0.010858754627406597, -0.020370997488498688, 0.007185842376202345, -0.006645904388278723, -0.00845236424356699, -0.0021914152894169092, -0.012178603559732437, -0.004746122285723686, -0.012805198319256306, -0.007479142397642136, 0.04682129621505737, -0.031089767813682556, 0.02118423767387867, -0.01229192316532135, 0.0020331002306193113, -0.04300840198993683, 0.001486496184952557, -0.024103902280330658, -0.017717968672513962, -0.009818874299526215, 0.012438573874533176, -0.031729694455862045, 0.0036495814565569162, -0.020864274352788925, 0.01657143235206604, -0.024170560762286186, 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-3,"Different nodes for different uses
+concerned about the path to the data. More on that in the Processing data section.
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+Different nodes for different uses,https://docs.mila.quebec/Theory_cluster.html#different-nodes-for-different-uses,"Different nodes for different uses
 It is important to note here the difference in intended uses between the
 compute nodes and the login nodes. While the compute nodes are meant for heavy
 computation, the login nodes are not.
@@ -29,8 +58,109 @@ be taken not to overburden these nodes. Consequently, only very short and light
 processes should be run on these otherwise the cluster may become inaccessible.
 In other words, please refrain from executing long or compute intensive
 processes on login nodes because it affects all other users. In some cases, you
-will also find that doing so might get you into trouble.",138,"[-4.763212564284913e-05, -0.01553431712090969, 0.015028802677989006, -0.03858303651213646, -0.015875881537795067, 0.010636293329298496, -0.02806287445127964, 0.00893530622124672, -0.0031594643369317055, -0.0371348038315773, 0.00720016285777092, 0.005769011098891497, -0.010062466375529766, 0.0037298756651580334, -0.010984688997268677, 0.0056426324881613255, 0.021231599152088165, -0.025986166670918465, 0.010465512052178383, -0.01971505768597126, -0.020534809678792953, 0.010793413035571575, 0.0001030561761581339, 0.007944772019982338, -0.011633659712970257, 0.007425595074892044, 0.02504345029592514, -0.020042957738041878, 0.0022287031169980764, -0.014837527647614479, 0.0003035647387150675, -0.00758954556658864, -0.0018324892735108733, -0.0033917275723069906, -0.014714564196765423, 0.023991433903574944, 0.01613546907901764, -0.01855373941361904, -0.010697775520384312, -0.025740239769220352, 0.014086087234318256, 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-4,"Data parallelism
+will also find that doing so might get you into trouble.
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+UNIX,https://docs.mila.quebec/Theory_cluster.html#unix,"UNIX
+All clusters typically run on GNU/Linux distributions. Hence a minimum
+knowledge of GNU/Linux and BASH is usually required to use them. See the
+following tutorial
+for a rough guide on getting started with Linux.
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+The workload manager,https://docs.mila.quebec/Theory_cluster.html#the-workload-manager,"The workload manager
+On a cluster, users don’t have direct access to the compute nodes but
+instead connect to a login node and add jobs to the workload manager
+queue. Whenever there are resources available to execute these jobs
+they will be allocated to a compute node and run, which can be
+immediately or after a wait of up to several days.
+A job is comprised of a number of steps that will run one after the
+other. This is done so that you can schedule a sequence of processes
+that can use the results of the previous steps without having to
+manually interact with the scheduler.
+Each step can have any number of tasks which are groups of processes
+that can be scheduled independently on the cluster but can run in
+parallel if there are resources available. The distinction between
+steps and tasks is that multiple tasks, if they are part of the same
+step, cannot depend on results of other tasks because there are no
+guarantees on the order in which they will be executed.
+Finally each process group is the basic unit that is scheduled in the
+cluster. It comprises of a set of processes (or threads) that can run
+on a number of resources (CPU, GPU, RAM, …) and are scheduled
+together as a unit on one or more machines.
+Each of these concepts lends itself to a particular use. For multi-gpu
+training in AI workloads you would use one task per GPU for data
+paralellism or one process group if you are doing model
+parallelism. Hyperparameter optimisation can be done using a
+combination of tasks and steps but is probably better left to a
+framework outside of the scope of the workload manager.
+If this all seems complicated, you should know that all these things
+do not need to always be used. It is perfectly acceptable to sumbit
+jobs with a single step, a single task and a single process.
+The available resources on the cluster are not infinite and it is the
+workload manager’s job to allocate them. Whenever a job request comes
+in and there are not enough resources available to start it
+immediately, it will go in the queue.
+Once a job is in the queue, it will stay there until another job
+finishes and then the workload manager will try to use the newly freed
+resources with jobs from the queue. The exact order in which the jobs
+will start is not fixed, because it depends on the local policies
+which can take into account the user priority, the time since the job
+was requested, the amount of resources requested and possibly other
+things. There should be a tool that comes with the manager where you
+can see the status of your queued jobs and why they remain in the
+queue.
+The workload manager will divide the cluster into partitions according
+to the configuration set by the admins. A partition is a set of
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+The workload manager,https://docs.mila.quebec/Theory_cluster.html#the-workload-manager,"nes typically reserved for a particular purpose. An example might
+be CPU-only machines for preprocessing setup as a separate partition.
+It is possible for multiple partitions to share resources.
+There will always be at least one partition that is the default
+partition in which jobs without a specific request will go. Other
+partitions can be requested, but might be restricted to a group of
+users, depending on policy.
+Partitions are useful for a policy standpoint to ensure efficient use
+of the cluster resources and avoid using up too much of one resource
+type blocking use of another. They are also useful for heterogenous
+clusters where different hardware is mixed in and not all software is
+compatible with all of it (for example x86 and POWER cpus).
+To ensure a fair share of the computing resources for all, the workload
+manager establishes limits on the amount of resources that a single
+user can use at once. These can be hard limits which prevent running
+jobs when you go over or soft limits which will let you run jobs, but
+only until some other job needs the resources.
+Admin policy will determine what those exact limits are for a
+particular cluster or user and whether they are hard or soft limits.
+The way soft limits are enforced is using preemption, which means that
+when another job with higher priority needs the resources that your
+job is using, your job will receive a signal that it needs to save its
+state and exit. It will be given a certain amount of time to do this
+(the grace period, which may be 0s) and then forcefully terminated if
+it is still running.
+Depending on the workload manager in use and the cluster configuration
+a job that is preempted like this may be automatically rescheduled to
+have a chance to finish or it may be up to the job to reschedule
+itself.
+The other limit you can encounter with a job that goes over its
+declared limits. When you schedule a job, you declare how much
+resources it will need (RAM, CPUs, GPUs, …). Some of those may have
+default values and not be explicitely defined. For certain types of
+devices, like GPUs, access to units over your job limit is made
+unavailable. For others, like RAM, usage is monitored and your job
+will be terminated if it goes too much over. This makes it important
+to ensure you estimate resource usage accurately.
+Mila as well as Digital Research Alliance of Canada use the workload
+manager Slurm to schedule and
+allocate resources on their infrastructure.
+Slurm client commands are available on the login nodes for you to submit
+jobs to the main controller and add your job to the queue. Jobs are of 2 types:
+batch jobs and interactive jobs.
+For practical examples of Slurm commands on the Mila cluster, see Running your code.",576,"[0.023459509015083313, -0.02788141369819641, -0.0015798663953319192, -0.026572750881314278, -0.01687486656010151, 0.00878181867301464, -0.022908493876457214, 0.014684578403830528, -0.01766006462275982, -0.042207829654216766, 0.014877433888614178, 0.01794934831559658, -0.0021196899469941854, 0.0012785295257344842, -0.01754986122250557, 0.0048627168871462345, 0.027013562619686127, -0.019478417932987213, -0.0005828716675750911, -0.01867944560945034, -0.008513198234140873, 0.008141262456774712, -0.007941518910229206, -0.0060232942923903465, -0.02423093095421791, 0.014057797379791737, 0.0399211160838604, -0.005961304996162653, -0.002495069755241275, 8.3836232079193e-05, -0.007728000171482563, 0.002024983987212181, -0.010427978821098804, -0.011233840137720108, -0.028459979221224785, 0.005913091357797384, 0.016227424144744873, -0.02216462232172489, 0.0017279519233852625, -0.02781253680586815, 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+Processing data,https://docs.mila.quebec/Theory_cluster.html#processing-data,"Processing data
+For processing large amounts of data common for deep learning, either
+for dataset preprocessing or training, several techniques exist. Each
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+Data parallelism,https://docs.mila.quebec/Theory_cluster.html#data-parallelism,"Data parallelism
 The first technique is called data parallelism (aka task
 parallelism in formal computer science). You simply run lots of
 processes each handling a portion of the data you want to
@@ -49,8 +179,9 @@ Finally another thing to keep in mind is that the transfer bandwidth
 is limited between the filesystems (see Filesystem concerns)
 and the compute nodes and if you run too many jobs using too much data
 at once they may end up not being any faster because they will spend
-their time waiting for data to arrive.",249,"[-0.029732953757047653, -0.00016010800027288496, 0.018897606059908867, -0.025160733610391617, -0.001895509660243988, 0.030571645125746727, -0.011572585441172123, -0.017544876784086227, -0.01997978799045086, -0.055678270757198334, 0.0029438736382871866, 0.022333532571792603, -0.0006281728856265545, -0.004673674236983061, 0.007730838377028704, 0.007622620090842247, 0.031058626249432564, -0.000368829641956836, 0.002262437017634511, -0.008055493235588074, -0.022536441683769226, 0.000889418413862586, -0.0017551641212776303, -0.005295928567647934, -0.03692946583032608, 0.015718694776296616, 0.04691259562969208, -0.008326038718223572, -0.004176546819508076, -0.005191092379391193, 0.00039863193524070084, -0.027730917558073997, -0.0028965279925614595, -0.010172511450946331, -0.0079878568649292, -0.011890475638210773, 0.02461964264512062, -0.014014258049428463, 0.004149491898715496, -0.012147494591772556, 0.009969602338969707, -0.01602982170879841, 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+Model parallelism,https://docs.mila.quebec/Theory_cluster.html#model-parallelism,"Model parallelism
 The second technique is called model parallelism (which doesn’t
 have a single equivalent in formal computer science). It is used
 mostly when a single instance of a model will not fit in a computing
@@ -60,8 +191,9 @@ In this case, the model is split into its constituent parts, each
 processed independently and their intermediate results communicated
 with each other to arrive at a final result.
 This is generally harder but necessary to work with larger, more
-powerful models like GPT.",116,"[-0.017057692632079124, 0.006039037834852934, 0.017525576055049896, -0.01827418990433216, 0.0004373874398879707, 0.029035506770014763, -0.0017328396206721663, -0.015774354338645935, -0.021509269252419472, -0.05694809556007385, 0.02685650810599327, 0.0364949069917202, -0.009330932050943375, 0.00040501158218830824, -0.0036929368507117033, -0.004224318545311689, 0.019811520352959633, 0.008756103925406933, 0.023140177130699158, 0.014758379198610783, 0.0014161828439682722, 0.006069116294384003, -0.0026518963277339935, -0.024303201586008072, -0.00859568640589714, 0.02573358826339245, 0.04609319940209389, -0.013461673632264137, 0.0005234445561654866, 0.0009449573699384928, -0.006396634504199028, -0.023888789117336273, -0.010346907190978527, -0.018955962732434273, -0.008214695379137993, -0.0074593983590602875, 0.027217445895075798, 0.00179968005977571, 4.6396697143791243e-05, 0.0003126881492789835, 0.011142308823764324, 0.008756103925406933, 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-6,"Communication concerns
+powerful models like GPT.
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+Communication concerns,https://docs.mila.quebec/Theory_cluster.html#communication-concerns,"Communication concerns
 The main difference of these two approaches is the need for
 communication between the multiple processes. Some common training
 methods, like stochastic gradient descent sit somewhere between the
@@ -81,8 +213,9 @@ optimal communication.  What is the best allocation depends on the
 specific cluster architecture available and the technologies it
 support (such as InfiniBand,
 RDMA,
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+Filesystem concerns,https://docs.mila.quebec/Theory_cluster.html#filesystem-concerns,"Filesystem concerns
 When working on a cluster, you will generally encounter several
 different filesystems.  Usually there will be names such as ‘home’,
 ‘scratch’, ‘datasets’, ‘projects’, ‘tmp’.
@@ -113,15 +246,28 @@ will also not have backups. For those it is important to maintain a
 copy of any crucial data somewhere else. The data will not be
 purposefully deleted, but the filesystem may fail and lose all or part
 of its data. If you have any data that is crucial for a paper or your
-thesis keep an additional copy of it somewhere else.",424,"[0.004579077009111643, -0.00905810110270977, 0.010225382633507252, -0.049812886863946915, -0.023479025810956955, 0.01619519107043743, -0.007283833809196949, -0.004082148429006338, 0.012373179197311401, -0.029455503448843956, 0.012333158403635025, 0.0005452870391309261, 0.008431104011833668, 0.00556959817185998, -0.0063233282417058945, 0.0029432158917188644, 0.03263050690293312, 0.00912480242550373, -0.010465508326888084, -0.03668597713112831, -0.002759785857051611, 0.0021294541656970978, -0.026240479201078415, -0.005576268304139376, -0.007650693412870169, 0.004212216939777136, 0.0165020190179348, -0.004202211741358042, -0.0003512265393510461, 0.00572968227788806, 0.007403897121548653, -0.015648236498236656, -0.00832438189536333, -0.012346498668193817, -0.033751096576452255, -0.006136563140898943, 0.021357908844947815, -0.017756013199687004, 0.0036419169045984745, -0.003296735230833292, 0.015341408550739288, 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+Software on the cluster,https://docs.mila.quebec/Theory_cluster.html#software-on-the-cluster,"Software on the cluster
+This section aims to raise awareness to problems one can encounter when trying
+to run a software on different computers and how this is dealt with on typical
+computation clusters.
+The Mila cluster and the Digital Research Alliance of Canada clusters both
+provide various useful software and computing environments, which can be
+activated through the module system. Alternatively, you may build containers
+with your desired software and run them on compute nodes.
+Regarding Python development, we recommend using virtual environments to install
+Python packages in isolation.
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+Cluster software modules,https://docs.mila.quebec/Theory_cluster.html#cluster-software-modules,"Cluster software modules
 Modules are small files which modify your environment variables to point to
 specific versions of various software and libraries. For instance, a module
 might provide the python command to point to Python 3.7, another might
 activate CUDA version 11.0, another might provide the torch package, and so
 on.
-For more information, see The module command.",79,"[-0.0077234129421412945, -0.0229937806725502, 0.0047304206527769566, -0.037001799792051315, -0.026998011395335197, 0.021921461448073387, -0.0010375366546213627, 0.010085229761898518, -0.007350137457251549, -0.014713848009705544, 0.029373401775956154, -0.010689257644116879, -0.010017361491918564, 0.005734871607273817, -0.026400769129395485, -0.005368382669985294, 0.010200605727732182, -0.011286498978734016, 0.00342904613353312, -0.004879731219261885, -0.015066763386130333, 0.004435193724930286, -0.012467407621443272, -0.012094131670892239, -0.010668897069990635, 0.015772594138979912, 0.013478645123541355, -0.01370261050760746, 0.02025190182030201, -0.0012190843699499965, 0.016139082610607147, -0.008863599970936775, -0.007384071592241526, -0.01330218743532896, -0.023360269144177437, -0.00025896000443026423, 0.010030935518443584, -0.027188042178750038, -0.02152782492339611, 0.01865020953118801, 0.01756431721150875, 0.0217178575694561, 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-9,"Containers
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+Containers,https://docs.mila.quebec/Theory_cluster.html#containers,"Containers
 Containers are a special form of isolation of software and its dependencies. A
 container is essentially a lightweight virtual machine: it encapsulates a
 virtual file system for a full OS installation, as well as a separate network
@@ -130,17 +276,33 @@ For example, you can create an Ubuntu container in which you install various
 packages using apt, modify settings as you would as a root user, and so on,
 but without interfering with your main installation. Once built, a container can
 be run on any compatible system.
-For more information, see Using containers on clusters.",117,"[0.003933650907129049, -0.014275675639510155, 0.020411351695656776, -0.020452257245779037, -0.01805252581834793, 0.003718902124091983, -0.008487686514854431, 0.0059311543591320515, -0.0015859020641073585, -0.009776178747415543, 0.019334200769662857, 0.03834116458892822, -0.012380433268845081, -0.0067662885412573814, 0.0009109776001423597, 0.009823900647461414, 0.03193278983235359, -0.008617217652499676, -0.008671756833791733, -0.013505307026207447, -0.02131125144660473, 0.0007047507097013295, -0.003340535331517458, -0.009510299190878868, -0.02297470159828663, 0.011323733255267143, 0.017411688342690468, -0.02991483360528946, 0.01439838856458664, -0.02773326076567173, 0.020424988120794296, -0.004243843257427216, -0.029805755242705345, -0.012639494612812996, -0.015080130659043789, 0.0005799066857434809, 0.0036336842458695173, -0.03348716348409653, -0.010676078498363495, -0.003923424519598484, 0.02382006123661995, 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-10,"Python Virtual environments
+For more information, see Using containers on clusters.
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+Python Virtual environments,https://docs.mila.quebec/Theory_cluster.html#python-virtual-environments,"Python Virtual environments
 A virtual environment in Python is a local, isolated environment in which you
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 environment (or other virtual environments). In order to use a virtual
 environment, you first have to activate it.
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-12,"The IDT team
-See https://mila.quebec/en/mila/team/?cat_id=143",24,"[-0.0011296229204162955, -0.012272964231669903, 0.005210418254137039, -0.009561169892549515, -0.010672446340322495, 0.030137255787849426, -0.022029830142855644, -0.018716968595981598, -0.0018433908699080348, -0.00495880888774991, 0.026880307123064995, -0.00414806604385376, -0.007076524198055267, -0.028124378994107246, -0.013370262458920479, -0.006402070168405771, 0.004846982192248106, -0.011042872443795204, -0.0014502506237477064, -0.0066956146620213985, -0.008960102684795856, 0.013663806952536106, 0.026265261694788933, -0.0020530656911432743, -0.017598703503608704, -0.014942822977900505, -0.0046792421489953995, -0.02324594557285309, 0.012119202874600887, 0.005469017196446657, 0.00818430632352829, -0.0162567850202322, -0.009679986163973808, -0.009379452094435692, -0.01899653486907482, -0.019276103004813194, 0.009064939804375172, -0.017053550109267235, 0.0038545215502381325, -0.020436301827430725, 0.026167413219809532, 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-13,"Intended audience
+For more information, see Virtual environments.
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+"Who, what, where is IDT",https://docs.mila.quebec/IDT.html#who-what-where-is-idt,"Who, what, where is IDT
+This section seeks to help Mila researchers understand the mission and role of
+the IDT team.
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+The IDT team,https://docs.mila.quebec/IDT.html#the-idt-team,"The IDT team
+See https://mila.quebec/en/mila/team/?cat_id=143
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+Purpose of this documentation,https://docs.mila.quebec/Purpose.html#purpose-of-this-documentation,"Purpose of this documentation
+This documentation aims to cover the information required to run scientific
+and data-intensive computing tasks at Mila and the available resources for its
+members.
+It also aims to be an outlet for sharing know-how, tips and tricks and examples
+from the IDT team to the Mila researcher community.
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+Intended audience,https://docs.mila.quebec/Purpose.html#intended-audience,"Intended audience
 This documentation is mainly intended for Mila researchers having access to the
 Mila cluster. This access is determined by your researcher status. See
 Roles and authorizations for more information. The core of the
@@ -149,8 +311,149 @@ Computing infrastructure and policies.
 However, we also aim to provide more general information which can be useful
 outside the scope of using the Mila cluster. For instance, more general theory
 on computational considerations and such. In this perspective, we hope the
-documentation can be of use for all of Mila members.",124,"[-0.008576859720051289, -0.00037237806827761233, 0.007361349184066057, -0.05626856908202171, -0.004360130988061428, 0.01847848854959011, -0.031739842146635056, -0.0055517409928143024, -0.023777566850185394, -0.02558034658432007, 0.0228078905493021, -0.017222005873918533, 0.008105678483843803, -0.0007682298310101032, -0.02969123050570488, 0.0014075677609071136, 0.03135743364691734, -0.027314839884638786, -0.002818549983203411, -0.0017737573944032192, -0.010816676542162895, 0.003470691852271557, -0.011881954967975616, 0.01033866684883833, -0.016320616006851196, 0.006623848341405392, 0.017331264913082123, -0.033242158591747284, -0.0016423047054558992, -0.011875126510858536, 0.01976228691637516, -0.004049425013363361, 0.0021049499046057463, -0.01017477735877037, -0.012954062782227993, -0.001918867463245988, 0.02969123050570488, -0.02115534245967865, -0.015350939705967903, 0.0038718783762305975, 0.02198844589293003, 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-14,"Connecting to the cluster
+documentation can be of use for all of Mila members.
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+Contributing,https://docs.mila.quebec/Purpose.html#contributing,"Contributing
+See the following file for contribution guidelines :
+# Contributing to the Mila Docs
+
+Thank you for your interest into making a better documentation for all at Mila.
+
+Here are some guidelines to help bring your contributions to life.
+
+## What should be included in the Mila Docs
+
+* Mila cluster usage
+* Digital Research Alliance of Canada cluster usage
+* Job management tips / tricks
+* Research good practices
+* Software development good practices
+* Useful tools
+
+**_NOTE_**: Examples should aim to not consume much more than 1 GPU/hour and 2 CPU/hour
+
+## Issues / Pull Requests
+
+### Issues
+
+Issues can be used to report any error in the documentation, missing or unclear
+sections, broken tools or other suggestions to improve the overall
+documentation.
+
+### Pull Requests
+
+PRs are welcome and we value the contents of contributions over the appearance
+or functionality of the pull request. If you don't know how to write the proper
+markup in reStructuredText, simply provide the content you would like to add in
+the PR text form which supports markdown or with instructions to format the
+content. In the PR, reference the related issues like this:
+
+```
+Resolves: #123
+See also: #456, #789
+```
+
+If you would like to contribute directly in the code of the documentation, keep
+the lines width to 80 characters or less. You can attempt to build the docs
+yourself to see if the formating is right:
+
+```console
+python3 -m pip install -r docs/requirements.txt
+sphinx-build -b html docs/ docs/_build/
+```
+
+This will produce the html version of the documentation which you can navigate
+by opening the local file `docs/_build/index.html`.
+
+If you have any trouble building the docs, don't hesitate to open an issue to
+request help.
+
+Regarding the restructured text format",391,"[-0.003202371532097459, 0.011510860174894333, -0.00019526136748027056, -0.045907456427812576, 0.008913607336580753, 0.021634705364704132, -0.006207569502294064, -0.006805889308452606, -0.028800945729017258, -0.016888940706849098, 0.03236366808414459, 0.010171439498662949, -0.006544124335050583, 0.009409940801560879, -0.02617649734020233, -0.011572051793336868, 0.02530621364712715, -0.008934004232287407, 0.01517556980252266, -0.011014526709914207, -0.004093052353709936, -0.0018952464452013373, -0.012727896682918072, -0.010524991899728775, -0.020560448989272118, 0.010953334160149097, 0.01703852042555809, -0.014957998879253864, -0.007268227636814117, 0.0031207825522869825, 0.012816284783184528, -0.01592347025871277, 0.001907144789583981, -0.013863344676792622, -0.00499733118340373, -0.00035801460035145283, 0.03190132975578308, 0.004031860735267401, -0.0047899591736495495, 0.001268880208954215, 0.017895204946398735, 0.012333549559116364, 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+Contributing,https://docs.mila.quebec/Purpose.html#contributing,", you can simply provide the content
+you would like to add in markdown or plain text format if more convenient
+for you and someone down the line should take responsibility to convert
+the format.
+
+## Sphinx / reStructuredText (reST)
+
+The markup language used for the Mila Docs is
+[reStructuredText](http://docutils.sourceforge.net/rst.html) and we follow the
+[Python’s Style Guide for documenting](https://docs.python.org/devguide/documenting.html#style-guide).
+
+Here are some of reST syntax directives which are useful to know :
+(more can be found in
+[Sphinx's reST Primer](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html)):
+
+
+### Inline markup
+
+* one asterisk: `*text*` for *emphasis* (italics),
+* two asterisks: `**text**` for **strong emphasis** (boldface), and
+* backquotes: ` ``text`` ` for `code samples`, and
+* external links: `` `Link text <http://target>`_ ``.
+
+### Lists
+
+```reST
+* this is
+* a list
+
+  * with a nested list
+  * and some subitems
+
+* and here the parent list continues
+```
+
+### Sections
+
+```reST
+#################
+This is a heading
+#################
+```
+
+There are no heading levels assigned to certain characters as the structure is
+determined from the succession of headings. However, the Python documentation
+suggests the following convention:
+
+    * `#` with overline, for parts
+    * `*` with overline, for chapters
+    * `=`, for sections
+    * `-`, for subsections
+    * `^`, for subsubsections
+    * `""`, for paragraphs
+
+### Note box
+
+```reST
+.. note:: This is a long
+   long long note
+```
+
+### Collapsible boxes
+
+This is a local extension, not part of Sphinx itself.  It works like this:
+
+```reST
+.. container:: toggle
+
+    .. container:: header
+
+        **Show/Hide Code**
+
+    .. code-block:: <type>
+       ...
+```
+
+
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+Visual Studio Code,https://docs.mila.quebec/VSCode.html#visual-studio-code,"Visual Studio Code
+One editor of choice for many researchers is VSCode. One feature of VSCode is
+remote editing through SSH. This allows you to edit files on the cluster as if
+they were local. You can also debug your programs using VSCode’s debugger, open
+terminal sessions, etc.
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+Connecting to the cluster,https://docs.mila.quebec/VSCode.html#connecting-to-the-cluster,"Connecting to the cluster
 VSCode cannot be used to edit code on the login nodes, because it is a heavy
 enough process (a node process, plus the language server, linter, and
 possibly other plugins depending on your configured environment) that there is a
@@ -159,8 +462,9 @@ time.
 Therefore, to use VSCode on the cluster, you first need to allocate a compute
 node, then connect to that node.
 The milatools package provides a command to make the operation easier. More
-info can be found here.",125,"[0.0038284112233668566, 0.006614935118705034, 0.0012140782782807946, -0.04394106939435005, -0.04108811542391777, 0.03180203586816788, -0.037396058440208435, -0.0008408519206568599, 0.015956956893205643, -0.03952179104089737, 0.020683908835053444, 0.002263832138851285, -0.010293006896972656, -0.00760787446051836, -0.029676305130124092, -0.013978070579469204, 0.015803121030330658, -0.017649149522185326, 0.02948051318526268, -0.014726271852850914, -0.028893141075968742, 0.01167752780020237, 0.013313780538737774, -0.019257431849837303, -0.01184534840285778, -0.0022078920155763626, 0.01383822038769722, -0.01481018215417862, 0.001377528766170144, 0.005303835496306419, 0.015705225989222527, -0.03306068852543831, -0.001258655684068799, -0.024180175736546516, -0.010516767390072346, 0.008335097692906857, 0.022390086203813553, -0.023005429655313492, -0.017005836591124535, -0.02689327672123909, 0.024152204394340515, -0.013229870237410069, -0.01024405937641859, 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+Activating an environment,https://docs.mila.quebec/VSCode.html#activating-an-environment,"Activating an environment
 Reference
 To activate a conda or pip environment, you can open the command palette with
 Ctrl+Shift+P and type “Python: Select interpreter”. This will prompt you for the
@@ -170,10 +474,12 @@ Tip
 If you already have the environment activated in a terminal session, you can
 run the command which python to get the path for this environment. This
 path can be pasted into the interpreter selection prompt in VSCode to use
-that same environment.",107,"[0.014973689801990986, -0.02109268121421337, 0.023762306198477745, -0.027330618351697922, -0.022638950496912003, 0.012006704695522785, -0.015951670706272125, -0.02693413943052292, 0.0054317619651556015, 0.0002589503419585526, 0.03782409429550171, 0.012119040824472904, -0.015766646713018417, -0.003176787868142128, -0.02131735347211361, -0.015925239771604538, 0.009627831168472767, -0.0001478536141803488, 0.005573833826929331, -0.022295335307717323, -0.037189729511737823, -0.003865669947117567, 0.0298945140093565, -0.01777547411620617, -0.037189729511737823, 0.00032668214407749474, -0.0006149554392322898, -0.02055082656443119, 0.010050741955637932, -0.027198458090424538, 0.006925166118890047, -0.03134826943278313, -0.012839310802519321, -0.02894296497106552, -0.0005910014733672142, 0.004592548124492168, 0.005124490708112717, -0.04197390750050545, 0.022480357438325882, 0.0194274690002203, 0.009356903843581676, 0.0010110214352607727, -0.0008280628826469183, 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-16,"Troubleshooting
+that same environment.
 
-“Cannot reconnect”
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+“Cannot reconnect”,https://docs.mila.quebec/VSCode.html#cannot-reconnect,"“Cannot reconnect”
 When connecting to multiple compute nodes (and/or from multiple computers), some
 instances may crash with that message because of conflicts in the lock files
 VSCode installs in ~/.vscode-server (which is shared on all compute nodes).
@@ -183,47 +489,173 @@ To fix this issue, you can change this setting in your settings.json file:
 
 This will store the necessary lockfiles in /tmp on the compute nodes (which
 are local to the node).
-
-
-Debugger timeouts
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+Debugger timeouts,https://docs.mila.quebec/VSCode.html#debugger-timeouts,"Debugger timeouts
 Sometimes, slowness on the compute node or the networked filesystem might cause
 the VSCode debugger to timeout when starting a remote debug process. As a quick
 fix, you can add this to your ~/.bashrc or ~/.profile or equivalent
 resource file for your preferred shell, to increase the timeout delay to 500
 seconds:
-export DEBUGPY_PROCESS_SPAWN_TIMEOUT=500",194,"[-0.01063205860555172, -0.005515724886208773, 0.022613782435655594, -0.04313420504331589, -0.03897503390908241, 0.009227304719388485, -0.04043487459421158, -0.009330595843493938, -0.01398556400090456, -0.012105672620236874, 0.02213175967335701, 0.0013488046824932098, 0.010032972320914268, -0.0020296627189964056, -0.018440837040543556, -0.0020968017634004354, 0.020961130037903786, -0.029582463204860687, 0.03222670778632164, -0.03644096851348877, -0.017063627019524574, -0.018509697169065475, -0.011754484847187996, -0.022613782435655594, -0.007085743825882673, -0.0002541382273193449, 0.013221212662756443, -0.027227435261011124, 0.019528832286596298, -0.011747598648071289, 0.006624378263950348, -0.014915180392563343, -0.020699461922049522, -0.006255974993109703, -0.0016681451816111803, 0.007092629559338093, 0.018950404599308968, -0.031593188643455505, -0.007292325142771006, -0.01214698888361454, 0.01596185937523842, -0.02126411721110344, 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-17,"“Cannot reconnect”
-When connecting to multiple compute nodes (and/or from multiple computers), some
-instances may crash with that message because of conflicts in the lock files
-VSCode installs in ~/.vscode-server (which is shared on all compute nodes).
-To fix this issue, you can change this setting in your settings.json file:
-{ ""remote.SSH.lockfilesInTmp"": true }
+export DEBUGPY_PROCESS_SPAWN_TIMEOUT=500
+
+
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+Computational resources outside of Mila,https://docs.mila.quebec/Extra_compute.html#computational-resources-outside-of-mila,"Computational resources outside of Mila
+This section seeks to provide insights and information on computational
+resources outside the Mila cluster itself.
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+Digital Research Alliance of Canada Clusters,https://docs.mila.quebec/Extra_compute.html#digital-research-alliance-of-canada-clusters,"Digital Research Alliance of Canada Clusters
+The clusters named Beluga, Cedar, Graham, Narval and Niagara are
+clusters provided by the Digital Research Alliance of Canada organisation (the Alliance). For Mila researchers, these
+clusters are to be used for larger experiments having many jobs, multi-node
+computation and/or multi-GPU jobs as well as long running jobs. If you use
+these resources for your research, please remember to acknowledge their use in
+your papers.
 
+Note
+Compute Canada ceased its operational responsibilities for supporting Canada’s
+national advanced research computing (ARC) platform on March 31, 2022. The services
+will be supported by the new Digital Research Alliance of Canada.
+https://ace-net.ca/compute-canada-operations-move-to-the-digital-research-alliance-of-canada-(the-alliance).html
 
-This will store the necessary lockfiles in /tmp on the compute nodes (which
-are local to the node).",104,"[-0.013952026143670082, -0.00891229510307312, 0.03600000590085983, -0.03805097937583923, -0.0297391340136528, 0.013027737848460674, -0.054431792348623276, -0.0020847078412771225, 0.0006674944888800383, -0.011010495945811272, 0.02795802429318428, -0.0016225639265030622, 0.014316343702375889, -0.011563719250261784, -0.022250378504395485, -0.012245128862559795, 0.01976761966943741, -0.019673166796565056, 0.01964617893099785, -0.04037182033061981, -0.01847226545214653, -0.009080960415303707, -0.0049992511048913, -0.02436881884932518, -0.005697526969015598, -0.007988006807863712, 0.01756821759045124, -0.021724140271544456, 0.016124440357089043, -0.01679910346865654, 0.005559220910072327, -0.010167167522013187, -0.01963268592953682, -0.01372938696295023, -0.01121964119374752, -0.010403299704194069, 0.02117091789841652, -0.03732234239578247, -0.02156222239136696, -0.013607948087155819, 0.0014699214370921254, -0.02681109867990017, -0.00891229510307312, 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-18,"Debugger timeouts
-Sometimes, slowness on the compute node or the networked filesystem might cause
-the VSCode debugger to timeout when starting a remote debug process. As a quick
-fix, you can add this to your ~/.bashrc or ~/.profile or equivalent
-resource file for your preferred shell, to increase the timeout delay to 500
-seconds:
-export DEBUGPY_PROCESS_SPAWN_TIMEOUT=500",84,"[-0.011595424264669418, -0.011575057171285152, 0.011208457872271538, -0.03345565125346184, -0.04515969753265381, 0.006181284319609404, -0.017243780195713043, -0.01921255886554718, -0.011486802250146866, -0.023625336587429047, 0.025594115257263184, 0.011778724379837513, 0.006398528814315796, -0.010142602026462555, -0.017528913915157318, -0.0017498363740742207, 0.017488181591033936, -0.020774003118276596, 0.0385608971118927, -0.030767248943448067, -0.0036999450530856848, -0.023380937054753304, -0.018723757937550545, -0.010936901904642582, -0.013129713013768196, 0.004188745282590389, 0.004721673205494881, -0.028187470510601997, 0.014487490989267826, -0.013523468747735023, 0.004850661847740412, -0.0312560498714447, -0.003253575647249818, -0.0043448894284665585, -0.001015787711367011, 0.01602178066968918, -0.0033163728658109903, -0.024059826508164406, 0.009409401565790176, -0.012369357980787754, 0.019158247858285904, 0.0042328727431595325, 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+Current allocation description,https://docs.mila.quebec/Extra_compute.html#current-allocation-description,"Current allocation description
+Clusters of the Alliance are shared with researchers across the country.
+Allocations are given by the Alliance to selected research groups to ensure to
+a minimal amount of computational resources throughout the year.
+Depending on your affiliation, you will have access to different allocations. If
+you are a student at University of Montreal, you can have access to the
+rrg-bengioy-ad allocation described below. For students from other
+universities, you should ask your advisor to know which allocations you could
+have access to.
+From the Alliance’s documentation: An allocation is an amount of resources
+that a research group can target for use for a period of time, usually a year.
+To be clear, it is not a maximal amount of resources that can be used
+simultaneously, it is a weighting factor of the workload manager to balance
+jobs. For instance, even though we are allocated 400 GPU-years across all
+clusters, we can use more or less than 400 GPUs simultaneously depending on the
+history of usage from our group and other groups using the cluster at a given
+period of time. Please see the Alliance’s documentation for
+more information on how allocations and resource scheduling are configured for
+these installations.
+The table below provides information on the allocation for
+rrg-bengioy-ad for the period which spans from April 2022 to
+April 2023. Note that there are no special allocations for GPUs on
+Graham and therefore jobs with GPUs should be submitted with the
+account def-bengioy.
+
+
+
+
+
+
+
+
+
+
+
+Cluster
+CPUs
+GPUs
+
+#
+account
+Model
+#
+SLURM type specifier
+account
+
+Beluga
+238
+rrg-bengioy-ad
+V100-16G
+77
+v100
+rrg-bengioy-ad
+
+Cedar
+34
+rrg-bengioy-ad
+V100-32G
+138
+v100l
+rrg-bengioy-ad
+
+Graham
+34
+rrg-bengioy-ad
+various
+–
+–
+def-bengioy
+
+Narval
+34
+rrg-bengioy-ad
+A100-40G
+185
+a100
+rrg-bengioy-ad
+
+
+
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+Account Creation,https://docs.mila.quebec/Extra_compute.html#account-creation,"Account Creation
+To access the Alliance clusters you have to first create an account at
+https://ccdb.computecanada.ca. Use a password with at least 8 characters, mixed
+case letters, digits and special characters. Later you will be asked to create
+another password with those rules, and it’s really convenient that the two
+password are the same.
+Then, you have to apply for a role at
+https://ccdb.computecanada.ca/me/add_role, which basically means telling the
+Alliance that you are part of the lab so they know which cluster you can have
+access to, and track your usage.
+You will be asked for the CCRI (See screenshot below). Please reach out to your
+sponsor to get the CCRI.
+
+You will need to wait for your sponsor to accept before being able to login
+to the Alliance clusters.
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+Clusters,https://docs.mila.quebec/Extra_compute.html#clusters,"Clusters
+
+Beluga:(Mila doc)
+(Digital Research Alliance of Canada doc)
+For most students, Beluga is the best choice for both CPU and GPU jobs because
+of larger allocations on this cluster.
+
+Narval:(Mila doc)
+(Digital Research Alliance of Canada doc)
+Narval is the newest cluster, and contains the most powerful GPUs (A100). If your
+job can benefit from the A100’s features, such as TF32 floating-point math, Narval
+is the best choice.
+
+Cedar:(Mila doc)
+(Digital Research Alliance of Canada doc)
+Cedar is a good alternative to Beluga if you absolutely need to have an internet connection
+on the compute nodes.
+
+Graham:(Mila doc)
+(Digital Research Alliance of Canada doc)
+We do not have a GPU allocation on Graham anymore but it remains an alternative for CPU jobs.
+
+Niagara:(Mila doc)
+(Digital Research Alliance of Canada doc)
+Niagara is not recommended for most students. It is a CPU-only cluster with unusual
+configurations. Access is not automatic; It is opt-in and must be requested via
 CCDB manually. Compute resources in Niagara are not assigned to jobs on a per-CPU,
 but on a per-node basis.
 
 
-
-Beluga
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+Beluga,https://docs.mila.quebec/Extra_compute.html#beluga,"Beluga
 Beluga is a cluster located at ÉTS in Montreal. It
 uses SLURM to schedule jobs. Its full documentation can be found here, and its current status
 here.
 You can access Beluga via ssh:
 ssh <user>@beluga.computecanada.ca
 Where <user> is the username you created previously (see Account Creation).
-
-Launching Jobs
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+Launching Jobs,https://docs.mila.quebec/Extra_compute.html#launching-jobs,"Launching Jobs
 Users must specify the resource allocation Group Name using the flag
 --account=rrg-bengioy-ad.  To launch a CPU-only job:
 sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
@@ -236,9 +668,8 @@ sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
 And to get an interactive session, use the salloc command:
 salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
 The full documentation for jobs launching on Beluga can be found here.
-
-
-Beluga nodes description
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+Beluga nodes description,https://docs.mila.quebec/Extra_compute.html#beluga-nodes-description,"Beluga nodes description
 Each GPU node consists of:
 
 40 CPU cores
@@ -252,9 +683,8 @@ requesting (as explained here),
 otherwise, your job will count for more than 1 allocation, and will take
 more time to get scheduled.
 
-
-
-Beluga Storage
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+Beluga Storage,https://docs.mila.quebec/Extra_compute.html#beluga-storage,"Beluga Storage
 
 
 
@@ -323,9 +753,8 @@ job.
 When an experiment is finished, results should be transferred back to Mila
 servers.
 More details on storage can be found here.
-
-
-Modules
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+Modules,https://docs.mila.quebec/Extra_compute.html#modules,"Modules
 Many software, such as Python or MATLAB are already compiled and available on
 Beluga through the module command and its subcommands. Its full
 documentation can be found here.
@@ -360,9 +789,8 @@ avail_wheels <wheel>
 Such wheels can be installed using pip. Moreover, the most efficient way to use
 modules on the cluster is to build your environnement inside your job.
 See the script example below.
-
-
-Script Example
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+Script Example,https://docs.mila.quebec/Extra_compute.html#script-example,"Script Example
 Here is a sbatch script that follows good practices on Beluga:
  1#!/bin/bash
  2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
@@ -393,19 +821,16 @@ Here is a sbatch script that follows good practices on Beluga:
 27cp $SLURM_TMPDIR/<to_save> $SCRATCH
 
 
-
-
-Using CometML and Wandb
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+Using CometML and Wandb,https://docs.mila.quebec/Extra_compute.html#using-cometml-and-wandb,"Using CometML and Wandb
 The compute nodes for Beluga don’t have access to the internet,
 but there is a special module that can be loaded in order to allow
 training scripts to access some specific servers, which includes
 the necessary servers for using CometML and Wandb (“Weights and Biases”).
 module load httpproxy
 More documentation about this can be found here.
-
-
-
-Graham
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+Graham,https://docs.mila.quebec/Extra_compute.html#graham,"Graham
 Graham is a cluster located at University of Waterloo. It uses SLURM to schedule
 jobs. Its full documentation can be found here, and its current status here.
 You can access Graham via ssh:
@@ -418,9 +843,8 @@ Canada Documentation.
 Note
 For GPU jobs the ressource allocation Group Name is the same as Beluga, so you should use the flag --account=rrg-bengioy-ad for GPU jobs.
 
-
-
-Cedar
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+Cedar,https://docs.mila.quebec/Extra_compute.html#cedar,"Cedar
 Cedar is a cluster located at Simon Fraser University. It uses SLURM to schedule
 jobs. Its full documentation can be found here, and its current status here.
 You can access Cedar via ssh:
@@ -435,9 +859,8 @@ However, we don’t have any CPU priority on Cedar, in this case you can
 use --account=def-bengioy for CPU. Thus, it might take some time before
 they start.
 
-
-
-Niagara
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+Niagara,https://docs.mila.quebec/Extra_compute.html#niagara,"Niagara
 Niagara is a cluster located at University of Toronto. It uses SLURM to schedule
 jobs. Its full documentation can be found here, and its current status here.
 You can access Niagara via ssh:
@@ -446,43 +869,69 @@ Where <user> is the username you created previously (see Account Creation).
 Since its structure is similar to Beluga, please look at the Beluga
 documentation, as well as relevant parts of the Digital Research Alliance of
 Canada Documentation.
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+What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?,https://docs.mila.quebec/Extra_compute.html#what-to-do-with-importerror-lib64-libm-so-6-version-glibc-2-23-not-found,"What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
+The structure of the file system is different than a classical Linux, so your
+code has trouble finding libraries. See how to install binary packages.
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+Disk quota exceeded error on /project file systems,https://docs.mila.quebec/Extra_compute.html#disk-quota-exceeded-error-on-project-file-systems,"Disk quota exceeded error on /project file systems
+You have files in /project with the wrong permissions. See how to change
+permissions.
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+Computing infrastructure and policies,https://docs.mila.quebec/Information.html#computing-infrastructure-and-policies,"Computing infrastructure and policies
+This section seeks to provide factual information and policies on the Mila cluster computing environments.
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+Roles and authorizations,https://docs.mila.quebec/Information.html#roles-and-authorizations,"Roles and authorizations
+There are mainly two types of researchers statuses at Mila :
+
+Core researchers
+Affiliated researchers
 
+This is determined by Mila policy. Core researchers have access to the Mila
+computing cluster. See your supervisor’s Mila status to know what is your own
+status.
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+Overview of available computing resources at Mila,https://docs.mila.quebec/Information.html#overview-of-available-computing-resources-at-mila,"Overview of available computing resources at Mila
+The Mila cluster is to be used for regular development and relatively small
+number of jobs (< 5). It is a heterogeneous cluster. It uses
+SLURM to schedule jobs.
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+Mila cluster versus Digital Research Alliance of Canada clusters,https://docs.mila.quebec/Information.html#mila-cluster-versus-digital-research-alliance-of-canada-clusters,"Mila cluster versus Digital Research Alliance of Canada clusters
+There are a lot of commonalities between the Mila cluster and the clusters from
+Digital Research Alliance of Canada (the Alliance). At the time being, the
+Alliance clusters where we have a large allocation of resources are beluga,
+cedar, graham and narval. We also have comparable computational resources
+in the Mila cluster, with more to come.
+The main distinguishing factor is that we have more control over our own
+cluster than we have over the ones at the Alliance. Notably, also, the compute
+nodes in the Mila cluster all have unrestricted access to the Internet, which
+is not the case in general for the Alliance clusters (although cedar does
+allow it).
+At the current time of this writing (June 2021), Mila students are advised to
+use a healthy diet of a mix of Mila and Alliance clusters. This is especially
+true in times when your favorite cluster is oversubscribed, because you can
+easily switch over to a different one if you are used to it.
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+Guarantees about one GPU as absolute minimum,https://docs.mila.quebec/Information.html#guarantees-about-one-gpu-as-absolute-minimum,"Guarantees about one GPU as absolute minimum
+There are certain guarantees that the Mila cluster tries to honor when it comes
+to giving at minimum one GPU per student, all the time, to be used in
+interactive mode. This is strictly better than “one GPU per student on average”
+because it’s a floor meaning that, at any time, you should be able to ask for
+your GPU, right now, and get it (although it might take a minute for the
+request to be processed by SLURM).
+Interactive sessions are possible on the Alliance clusters, and there are
+generally special rules that allow you to get resources more easily if you
+request them for a very short duration (for testing code before queueing long
+jobs). You do not get the same guarantee as on the Mila cluster, however.
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+Node profile description,https://docs.mila.quebec/Information.html#node-profile-description,"Node profile description
 
 
-FAQ
 
-What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
-The structure of the file system is different than a classical Linux, so your
-code has trouble finding libraries. See how to install binary packages.
 
 
-Disk quota exceeded error on /project file systems
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-permissions.",1873,"[-0.007660632021725178, -0.026531251147389412, -0.010391999036073685, -0.04660395532846451, -0.0066790468990802765, 0.009837190620601177, -0.03675965219736099, 0.0030959052965044975, -0.01796727627515793, -0.034369707107543945, 0.017455145716667175, 0.001941831549629569, -0.009837190620601177, -0.0010776098351925611, -0.005516082048416138, -0.0018458068370819092, 0.009758948348462582, -0.016843432560563087, 0.005889511201530695, -0.0006388305337168276, -0.011437601409852505, -0.0040010265074670315, 0.022932104766368866, -0.019802413880825043, -0.017412466928362846, 0.0021445502061396837, 0.02190784364938736, -0.004353116732090712, 0.0025322053115814924, 0.013073576614260674, 0.0022565789986401796, -0.005665453616529703, 9.652476728660986e-05, -0.010790323838591576, -0.03266260027885437, -0.011715005151927471, 0.01443925965577364, -0.004100607708096504, 0.014425033703446388, 0.012355170212686062, 0.02384256199002266, 0.012191572226583958, -0.0074401311576366425, 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-20,"Current allocation description
-Clusters of the Alliance are shared with researchers across the country.
-Allocations are given by the Alliance to selected research groups to ensure to
-a minimal amount of computational resources throughout the year.
-Depending on your affiliation, you will have access to different allocations. If
-you are a student at University of Montreal, you can have access to the
-rrg-bengioy-ad allocation described below. For students from other
-universities, you should ask your advisor to know which allocations you could
-have access to.
-From the Alliance’s documentation: An allocation is an amount of resources
-that a research group can target for use for a period of time, usually a year.
-To be clear, it is not a maximal amount of resources that can be used
-simultaneously, it is a weighting factor of the workload manager to balance
-jobs. For instance, even though we are allocated 400 GPU-years across all
-clusters, we can use more or less than 400 GPUs simultaneously depending on the
-history of usage from our group and other groups using the cluster at a given
-period of time. Please see the Alliance’s documentation for
-more information on how allocations and resource scheduling are configured for
-these installations.
-The table below provides information on the allocation for
-rrg-bengioy-ad for the period which spans from April 2022 to
-April 2023. Note that there are no special allocations for GPUs on
-Graham and therefore jobs with GPUs should be submitted with the
-account def-bengioy.
 
 
 
@@ -494,340 +943,382 @@ account def-bengioy.
 
 
 
-Cluster
+Name
+GPU
 CPUs
-GPUs
+Sockets
+Cores/Socket
+Threads/Core
+Memory (GB)
+TmpDisk (TB)
+Arch
+Slurm Features
 
-#
-account
 Model
+Mem
 #
-SLURM type specifier
-account
+GPU Arch and Memory
 
-Beluga
-238
-rrg-bengioy-ad
-V100-16G
-77
-v100
-rrg-bengioy-ad
 
-Cedar
-34
-rrg-bengioy-ad
-V100-32G
-138
-v100l
-rrg-bengioy-ad
 
-Graham
-34
-rrg-bengioy-ad
-various
-–
-–
-def-bengioy
+GPU Compute Nodes
 
-Narval
-34
-rrg-bengioy-ad
-A100-40G
-185
-a100
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-21,"Account Creation
-To access the Alliance clusters you have to first create an account at
-https://ccdb.computecanada.ca. Use a password with at least 8 characters, mixed
-case letters, digits and special characters. Later you will be asked to create
-another password with those rules, and it’s really convenient that the two
-password are the same.
-Then, you have to apply for a role at
-https://ccdb.computecanada.ca/me/add_role, which basically means telling the
-Alliance that you are part of the lab so they know which cluster you can have
-access to, and track your usage.
-You will be asked for the CCRI (See screenshot below). Please reach out to your
-sponsor to get the CCRI.
+cn-a[001-011]
+RTX8000
+48
+8
+40
+2
+20
+1
+384
+3.6
+x86_64
+turing,48gb
 
-You will need to wait for your sponsor to accept before being able to login
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-22,"It is opt-in and must be requested via
-CCDB manually. Compute resources in Niagara are not assigned to jobs on a per-CPU,
-but on a per-node basis.
+cn-b[001-005]
+V100
+32
+8
+40
+2
+20
+1
+384
+3.6
+x86_64
+volta,nvlink,32gb
 
+cn-c[001-040]
+RTX8000
+48
+8
+64
+2
+32
+1
+384
+3
+x86_64
+turing,48gb
 
+cn-g[001-026]
+A100
+80
+4
+64
+2
+32
+1
+1024
+7
+x86_64
+ampere,nvlink,80gb
 
-Beluga
-Beluga is a cluster located at ÉTS in Montreal. It
-uses SLURM to schedule jobs. Its full documentation can be found here, and its current status
-here.
-You can access Beluga via ssh:
-ssh <user>@beluga.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
+DGX Systems
 
-Launching Jobs
-Users must specify the resource allocation Group Name using the flag
---account=rrg-bengioy-ad.  To launch a CPU-only job:
-sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
+cn-d[001-002]
+A100
+40
+8
+128
+2
+64
+1
+1024
+14
+x86_64
+ampere,nvlink,40gb
 
-Note
-The account name will differ based on your affiliation.
+cn-d[003-004]
+A100
+80
+8
+128
+2
+64
+1
+2048
+28
+x86_64
+ampere,nvlink,80gb
 
-To launch a GPU job:
-sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
-And to get an interactive session, use the salloc command:
-salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
-The full documentation for jobs launching on Beluga can be found here.
+cn-e[002-003]
+V100
+32
+8
+40
+2
+20
+1
+512
+7
+x86_64
+volta,32gb
 
+CPU Compute Nodes
 
-Beluga nodes description
-Each GPU node consists of:
+cn-f[001-004]
 
-40 CPU cores
-186 GB RAM
-4 GPU NVIDIA V100 (16GB)
 
 
-Tip
-You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
-requesting (as explained here),
-otherwise, your job will count for more than 1 allocation, and will take
-more time to get scheduled.
 
 
 
-Beluga Storage
 
 
 
 
 
 
+32
+1
+32
+1
+256
+10
+x86_64
+rome
 
-Storage
-Path
-Usage
+cn-h[001-004]
 
 
 
-$HOME
-/home/<user>/
 
-Code
-Specific libraries
 
 
 
-$HOME/projects
-/project/rpp-bengioy
 
-Compressed raw datasets
 
 
 
-$SCRATCH
-/scratch/<user>
 
-Processed datasets
-Experimental results
-Logs of experiments
+64
+2
+32
+1
+768
+7
+x86_64
+milan
 
+Legacy GPU Compute Nodes
 
+kepler5
+V100
+16
+2
+16
+2
+4
+2
+256
+3.6
+x86_64
+volta,16gb
 
-$SLURM_TMPDIR
+TITAN RTX
 
+rtx[1,3-5,7]
+titanrtx
+24
+2
+20
+1
+10
+2
+128
+0.93
+x86_64
+turing,24gb
 
-Temporary job results
 
 
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+Special nodes and outliers,https://docs.mila.quebec/Information.html#special-nodes-and-outliers,"Special nodes and outliers
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+DGX A100,https://docs.mila.quebec/Information.html#dgx-a100,"DGX A100
+DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
+GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
+interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
+In order to run jobs on a DGX A100, add the flags below to your Slurm
+commands:
+--gres=gpu:a100:<number> --reservation=DGXA100
 
 
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+MIG,https://docs.mila.quebec/Information.html#mig,"MIG
+MIG (Multi-Instance GPU)
+is an NVIDIA technology allowing certain GPUs to be
+partitioned into multiple instances, each of which has a roughly proportional
+amount of compute resources, device memory and bandwidth to that memory.
+NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
+instances. Although this can theoretically be done dynamically, the SLURM job
+scheduler does not support doing so in practice as it does not model
+reconfigurable resources very well. Therefore, the A100s must currently be
+statically partitioned into the required number of instances of every size
+expected to be used.
+The cn-g series of nodes include A100-80GB GPUs. One third have been
+configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
+have been configured in MIG mode, and offer the following profiles:
 
-They are roughly listed in order of increasing performance and optimized for
-different uses:
 
-The $HOME folder on NFS is appropriate for codes and libraries which are
-small and read once. Do not write experiemental results here!
-The $HOME/projects folder should only contain compressed raw datasets
-(processed datasets should go in $SCRATCH). We have a limit on the
-size and number of file in $HOME/projects, so do not put anything else
-there.  If you add a new dataset there (make sure it is readable by every
-member of the group using chgrp -R rpp-bengioy <dataset>).
-The $SCRATCH space can be used for short term storage. It has good
-performance and large quotas, but is purged regularly (every file that has
-not been used in the last 3 months gets deleted, but you receive an email
-before this happens).
-$SLURM_TMPDIR points to the local disk of the node on which a job is
-running. It should be used to copy the data on the node at the beginning of
-the job and write intermediate checkpoints. This folder is cleared after each
-job.
 
-When an experiment is finished, results should be transferred back to Mila
-servers.
-More details on storage can be found here.
 
 
-Modules
-Many software, such as Python or MATLAB are already compiled and available on
-Beluga through the module command and its subcommands. Its full
-documentation can be found here.
 
 
 
 
+Name
+GPU
+Cluster-wide
 
+Model
+Memory
+Compute
+#
 
-module avail
-Displays all the available modules
 
-module load <module>
-Loads <module>
-
-module spider <module>
-Shows specific details about <module>
 
+a100l.1g.10gb
+a100l.1
+A100
+10GB 
+(1/8th)
+1/7th
+of full
+72
 
+a100l.2g.20gb
+a100l.2
+A100
+20GB 
+(2/8th)
+2/7th
+of full
+108
 
-In particular, if you with to use Python 3.6 you can simply do:
-module load python/3.6
+a100l.3g.40gb
+a100l.3
+A100
+40GB 
+(4/8th)
+3/7th
+of full
+72
 
-Tip
-If you wish to use Python on the cluster, we strongly encourage you to
-read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.
 
-The cluster has many Python packages (or wheels), such already compiled for
-the cluster. See here for the
-details. In particular, you can browse the packages by doing:
-avail_wheels <wheel>
-Such wheels can be installed using pip. Moreover, the most efficient way to use
-modules on the cluster is to build your environnement inside your job.
-See the script example below.
 
+And can be requested using a SLURM flag such as --gres=gpu:a100l.1
+The partitioning may be revised as needs and SLURM capabilities evolve. Other
+MIG profiles exist and could be introduced.
 
-Script Example
-Here is a sbatch script that follows good practices on Beluga:
- 1#!/bin/bash
- 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
- 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
- 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
- 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
- 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
- 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
- 8
- 9# 1. Create your environement locally
-10module load python/3.6
-11virtualenv --no-download $SLURM_TMPDIR/env
-12source $SLURM_TMPDIR/env/bin/activate
-13pip install --no-index torch torchvision
-14
-15# 2. Copy your dataset on the compute node
-16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
-17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
-18
-19# 3. Eventually unzip your dataset
-20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
-21
-22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
-23#    and look for the dataset into $SLURM_TMPDIR
-24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
-25
-26# 5. Copy whatever you want to save on $SCRATCH
-27cp $SLURM_TMPDIR/<to_save> $SCRATCH
+Warning
+MIG has a number of important limitations,
+most notably that a GPU in MIG mode does not support graphics APIs
+(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
+limit every MIG job to exactly one MIG slice and no more. Thus,
+--gres=gpu:a100l.3 will work (and request a size-3 slice of an
+a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
+three size-1 slices) will not.
 
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+AMD,https://docs.mila.quebec/Information.html#amd,"AMD
 
+Warning
+As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
+more samples. You can join the amd slack channels to get the latest
+information
 
+Mila has a few node equipped with MI50 GPUs.
+srun --gres=gpu -c 8 --reservation=AMD --pty bash
 
-Using CometML and Wandb
-The compute nodes for Beluga don’t have access to the internet,
-but there is a special module that can be loaded in order to allow
-training scripts to access some specific servers, which includes
-the necessary servers for using CometML and Wandb (“Weights and Biases”).
-module load httpproxy
-More documentation about this can be found here.
+ first time setup of AMD stack
+conda create -n rocm python=3.6
+conda activate rocm
 
+pip install tensorflow-rocm
+pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl
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+Data sharing policies,https://docs.mila.quebec/Information.html#data-sharing-policies,"Data sharing policies
 
+Note
+/network/scratch aims to support
+Access Control Lists (ACLs)
+to allow collaborative work on rapidly changing data, e.g. work in process
+datasets, model checkpoints, etc…
 
-Graham
-Graham is a cluster located at University of Waterloo. It uses SLURM to schedule
-jobs. Its full documentation can be found here, and its current status here.
-You can access Graham via ssh:
-ssh <user>@graham.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
-Since its structure is similar to Beluga, please look at the Beluga
-documentation, as well as relevant parts of the Digital Research Alliance of
-Canada Documentation.
+/network/projects aims to offer a collaborative
+space for long-term projects. Data that should be kept for a longer period then
+90 days can be stored in that location but first a request to Mila’s helpdesk has to be made to create the project
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+Monitoring,https://docs.mila.quebec/Information.html#monitoring,"Monitoring
+Every compute node on the Mila cluster has a Netdata
+monitoring daemon allowing you to get a sense of the state of the node.
+This information is exposed in two ways:
 
-Note
-For GPU jobs the ressource allocation Group Name is the same as Beluga, so you should use the flag --account=rrg-bengioy-ad for GPU jobs.
+For every node, there is a web interface from Netdata itself at <node>.server.mila.quebec:19999.
+This is accessible only when using the Mila wifi or through SSH tunnelling.
 
+SSH tunnelling: on your local machine, run
 
+ssh -L 19999:<node>.server.mila.quebec:19999 -p 2222
+login.server.mila.quebec
+or ssh -L 19999:<node>.server.mila.quebec:19999 mila if you have
+already setup your SSH Login,
 
-Cedar
-Cedar is a cluster located at Simon Fraser University. It uses SLURM to schedule
-jobs. Its full documentation can be found here, and its current status here.
-You can access Cedar via ssh:
-ssh <user>@cedar.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
-Since its structure is similar to Beluga, please look at the Beluga
-documentation, as well as relevant parts of the Digital Research Alliance of
-Canada Documentation.
 
-Note
-However, we don’t have any CPU priority on Cedar, in this case you can
-use --account=def-bengioy for CPU. Thus, it might take some time before
-they start.
+then open http://localhost:19999 in your browser.
 
 
+The Mila dashboard at dashboard.server.mila.quebec
+exposes aggregated statistics with the use of grafana.
+These are collected internally to an instance of prometheus.
 
-Niagara
-Niagara is a cluster located at University of Toronto. It uses SLURM to schedule
-jobs. Its full documentation can be found here, and its current status here.
-You can access Niagara via ssh:
-ssh <user>@niagara.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
-Since its structure is similar to Beluga, please look at the Beluga
-documentation, as well as relevant parts of the Digital Research Alliance of
-Canada Documentation.",1781,"[-0.009789498522877693, -0.029214439913630486, -0.008206932805478573, -0.040698543190956116, -0.0009470884106121957, 0.01232440397143364, -0.04072655364871025, 0.0030583429615944624, -0.018332550302147865, -0.033219870179891586, 0.019719045609235764, 0.005637014284729958, -0.008332978002727032, 0.000261280860286206, -0.004506110213696957, -0.004824724048376083, 0.008473027497529984, -0.020951487123966217, 0.003573026740923524, -0.002956806682050228, -0.01632983610033989, -0.0023440876975655556, 0.017604291439056396, -0.015475530177354813, -0.023584427312016487, 0.005630011670291424, 0.02267410233616829, -0.0027974997647106647, 0.0026154345832765102, 0.010328690521419048, 0.005482959095388651, -0.0011974278604611754, 0.0017322440398856997, -0.010881888680160046, -0.030166780576109886, -0.004240015055984259, 0.014110041782259941, -0.006956986151635647, 0.013402789831161499, 0.013206719420850277, 0.022940197959542274, 0.012338409200310707, 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-23,"Beluga
-Beluga is a cluster located at ÉTS in Montreal. It
-uses SLURM to schedule jobs. Its full documentation can be found here, and its current status
-here.
-You can access Beluga via ssh:
-ssh <user>@beluga.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
+In both cases, those graphs are not editable by individual users,
+but they provide valuable insight into the state of the whole cluster
+or the individual nodes.
+One of the important uses is to collect data about the health
+of the Mila cluster and to sound the alarm if outages occur
+(e.g. if the nodes crash or if GPUs mysteriously become unavailable for SLURM).
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+Example with Netdata on cn-c001,https://docs.mila.quebec/Information.html#example-with-netdata-on-cn-c001,"Example with Netdata on cn-c001
+For example, if we have a job running on cn-c001, we can type
+cn-c001.server.mila.quebec:19999 in a browser address bar and the following
+page will appear.
 
-Launching Jobs
-Users must specify the resource allocation Group Name using the flag
---account=rrg-bengioy-ad.  To launch a CPU-only job:
-sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
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+Example watching the CPU/RAM/GPU usage,https://docs.mila.quebec/Information.html#example-watching-the-cpu-ram-gpu-usage,"Example watching the CPU/RAM/GPU usage
+Given that compute nodes are generally shared
+with other users who are also running jobs at the same time and
+consuming resources, this is not generally a good way to profile your code
+in fine details.
+However, it can still be a very useful source of information
+for getting an idea of whether the machine that you requested is being
+used in its full capacity.
+Given how expensive the GPUs are, it generally makes sense to try to
+make sure that this resources is always kept busy.
 
-Note
-The account name will differ based on your affiliation.
 
-To launch a GPU job:
-sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
-And to get an interactive session, use the salloc command:
-salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
-The full documentation for jobs launching on Beluga can be found here.
+CPU
+iowait (pink line): High values means your model is waiting on IO a lot (disk or network).
 
 
-Beluga nodes description
-Each GPU node consists of:
 
-40 CPU cores
-186 GB RAM
-4 GPU NVIDIA V100 (16GB)
 
 
-Tip
-You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
-requesting (as explained here),
-otherwise, your job will count for more than 1 allocation, and will take
-more time to get scheduled.
 
 
 
-Beluga Storage
+CPU RAM
+You can see how much CPU RAM is being used by your script in practice,
+considering the amount that you requested (e.g. `sbatch --mem=8G ...`).
+GPU usage is generally more important to monitor than CPU RAM.
+You should not cut it so close to the limit that your experiments randomly fail
+because they run out of RAM. However, you should not request blindly 32GB of RAM
+when you actually require only 8GB.
 
 
 
@@ -835,565 +1326,496 @@ Beluga Storage
 
 
 
-Storage
-Path
-Usage
 
+GPU
+Monitors the GPU usage using an nvidia-smi plugin for Netdata.
+Under the plugin interface, select the GPU number which was allocated to
+you. You can figure this out by running echo $SLURM_JOB_GPUS on the
+allocated node or, if you have the job ID,
+scontrol show -d job YOUR_JOB_ID | grep 'GRES' and checking IDX
+You should make sure you use the GPUs to their fullest capacity.
+Select the biggest batch size if possible to increase GPU memory usage and
+the GPU computational load.
+Spawn multiple experiments if you can fit many on a single GPU.
+Running 10 independent MNIST experiments on a single GPU will probably take
+less than 10x the time to run a single one. This assumes that you have more
+experiments to run, because nothing is gained by gratuitously running experiments.
+You can request a less powerful GPU and leave the more powerful GPUs
+to other researchers who have experiments that can make best use of them.
+Sometimes you really just need a k80 and not a v100.
 
 
-$HOME
-/home/<user>/
 
-Code
-Specific libraries
 
 
 
-$HOME/projects
-/project/rpp-bengioy
 
-Compressed raw datasets
 
+Other users or jobs
+If the node seems unresponsive or slow,
+it may be useful to check what other tasks are
+running at the same time on that node.
+This should not be an issue in general,
+but in practice it is useful to be able to
+inspect this to diagnose certain problems.
 
 
-$SCRATCH
-/scratch/<user>
 
-Processed datasets
-Experimental results
-Logs of experiments
 
 
 
-$SLURM_TMPDIR
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+Example with Mila dashboard,https://docs.mila.quebec/Information.html#example-with-mila-dashboard,"Example with Mila dashboard
 
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+Storage,https://docs.mila.quebec/Information.html#storage,"Storage
 
-Temporary job results
 
 
 
 
 
-They are roughly listed in order of increasing performance and optimized for
-different uses:
 
-The $HOME folder on NFS is appropriate for codes and libraries which are
-small and read once. Do not write experiemental results here!
-The $HOME/projects folder should only contain compressed raw datasets
-(processed datasets should go in $SCRATCH). We have a limit on the
-size and number of file in $HOME/projects, so do not put anything else
-there.  If you add a new dataset there (make sure it is readable by every
-member of the group using chgrp -R rpp-bengioy <dataset>).
-The $SCRATCH space can be used for short term storage. It has good
-performance and large quotas, but is purged regularly (every file that has
-not been used in the last 3 months gets deleted, but you receive an email
-before this happens).
-$SLURM_TMPDIR points to the local disk of the node on which a job is
-running. It should be used to copy the data on the node at the beginning of
-the job and write intermediate checkpoints. This folder is cleared after each
-job.
 
-When an experiment is finished, results should be transferred back to Mila
-servers.
-More details on storage can be found here.
 
 
-Modules
-Many software, such as Python or MATLAB are already compiled and available on
-Beluga through the module command and its subcommands. Its full
-documentation can be found here.
+Path
+Performance
+Usage
+Quota (Space/Files)
+Backup
+Auto-cleanup
 
 
 
+/network/datasets/
+High
 
+Curated raw datasets (read only)
 
 
-module avail
-Displays all the available modules
 
-module load <module>
-Loads <module>
 
-module spider <module>
-Shows specific details about <module>
 
 
+$HOME or /home/mila/<u>/<username>/
+Low
 
-In particular, if you with to use Python 3.6 you can simply do:
-module load python/3.6
+Personal user space
+Specific libraries, code, binaries
 
-Tip
-If you wish to use Python on the cluster, we strongly encourage you to
-read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.
 
-The cluster has many Python packages (or wheels), such already compiled for
-the cluster. See here for the
-details. In particular, you can browse the packages by doing:
-avail_wheels <wheel>
-Such wheels can be installed using pip. Moreover, the most efficient way to use
-modules on the cluster is to build your environnement inside your job.
-See the script example below.
+100GB/1000K
+Daily
+no
 
+$SCRATCH or /network/scratch/<u>/<username>/
+High
 
-Script Example
-Here is a sbatch script that follows good practices on Beluga:
- 1#!/bin/bash
- 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
- 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
- 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
- 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
- 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
- 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
- 8
- 9# 1. Create your environement locally
-10module load python/3.6
-11virtualenv --no-download $SLURM_TMPDIR/env
-12source $SLURM_TMPDIR/env/bin/activate
-13pip install --no-index torch torchvision
-14
-15# 2. Copy your dataset on the compute node
-16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
-17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
-18
-19# 3. Eventually unzip your dataset
-20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
-21
-22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
-23#    and look for the dataset into $SLURM_TMPDIR
-24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
-25
-26# 5. Copy whatever you want to save on $SCRATCH
-27cp $SLURM_TMPDIR/<to_save> $SCRATCH
+Temporary job results
+Processed datasets
+Optimized for small Files
 
 
+no
+no
+90 days
 
+$SLURM_TMPDIR
+Highest
 
-Using CometML and Wandb
-The compute nodes for Beluga don’t have access to the internet,
-but there is a special module that can be loaded in order to allow
-training scripts to access some specific servers, which includes
-the necessary servers for using CometML and Wandb (“Weights and Biases”).
-module load httpproxy
-More documentation about this can be found here.",1338,"[-0.008518395014107227, -0.013767942786216736, -0.0093494588509202, -0.0371762178838253, -0.018144875764846802, 0.0013080585049465299, -0.03703770786523819, 0.00247760652564466, 0.00042310627759434283, -0.02718961052596569, 0.012590603902935982, -0.006146403029561043, -0.02322820946574211, -0.004238421097397804, -0.008061311207711697, -0.008227523416280746, 0.009224798530340195, -0.023089699447155, -0.0011530998162925243, -0.005450387950986624, -0.006440738216042519, 0.0035597197711467743, 0.015346962958574295, -0.029308820143342018, -0.018934383988380432, -0.0007241501589305699, 0.021940061822533607, -0.008255225606262684, -0.0047855377197265625, 0.006911673583090305, 0.00018385103612672538, -0.018144875764846802, -0.01699523814022541, -0.011094690300524235, -0.024059273302555084, -0.0030229915864765644, 0.026677120476961136, -0.005218382924795151, 0.012285880744457245, 0.0049517503939569, 0.030306097120046616, 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-24,"Launching Jobs
-Users must specify the resource allocation Group Name using the flag
---account=rrg-bengioy-ad.  To launch a CPU-only job:
-sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
+High speed disk for temporary job
+results
 
-Note
-The account name will differ based on your affiliation.
 
-To launch a GPU job:
-sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
-And to get an interactive session, use the salloc command:
-salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
-The full documentation for jobs launching on Beluga can be found here.",160,"[-0.0019417053554207087, -0.01596130058169365, -0.011181187815964222, -0.03498518466949463, -0.013209113851189613, -0.01127085741609335, -0.02897038124501705, 0.0038834107108414173, -0.02365914359688759, -0.030294740572571754, 0.023755712434649467, 0.012340002693235874, -0.004842192400246859, -0.0014554167864844203, 0.002252102131024003, -0.01202960591763258, 0.0024210962001234293, -0.009505043737590313, 0.012264127843081951, -0.005218117497861385, -0.021603627130389214, 0.010919074527919292, 0.011181187815964222, 0.005176731385290623, -0.004738726653158665, -0.008622136898338795, 0.016306186094880104, 0.0021038015838712454, 0.0130021832883358, 0.006604556925594807, -0.011953731067478657, 0.006170001346617937, 0.005935479421168566, 0.006121717393398285, -0.02797711081802845, 0.010725938715040684, -0.0036454396322369576, 0.008642829954624176, 0.018554838374257088, -0.0014442079700529575, 0.030294740572571754, 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-25,"Beluga nodes description
-Each GPU node consists of:
+4TB/-
+no
+at job end
 
-40 CPU cores
-186 GB RAM
-4 GPU NVIDIA V100 (16GB)
+/network/projects/<groupname>/
+Fair
 
+Shared space to facilitate
+collaboration between researchers
+Long-term project storage
 
-Tip
-You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
-requesting (as explained here),
-otherwise, your job will count for more than 1 allocation, and will take
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-26,"Beluga Storage
 
+200GB/1000K
+Daily
+no
 
+$ARCHIVE or /network/archive/<u>/<username>/
+Low
 
+Long-term personal storage
 
 
+500GB
+no
+no
 
 
-Storage
-Path
-Usage
 
 
+Note
+The $HOME file system is backed up once a day. For any file
+restoration request, file a request to Mila’s IT support with the path to the file or directory to
+restore, with the required date.
 
-$HOME
-/home/<user>/
 
-Code
-Specific libraries
+Warning
+Currently there is no backup system for any other file systems of
+the Mila cluster. Storage local to personal computers, Google Drive and other
+related solutions should be used to backup important data
 
+",291,"[0.013501984067261219, -2.8280830520088784e-05, 0.014077569358050823, -0.03381390497088432, -0.026990098878741264, 0.009472886100411415, -0.0175172109156847, -0.023370152339339256, -0.003517658682540059, -0.0199859868735075, 0.02879313752055168, -0.012392422184348106, 0.014042895287275314, 0.006823807023465633, -0.009833494201302528, 0.00026265415363013744, 0.014660089276731014, -0.008571367710828781, 0.020984593778848648, -0.02515931986272335, 0.0034847185015678406, 0.023800106719136238, -0.02979174256324768, -0.005440321750938892, 0.004729508422315121, -0.004486791789531708, 0.020152421668171883, -0.022704415023326874, 0.017392385751008987, -0.0016270688502117991, 0.01173361949622631, -0.02766970545053482, -0.004878605715930462, -0.007080393377691507, -0.024687757715582848, -0.001017676549963653, 0.012961071915924549, -0.019930509850382805, 0.008037390187382698, 0.006255156360566616, -0.0032177302055060863, 0.02808579057455063, 0.010180232115089893, -0.007995781488716602, 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+$HOME,https://docs.mila.quebec/Information.html#home,"$HOME
+$HOME is appropriate for codes and libraries which are small and read once,
+as well as the experimental results that would be needed at a later time (e.g.
+the weights of a network referenced in a paper).
+Quotas are enabled on $HOME for both disk capacity (blocks) and number of
+files (inodes). The limits for blocks and inodes are respectively 100GiB and 1
+million per user. The command to check the quota usage from a login node is:
+beegfs-ctl --cfgFile=/etc/beegfs/home.d/beegfs-client.conf --getquota --uid $USER
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+$SCRATCH,https://docs.mila.quebec/Information.html#scratch,"$SCRATCH
+$SCRATCH can be used to store processed datasets, work in progress datasets
+or temporary job results. Its block size is optimized for small files which
+minimizes the performance hit of working on extracted datasets.
 
+Note
+Auto-cleanup: this file system is cleared on a weekly basis,
+files not used for more than 90 days will be deleted.
 
-$HOME/projects
-/project/rpp-bengioy
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+$SLURM_TMPDIR,https://docs.mila.quebec/Information.html#slurm-tmpdir,"$SLURM_TMPDIR
+$SLURM_TMPDIR points to the local disk of the node on which a job is
+running. It should be used to copy the data on the node at the beginning of the
+job and write intermediate checkpoints. This folder is cleared after each job.
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+projects,https://docs.mila.quebec/Information.html#projects,"projects
+projects can be used for collaborative projects. It aims to ease the
+sharing of data between users working on a long-term project.
+Quotas are enabled on projects for both disk capacity (blocks) and number
+of files (inodes). The limits for blocks and inodes are respectively 200GiB and
+1 million per user and per group.
 
-Compressed raw datasets
+Note
+It is possible to request higher quota limits if the project requires
+it. File a request to Mila’s IT support.
 
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+$ARCHIVE,https://docs.mila.quebec/Information.html#archive,"$ARCHIVE
+$ARCHIVE purpose is to store data other than datasets that has to be kept
+long-term (e.g.  generated samples, logs, data relevant for paper submission).
+$ARCHIVE is only available on the login nodes. Because this file system
+is tuned for large files, it is recommended to archive your directories. For
+example, to archive the results of an experiment in
+$SCRATCH/my_experiment_results/, run the commands below from a login node:
+cd $SCRATCH
+tar cJf $ARCHIVE/my_experiment_results.tar.xz --xattrs my_experiment_results
+Disk capacity quotas are enabled on $ARCHIVE. The soft limit per user is
+500GB, the hard limit is 550GB. The grace time is 7 days. This means that one
+can use more than 500GB for 7 days before the file system enforces quota.
+However, it is not possible to use more than 550GB.
+The command to check the quota usage from a login node is df:
+df -h $ARCHIVE
 
+Note
+There is NO backup of this file system.
 
-$SCRATCH
-/scratch/<user>
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+datasets,https://docs.mila.quebec/Information.html#datasets,"datasets
+datasets contains curated datasets to the benefit of the Mila community.
+To request the addition of a dataset or a preprocessed dataset you think could
+benefit the research of others, you can fill this form. Datasets can also be browsed from the
+web : Mila Datasets
+Datasets in datasets/restricted are restricted and require an explicit
+request to gain access. Please submit a support ticket mentioning the dataset’s
+access group (ex.: scannet_users), your cluster’s username and the
+approbation of the group owner. You can find the dataset’s access group by
+listing the content of /network/datasets/restricted with the ls command.
+Those datasets are mirrored to the Alliance clusters in
+~/projects/rrg-bengioy-ad/data/curated/ if they follow Digital Research
+Alliance of Canada’s good practices on data.
+To list the local datasets on an Alliance cluster, you can execute the
+following command:
+ssh [CLUSTER_LOGIN] -C ""projects/rrg-bengioy-ad/data/curated/list_datasets_cc.sh""
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+Data Transmission,https://docs.mila.quebec/Information.html#data-transmission,"Data Transmission
+Multiple methods can be used to transfer data to/from the cluster:
 
-Processed datasets
-Experimental results
-Logs of experiments
+rsync --bwlimit=10mb; this is the favored method since the bandwidth can
+be limited to prevent impacting the usage of the cluster: rsync
+Digital Research Alliance of Canada: Globus
+
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+Getting started,https://docs.mila.quebec/Getting_started.html#getting-started,"Getting started
+See User’s guide.
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+User’s guide,https://docs.mila.quebec/Userguide.html#user-s-guide,"User’s guide
+…or IDT’s list of opinionated howtos
+This section seeks to provide users of the Mila infrastructure with practical
+knowledge, tips and tricks and example commands.
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+Quick Start,https://docs.mila.quebec/Userguide.html#quick-start,"Quick Start
+Users first need login access to the cluster. It is
+recommended to install milatools which will help in the set up of the
+ssh configuration needed to securely and easily connect to the
+cluster.
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+mila code,https://docs.mila.quebec/Userguide.html#mila-code,"mila code
+milatools also makes it easy to run and debug code on the Mila cluster. Using
+the mila code command will allow you to use VSCode on the server. Simply run:
+mila code path/on/cluster
 
 
+The details of the command can be found on the github page of the package. Note that you need to
+first setup your ssh configuration using mila init before the mila code
+command can be used. The initialisation of the ssh configuration is explained
+here and on the github page of the package.
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+Logging in to the cluster,https://docs.mila.quebec/Userguide.html#logging-in-to-the-cluster,"Logging in to the cluster
+To access the Mila Cluster clusters, you will need a Mila account. Please contact
+Mila systems administrators if you don’t have it already. Our IT support service
+is available here: https://it-support.mila.quebec/
+You will also need to complete and return an IT Onboarding Training to get
+access to the cluster.  Please refer to the Mila Intranet for more
+informations:
+https://sites.google.com/mila.quebec/mila-intranet/it-infrastructure/it-onboarding-training
+IMPORTANT : Your access to the Cluster is granted based on your status at
+Mila (for students, your status is the same as your main supervisor’ status),
+and on the duration of your stay, set during the creation of your account. The
+following have access to the cluster : Current Students of Core Professors -
+Core Professors - Staff
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+SSH Login,https://docs.mila.quebec/Userguide.html#ssh-login,"SSH Login
+You can access the Mila cluster via ssh:
+# Generic login, will send you to one of the 4 login nodes to spread the load
+ssh <user>@login.server.mila.quebec -p 2222
 
-$SLURM_TMPDIR
+# To connect to a specific login node, X in [1, 2, 3, 4]
+ssh <user>@login-X.login.server.mila.quebec -p 2222
+Four login nodes are available and accessible behind a load balancer. At each
+connection, you will be redirected to the least loaded login-node.
+The ECDSA, RSA and ED25519 fingerprints for Mila’s login nodes are:
+SHA256:baEGIa311fhnxBWsIZJ/zYhq2WfCttwyHRKzAb8zlp8 (ECDSA)
+SHA256:Xr0/JqV/+5DNguPfiN5hb8rSG+nBAcfVCJoSyrR0W0o (RSA)
+SHA256:gfXZzaPiaYHcrPqzHvBi6v+BWRS/lXOS/zAjOKeoBJg (ED25519)
 
 
-Temporary job results
 
+Important
+Login nodes are merely entry points to the cluster. They give you access
+to the compute nodes and to the filesystem, but they are not meant to run
+anything heavy. Do not run compute-heavy programs on these nodes,
+because in doing so you could bring them down, impeding cluster access for
+everyone.
+This means no training or experiments, no compiling programs, no Python
+scripts, but also no zip of a large folder or anything that demands a
+sustained amount of computation.
+Rule of thumb: never run a program that takes more than a few seconds on
+a login node.
 
+Note
+In a similar vein, you should not run VSCode remote SSH instances directly
+on login nodes, because even though they are typically not very
+computationally expensive, when many people do it, they add up! See
+Visual Studio Code for specific instructions.
 
 
-
-They are roughly listed in order of increasing performance and optimized for
-different uses:
-
-The $HOME folder on NFS is appropriate for codes and libraries which are
-small and read once. Do not write experiemental results here!
-The $HOME/projects folder should only contain compressed raw datasets
-(processed datasets should go in $SCRATCH). We have a limit on the
-size and number of file in $HOME/projects, so do not put anything else
-there.  If you add a new dataset there (make sure it is readable by every
-member of the group using chgrp -R rpp-bengioy <dataset>).
-The $SCRATCH space can be used for short term storage. It has good
-performance and large quotas, but is purged regularly (every file that has
-not been used in the last 3 months gets deleted, but you receive an email
-before this happens).
-$SLURM_TMPDIR points to the local disk of the node on which a job is
-running. It should be used to copy the data on the node at the beginning of
-the job and write intermediate checkpoints. This folder is cleared after each
-job.
-
-When an experiment is finished, results should be transferred back to Mila
-servers.
-More details on storage can be found here.",336,"[-0.006038684397935867, 0.008936967700719833, 0.010097705759108067, -0.044207729399204254, -0.040504761040210724, 0.00046287087025120854, -0.01986784115433693, -0.02140599675476551, 0.0057894461788237095, -0.03392487391829491, 0.007797593716531992, -0.014683688059449196, -0.006928820628672838, 0.003241876373067498, -0.0011313632130622864, 0.006900336127728224, 0.005938989110291004, -0.019497545436024666, 0.006629734765738249, -0.01875695213675499, -0.014057031832635403, 0.02418322116136551, -0.01313128974288702, -0.03742844983935356, -0.003941523376852274, -0.015239132568240166, 0.016777288168668747, -0.014669445343315601, 0.009827104397118092, -0.004457802511751652, 0.010147553868591785, -0.025436533614993095, 0.004867265000939369, -0.011329654604196548, -0.025279870256781578, 0.00023477344075217843, 0.011023447848856449, -0.008367280475795269, 0.017774241045117378, 0.007712140679359436, 0.0032721408642828465, 0.005009687040001154, 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-27,"Modules
-Many software, such as Python or MATLAB are already compiled and available on
-Beluga through the module command and its subcommands. Its full
-documentation can be found here.
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+mila init,https://docs.mila.quebec/Userguide.html#mila-init,"mila init
+To make it easier to set up a productive environment, Mila publishes the
+milatools package, which defines a mila init command which will
+automatically perform some of the below steps for you. You can install it with
+pip and use it, provided your Python version is at least 3.8:
+$ pip install milatools
+$ mila init
 
 
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+SSH Config,https://docs.mila.quebec/Userguide.html#ssh-config,"SSH Config
+The login nodes support the following authentication mechanisms:
+publickey,keyboard-interactive.  If you would like to set an entry in your
+.ssh/config file, please use the following recommendation:
+Host mila
+    User YOUR-USERNAME
+    Hostname login.server.mila.quebec
+    PreferredAuthentications publickey,keyboard-interactive
+    Port 2222
+    ServerAliveInterval 120
+    ServerAliveCountMax 5
 
 
+Then you can simply write ssh mila to connect to a login node. You will also
+be able to use mila with scp, rsync and other such programs.
 
+Tip
+You can run commands on the login node with ssh directly, for example
+ssh mila squeue -u '$USER' (remember to put single quotes around any
+$VARIABLE you want to evaluate on the remote side, otherwise it will be
+evaluated locally before ssh is even executed).
 
-module avail
-Displays all the available modules
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+Passwordless login,https://docs.mila.quebec/Userguide.html#passwordless-login,"Passwordless login
+To save you some repetitive typing it is highly recommended to set up public
+key authentication, which means you won’t have to enter your password every time
+you connect to the cluster.
+# ON YOUR LOCAL MACHINE
+# You might already have done this in the past, but if you haven't:
+ssh-keygen  # Press ENTER 3x
 
-module load <module>
-Loads <module>
+# Copy your public key over to the cluster
+# You will need to enter your password
+ssh-copy-id mila
 
-module spider <module>
-Shows specific details about <module>
 
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+Connecting to compute nodes,https://docs.mila.quebec/Userguide.html#connecting-to-compute-nodes,"Connecting to compute nodes
+If (and only if) you have a job running on compute node “cnode”, you are
+allowed to SSH to it directly, if for some reason you need a second terminal.
+That session will be automatically ended when your job is relinquished.
+First, however, you need to have
+password-less ssh either with a key present in your home or with an
+ssh-agent. To generate a key pair on the login node:
+# ON A LOGIN NODE
+ssh-keygen  # Press ENTER 3x
+cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
+chmod 600 ~/.ssh/authorized_keys
+chmod 700 ~/.ssh
 
 
-In particular, if you with to use Python 3.6 you can simply do:
-module load python/3.6
+Then from the login node you can write ssh <node>. From your local
+machine, you can use ssh -J mila USERNAME@<node> (-J represents a “jump”
+through the login node, necessary because the compute nodes are behind a
+firewall).
+If you wish, you may also add the following wildcard rule in your .ssh/config:
+Host *.server.mila.quebec !*login.server.mila.quebec
+    HostName %h
+    User YOUR-USERNAME
+    ProxyJump mila
 
-Tip
-If you wish to use Python on the cluster, we strongly encourage you to
-read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.
 
-The cluster has many Python packages (or wheels), such already compiled for
-the cluster. See here for the
-details. In particular, you can browse the packages by doing:
-avail_wheels <wheel>
-Such wheels can be installed using pip. Moreover, the most efficient way to use
-modules on the cluster is to build your environnement inside your job.
-See the script example below.",210,"[0.0008739479817450047, -0.007414562162011862, 0.005203700624406338, -0.01633620820939541, -0.01620824821293354, 0.012582718394696712, -0.017957031726837158, 0.002338821766898036, 0.006401546765118837, -0.019904864951968193, 0.027454497292637825, -0.004208457190543413, -0.013819663785398006, 0.00542052136734128, -0.01858261413872242, -0.010101718828082085, 0.008914535865187645, 0.006266477983444929, 0.004275991581380367, -0.004212011583149433, -0.0007046677637845278, 0.016393078491091728, 0.017544716596603394, -0.042283620685338974, -0.026644084602594376, 0.021881133317947388, 0.0034300347324460745, -0.018710574135184288, -0.0017594479722902179, 0.008658616803586483, 0.022065965458750725, -0.004315090365707874, 0.0015950551023706794, -0.02867722325026989, -0.019023364409804344, -0.003952537663280964, 0.03184778615832329, -0.013976058922708035, 0.0030248286202549934, 0.01844043657183647, 0.03460603207349777, 0.03153499215841293, 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-28,"Script Example
-Here is a sbatch script that follows good practices on Beluga:
+This will let you connect to a compute node with ssh <node>.server.mila.quebec.
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+Running your code,https://docs.mila.quebec/Userguide.html#running-your-code,"Running your code
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+SLURM commands guide,https://docs.mila.quebec/Userguide.html#slurm-commands-guide,"SLURM commands guide
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+Basic Usage,https://docs.mila.quebec/Userguide.html#basic-usage,"Basic Usage
+The SLURM documentation
+provides extensive information on the available commands to query the cluster
+status or submit jobs.
+Below are some basic examples of how to use SLURM.
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+Batch job,https://docs.mila.quebec/Userguide.html#batch-job,"Batch job
+In order to submit a batch job, you have to create a script containing the main
+command(s) you would like to execute on the allocated resources/nodes.
  1#!/bin/bash
- 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
- 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
- 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
- 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
- 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
- 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
+ 2#SBATCH --job-name=test
+ 3#SBATCH --output=job_output.txt
+ 4#SBATCH --error=job_error.txt
+ 5#SBATCH --ntasks=1
+ 6#SBATCH --time=10:00
+ 7#SBATCH --mem=100Gb
  8
- 9# 1. Create your environement locally
-10module load python/3.6
-11virtualenv --no-download $SLURM_TMPDIR/env
-12source $SLURM_TMPDIR/env/bin/activate
-13pip install --no-index torch torchvision
-14
-15# 2. Copy your dataset on the compute node
-16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
-17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
-18
-19# 3. Eventually unzip your dataset
-20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
-21
-22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
-23#    and look for the dataset into $SLURM_TMPDIR
-24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
-25
-26# 5. Copy whatever you want to save on $SCRATCH
-27cp $SLURM_TMPDIR/<to_save> $SCRATCH",389,"[-0.018072377890348434, 0.0018311974126845598, 0.01637466996908188, -0.013123011216521263, -0.003434778656810522, 0.008851357735693455, -0.009919270873069763, -0.013396834954619408, -0.008474850095808506, -0.017538420855998993, 0.02350778318941593, 0.012486370280385017, -0.014006093144416809, 0.0018979419255629182, 0.0029555868823081255, 0.006010435055941343, 0.016785405576229095, -0.012185163795948029, 0.002164920326322317, -0.014293608255684376, -0.012814958579838276, 0.007591767702251673, 0.009953498840332031, -0.015690110623836517, -0.009036188945174217, -0.007701297290623188, 0.0061233872547745705, -0.01571749337017536, 0.004322994966059923, 0.009015652351081371, 0.0032379678450524807, -0.017415201291441917, 0.006181574892252684, -0.006783987395465374, -0.023877445608377457, 0.00041287511703558266, -0.0019732436630874872, -0.009590682573616505, 0.015046623535454273, -0.0053566801361739635, 0.0226726196706295, 0.02306966483592987, 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-29,"Using CometML and Wandb
-The compute nodes for Beluga don’t have access to the internet,
-but there is a special module that can be loaded in order to allow
-training scripts to access some specific servers, which includes
-the necessary servers for using CometML and Wandb (“Weights and Biases”).
-module load httpproxy
-More documentation about this can be found here.",82,"[-0.022885732352733612, -0.018110807985067368, 0.022306526079773903, -0.01950938068330288, -0.0031538517214357853, 0.009189610369503498, -0.004605400376021862, -0.0180966816842556, 0.015666836872696877, -0.03144669160246849, 0.027265101671218872, 0.023168271407485008, -0.009005959145724773, -0.0055413139052689075, -0.008885880000889301, 0.006848060991615057, 0.007522624917328358, -0.004421749617904425, -0.008151276037096977, -0.015666836872696877, -0.005421234294772148, -0.010206754319369793, 0.0063606793992221355, -0.03681495040655136, -0.03136193007230759, -0.003494665492326021, 0.036588918417692184, -0.0290451031178236, -0.009733499959111214, -0.002535795560106635, 0.016457948833703995, -0.012120962142944336, -0.007854609750211239, -0.027604149654507637, 0.0011204472975805402, -0.010235007852315903, 0.019269222393631935, -0.017616363242268562, 0.013583105988800526, -0.0040579792112112045, 0.01720668002963066, 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-30,"Graham
-Graham is a cluster located at University of Waterloo. It uses SLURM to schedule
-jobs. Its full documentation can be found here, and its current status here.
-You can access Graham via ssh:
-ssh <user>@graham.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
-Since its structure is similar to Beluga, please look at the Beluga
-documentation, as well as relevant parts of the Digital Research Alliance of
-Canada Documentation.
-
-Note
-For GPU jobs the ressource allocation Group Name is the same as Beluga, so you should use the flag --account=rrg-bengioy-ad for GPU jobs.",147,"[-0.013273514807224274, -0.020435990765690804, -0.011241206899285316, -0.04425916448235512, -0.0037082573398947716, 0.0074941376224160194, -0.04575517028570175, 0.006851984653621912, -0.014578991569578648, -0.01656896062195301, 0.00021280140208546072, -0.016244355589151382, 0.003036113688722253, 0.0062380582094192505, -0.004233976360410452, -0.0064003607258200645, 0.001181985018774867, -0.008637310937047005, -0.025206271559000015, -0.011953925713896751, -0.017429867759346962, -0.013506383635103703, 0.019956141710281372, -0.008369159884750843, -0.00932886078953743, 0.00422691972926259, 0.014607218094170094, -0.0011572868097573519, 0.006513266824185848, 0.0003223996318411082, 0.002027016133069992, -0.008623197674751282, -0.014310839585959911, -0.0026303576305508614, -0.030061230063438416, 0.005729981232434511, 0.012716041877865791, 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-31,"Cedar
-Cedar is a cluster located at Simon Fraser University. It uses SLURM to schedule
-jobs. Its full documentation can be found here, and its current status here.
-You can access Cedar via ssh:
-ssh <user>@cedar.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
-Since its structure is similar to Beluga, please look at the Beluga
-documentation, as well as relevant parts of the Digital Research Alliance of
-Canada Documentation.
+ 9module load python/3.5
+10python my_script.py
 
-Note
-However, we don’t have any CPU priority on Cedar, in this case you can
-use --account=def-bengioy for CPU. Thus, it might take some time before
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-32,"Niagara
-Niagara is a cluster located at University of Toronto. It uses SLURM to schedule
-jobs. Its full documentation can be found here, and its current status here.
-You can access Niagara via ssh:
-ssh <user>@niagara.computecanada.ca
-Where <user> is the username you created previously (see Account Creation).
-Since its structure is similar to Beluga, please look at the Beluga
-documentation, as well as relevant parts of the Digital Research Alliance of
-Canada Documentation.",108,"[-0.00123020785395056, -0.03160620108246803, -0.00421180808916688, -0.024960432201623917, 0.010406059212982655, 0.015379802323877811, -0.05717335641384125, 0.0010820537572726607, -0.01571843959391117, -0.026554850861430168, 0.015859538689255714, -0.009651178494095802, 0.014187514781951904, -8.868299482855946e-05, -0.0033299389760941267, -0.027091028168797493, 0.003619191935285926, -0.035444095730781555, -0.0005370584549382329, -0.012099248357117176, -0.015704330056905746, 0.003929609898477793, 0.013404414989054203, -0.02335190214216709, -0.027867073193192482, 0.0064376466907560825, 0.007217219099402428, -0.012346171773970127, 0.0010406059445813298, 0.0047374023124575615, -0.0003796447708737105, -0.002769069978967309, -0.0026050421874970198, -0.01086463127285242, -0.019570445641875267, 0.004183588549494743, 0.003206477267667651, -0.01727052964270115, -0.013785382732748985, 0.0074006482027471066, 0.021771591156721115, 0.0011111554922536016, 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-33,"FAQ
 
-What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
-The structure of the file system is different than a classical Linux, so your
-code has trouble finding libraries. See how to install binary packages.
+Your job script is then submitted to SLURM with sbatch (ref.)
+sbatch job_script
+sbatch: Submitted batch job 4323674
+The working directory of the job will be the one where your executed sbatch.
 
+Tip
+Slurm directives can be specified on the command line alongside sbatch or
+inside the job script with a line starting with #SBATCH.
 
-Disk quota exceeded error on /project file systems
-You have files in /project with the wrong permissions. See how to change
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-34,"What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
-The structure of the file system is different than a classical Linux, so your
-code has trouble finding libraries. See how to install binary packages.",58,"[0.0077850413508713245, 0.008312370628118515, -0.0015299479709938169, -0.03355477750301361, -0.01691615954041481, 0.015861501917243004, -0.020829495042562485, -0.006407741457223892, -0.013911771588027477, -0.000915887241717428, 0.014348899945616722, -0.0010390463285148144, -0.015389680862426758, -0.005696541164070368, -0.012773851864039898, 0.01951117254793644, 0.011143294163048267, 0.03000224567949772, 0.014008911326527596, 0.0008738223114050925, 0.013460766524076462, 0.008180538192391396, 0.009859664365649223, -0.029641442000865936, -0.0011656746501103044, 0.00846501812338829, 0.009221319109201431, -0.019358525052666664, 0.019289139658212662, 0.016222305595874786, 0.011906534433364868, -0.026463590562343597, -0.007722594775259495, -0.018137343227863312, -0.02772640436887741, -0.03064059093594551, 0.007771164178848267, -0.007708717603236437, -0.002353899646550417, -0.019025474786758423, 0.03380456566810608, -0.009679262526333332, 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-35,"Disk quota exceeded error on /project file systems
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-36,"Roles and authorizations
-There are mainly two types of researchers statuses at Mila :
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+Interactive job,https://docs.mila.quebec/Userguide.html#interactive-job,"Interactive job
+Workload managers usually run batch jobs to avoid having to watch its
+progression and let the scheduler run it as soon as resources are available. If
+you want to get access to a shell while leveraging cluster resources, you can
+submit an interactive jobs where the main executable is a shell with the
+srun/salloc (srun/salloc) commands
+salloc
+Will start an interactive job on the first node available with the default
+resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
+sbatch with the exception that the environment is not passed.
 
-Core researchers
-Affiliated researchers
+Tip
+To pass your current environment to an interactive job, add
+--preserve-env to srun.
 
-This is determined by Mila policy. Core researchers have access to the Mila
-computing cluster. See your supervisor’s Mila status to know what is your own
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-37,"Overview of available computing resources at Mila
-The Mila cluster is to be used for regular development and relatively small
-number of jobs (< 5). It is a heterogeneous cluster. It uses
-SLURM to schedule jobs.
+salloc can also be used and is mostly a wrapper around srun if provided
+without more info but it gives more flexibility if for example you want to get
+an allocation on multiple nodes.
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+Job submission arguments,https://docs.mila.quebec/Userguide.html#job-submission-arguments,"Job submission arguments
+In order to accurately select the resources for your job, several arguments are
+available. The most important ones are:
 
-Mila cluster versus Digital Research Alliance of Canada clusters
-There are a lot of commonalities between the Mila cluster and the clusters from
-Digital Research Alliance of Canada (the Alliance). At the time being, the
-Alliance clusters where we have a large allocation of resources are beluga,
-cedar, graham and narval. We also have comparable computational resources
-in the Mila cluster, with more to come.
-The main distinguishing factor is that we have more control over our own
-cluster than we have over the ones at the Alliance. Notably, also, the compute
-nodes in the Mila cluster all have unrestricted access to the Internet, which
-is not the case in general for the Alliance clusters (although cedar does
-allow it).
-At the current time of this writing (June 2021), Mila students are advised to
-use a healthy diet of a mix of Mila and Alliance clusters. This is especially
-true in times when your favorite cluster is oversubscribed, because you can
-easily switch over to a different one if you are used to it.
 
 
-Guarantees about one GPU as absolute minimum
-There are certain guarantees that the Mila cluster tries to honor when it comes
-to giving at minimum one GPU per student, all the time, to be used in
-interactive mode. This is strictly better than “one GPU per student on average”
-because it’s a floor meaning that, at any time, you should be able to ask for
-your GPU, right now, and get it (although it might take a minute for the
-request to be processed by SLURM).
-Interactive sessions are possible on the Alliance clusters, and there are
-generally special rules that allow you to get resources more easily if you
-request them for a very short duration (for testing code before queueing long
-jobs). You do not get the same guarantee as on the Mila cluster, however.",447,"[-0.0027316929772496223, -0.018620111048221588, -0.014884939417243004, -0.034620027989149094, -0.03434128314256668, 0.017505135387182236, -0.02918451651930809, -0.005780457518994808, -0.014661944471299648, -0.04289872944355011, 0.008898908272385597, -0.00122124794870615, 0.012898887507617474, -0.01608354039490223, -0.027219370007514954, -0.011721192859113216, 0.02425074391067028, -0.015930229797959328, 0.009233401156961918, -0.0033327352721244097, -0.02094762586057186, 0.0035714099649339914, -0.0047316825948655605, -0.015735110267996788, -0.030104372650384903, -0.00044533738400787115, 0.01659921556711197, -0.003172457218170166, -0.0018553909612819552, 0.01116370502859354, 0.008006926625967026, -0.005850143730640411, -0.0064633809961378574, 0.0034668808802962303, -0.022661902010440826, -0.0010461617494001985, 0.027665359899401665, -0.004372799303382635, -0.021086998283863068, -8.966634050011635e-05, 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-38,"Mila cluster versus Digital Research Alliance of Canada clusters
-There are a lot of commonalities between the Mila cluster and the clusters from
-Digital Research Alliance of Canada (the Alliance). At the time being, the
-Alliance clusters where we have a large allocation of resources are beluga,
-cedar, graham and narval. We also have comparable computational resources
-in the Mila cluster, with more to come.
-The main distinguishing factor is that we have more control over our own
-cluster than we have over the ones at the Alliance. Notably, also, the compute
-nodes in the Mila cluster all have unrestricted access to the Internet, which
-is not the case in general for the Alliance clusters (although cedar does
-allow it).
-At the current time of this writing (June 2021), Mila students are advised to
-use a healthy diet of a mix of Mila and Alliance clusters. This is especially
-true in times when your favorite cluster is oversubscribed, because you can
-easily switch over to a different one if you are used to it.",225,"[0.007346095517277718, -0.020403482019901276, -0.014818485826253891, -0.044708043336868286, -0.03061925619840622, 0.030899910256266594, -0.030310537666082382, 0.0011041946709156036, -0.007739010266959667, -0.03614812344312668, -0.002011932199820876, -0.004308025352656841, 0.005283295176923275, -0.02187691256403923, -0.029636971652507782, -0.004522023722529411, 0.02654978632926941, -0.026086708530783653, 0.011275239288806915, 0.0019593096803873777, -0.02378535270690918, 0.013127550482749939, -0.007760059088468552, -0.015295595861971378, -0.014958811923861504, 0.009296635165810585, 0.014888648875057697, -0.0033801160752773285, -0.0013085452374070883, 0.002673221053555608, 0.009212438948452473, 0.001205931417644024, -0.00757061829790473, 0.006644462700933218, -0.013822167180478573, -0.009647451341152191, 0.01758291944861412, -0.021034952253103256, -0.014846550300717354, -0.002364502754062414, 0.009156308136880398, 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-39,"Guarantees about one GPU as absolute minimum
-There are certain guarantees that the Mila cluster tries to honor when it comes
-to giving at minimum one GPU per student, all the time, to be used in
-interactive mode. This is strictly better than “one GPU per student on average”
-because it’s a floor meaning that, at any time, you should be able to ask for
-your GPU, right now, and get it (although it might take a minute for the
-request to be processed by SLURM).
-Interactive sessions are possible on the Alliance clusters, and there are
-generally special rules that allow you to get resources more easily if you
-request them for a very short duration (for testing code before queueing long
-jobs). You do not get the same guarantee as on the Mila cluster, however.",173,"[-0.011539354920387268, -0.00905919261276722, -0.006082302890717983, -0.035764358937740326, -0.021300218999385834, 0.0005627259379252791, -0.032596420496702194, -0.002457583788782358, -0.02161979116499424, -0.032763153314590454, 0.019535621628165245, 0.02300923690199852, 0.009524657391011715, -0.008718778379261494, -0.020536022260785103, -0.005175689235329628, 0.02439868450164795, 0.0019834351260215044, 0.013310899958014488, 0.0007520380313508213, -0.01335952989757061, 0.014109831303358078, -0.001164530054666102, -0.017548711970448494, -0.032679785043001175, -0.012407759204506874, 0.02081391215324402, -0.005825255531817675, 0.00540494779124856, 0.007211228366941214, 0.016548311337828636, -0.007954582571983337, -0.026149386540055275, -0.01439466793090105, -0.026330014690756798, 0.0034041444305330515, 0.018368486315011978, 0.008371416479349136, -0.02238398604094982, -0.006172616966068745, 0.024259740486741066, 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-40,"Node profile description
 
 
 
+Argument
+Description
 
 
 
+-n, –ntasks=<number>
+The number of task in your script, usually =1
 
+-c, –cpus-per-task=<ncpus>
+The number of cores for each task
 
+-t, –time=<time>
+Time requested for your job
 
+–mem=<size[units]>
+Memory requested for all your tasks
 
+–gres=<list>
+Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL
 
 
 
 
+Tip
+Always consider requesting the adequate amount of resources to improve the
+scheduling of your job (small jobs always run first).
 
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+Checking job status,https://docs.mila.quebec/Userguide.html#checking-job-status,"Checking job status
+To display jobs currently in queue, use squeue and to get only your jobs type
+squeue -u $USER
+JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
+133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)
 
-Name
-GPU
-CPUs
-Sockets
-Cores/Socket
-Threads/Core
-Memory (GB)
-TmpDisk (TB)
-Arch
-Slurm Features
-
-Model
-Mem
-#
-GPU Arch and Memory
-
-
-
-GPU Compute Nodes
-
-cn-a[001-011]
-RTX8000
-48
-8
-40
-2
-20
-1
-384
-3.6
-x86_64
-turing,48gb
-
-cn-b[001-005]
-V100
-32
-8
-40
-2
-20
-1
-384
-3.6
-x86_64
-volta,nvlink,32gb
-
-cn-c[001-040]
-RTX8000
-48
-8
-64
-2
-32
-1
-384
-3
-x86_64
-turing,48gb
-
-cn-g[001-026]
-A100
-80
-4
-64
-2
-32
-1
-1024
-7
-x86_64
-ampere,nvlink,80gb
-
-DGX Systems
-
-cn-d[001-002]
-A100
-40
-8
-128
-2
-64
-1
-1024
-14
-x86_64
-ampere,nvlink,40gb
-
-cn-d[003-004]
-A100
-80
-8
-128
-2
-64
-1
-2048
-28
-x86_64
-ampere,nvlink,80gb
-
-cn-e[002-003]
-V100
-32
-8
-40
-2
-20
-1
-512
-7
-x86_64
-volta,32gb
-
-CPU Compute Nodes
-
-cn-f[001-004]
+Note
+The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
+at any given time from the given association. If this limit is reached, new submission requests
+will be denied until existing jobs in this association complete.
 
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+Removing a job,https://docs.mila.quebec/Userguide.html#removing-a-job,"Removing a job
+To cancel your job simply use scancel
+scancel 4323674
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+Partitioning,https://docs.mila.quebec/Userguide.html#partitioning,"Partitioning
+Since we don’t have many GPUs on the cluster, resources must be shared as fairly
+as possible.  The --partition=/-p flag of SLURM allows you to set the
+priority you need for a job.  Each job assigned with a priority can preempt jobs
+with a lower priority: unkillable > main > long. Once preempted, your job is
+killed without notice and is automatically re-queued on the same partition until
+resources are available. (To leverage a different preemption mechanism, see the
+Handling preemption)
 
 
 
@@ -1402,5722 +1824,1559 @@ cn-f[001-004]
 
 
 
+Flag
+Max Resource Usage
+Max Time
+Note
 
 
 
-32
-1
-32
-1
-256
-10
-x86_64
-rome
+--partition=unkillable
+6  CPUs, mem=32G,  1 GPU
+2 days
 
-cn-h[001-004]
 
+--partition=unkillable-cpu
+2  CPUs, mem=16G
+2 days
+CPU-only jobs
 
+--partition=short-unkillable
+24 CPUs, mem=128G, 4 GPUs
+3 hours (!)
+Large but short jobs
 
+--partition=main
+8  CPUs, mem=48G,  2 GPUs
+5 days
 
 
+--partition=main-cpu
+8  CPUs, mem=64G
+5 days
+CPU-only jobs
 
+--partition=long
+no limit of resources
+7 days
 
 
+--partition=long-cpu
+no limit of resources
+7 days
+CPU-only jobs
 
 
 
 
-64
-2
-32
-1
-768
-7
-x86_64
-milan
+Warning
+Historically, before the 2022 introduction of CPU-only nodes (e.g. the cn-f
+series), CPU jobs ran side-by-side with the GPU jobs on GPU nodes. To prevent
+them obstructing any GPU job, they were always lowest-priority and preemptible.
+This was implemented by automatically assigning them to one of the now-obsolete
+partitions cpu_jobs, cpu_jobs_low or cpu_jobs_low-grace.
+Do not use these partition names anymore. Prefer the *-cpu partition
+names defined above.
+For backwards-compatibility purposes, the legacy partition names are translated
+to their effective equivalent long-cpu, but they will eventually be removed
+entirely.
 
-Legacy GPU Compute Nodes
 
-kepler5
-V100
-16
-2
-16
-2
-4
-2
-256
-3.6
-x86_64
-volta,16gb
+Note
+As a convenience, should you request the unkillable, main or long
+partition for a CPU-only job, the partition will be translated to its -cpu
+equivalent automatically.
 
-TITAN RTX
+For instance, to request an unkillable job with 1 GPU, 4 CPUs, 10G of RAM and
+12h of computation do:
+sbatch --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable <job.sh>
+You can also make it an interactive job using salloc:
+salloc --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable
+The Mila cluster has many different types of nodes/GPUs. To request a specific
+type of node/GPU, you can add specific feature requirements to your job
+submission command.
+To access those special nodes you need to request them explicitly by adding the
+flag --constraint=<name>.  The full list of nodes in the Mila Cluster can be
+accessed Node profile description.
+Example:
+To request a machine with 2 GPUs using NVLink, you can use
+sbatch -c 4 --gres=gpu:2 --constraint=nvlink
 
-rtx[1,3-5,7]
-titanrtx
-24
-2
-20
-1
-10
-2
-128
-0.93
-x86_64
-turing,24gb
 
 
 
 
-Special nodes and outliers
 
-DGX A100
-DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
-GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
-interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
-In order to run jobs on a DGX A100, add the flags below to your Slurm
-commands:
---gres=gpu:a100:<number> --reservation=DGXA100
+Feature
+Particularities
 
 
 
+12GB/16GB/24GB/32GB/48GB
+Request a specific amount of GPU memory
 
-MIG
-MIG (Multi-Instance GPU)
-is an NVIDIA technology allowing certain GPUs to be
-partitioned into multiple instances, each of which has a roughly proportional
-amount of compute resources, device memory and bandwidth to that memory.
-NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
-instances. Although this can theoretically be done dynamically, the SLURM job
-scheduler does not support doing so in practice as it does not model
-reconfigurable resources very well. Therefore, the A100s must currently be
-statically partitioned into the required number of instances of every size
-expected to be used.
-The cn-g series of nodes include A100-80GB GPUs. One third have been
-configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
-have been configured in MIG mode, and offer the following profiles:
+volta/turing/ampere
+Request a specific GPU architecture
 
+nvlink
+Machine with GPUs using the NVLink interconnect technology
 
 
 
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+Information on partitions/nodes,https://docs.mila.quebec/Userguide.html#information-on-partitions-nodes,"Information on partitions/nodes
+sinfo (ref.) provides most of the
+information about available nodes and partitions/queues to submit jobs to.
+Partitions are a group of nodes usually sharing similar features. On a
+partition, some job limits can be applied which will override those asked for a
+job (i.e. max time, max CPUs, etc…)
+To display available partitions, simply use
+sinfo
+PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
+batch     up     infinite     2 alloc  node[1,3,5-9]
+batch     up     infinite     6 idle   node[10-15]
+cpu       up     infinite     6 idle   cpu_node[1-15]
+gpu       up     infinite     6 idle   gpu_node[1-15]
+To display available nodes and their status, you can use
+sinfo -N -l
+NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
+node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
+node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
+node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
+...
+And to get statistics on a job running or terminated, use sacct with some of
+the fields you want to display
+sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
+     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
+--------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
+my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
+my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
+Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional t",584,"[0.012504655867815018, 0.0033744636457413435, 0.015325617976486683, -0.02607148326933384, -0.03558964282274246, 0.004790117498487234, -0.0329962894320488, 0.004921164829283953, -0.012470169924199581, -0.03741050884127617, 0.023712635040283203, 0.028692426159977913, -0.018801815807819366, 0.0025054144207388163, 0.0016984398243948817, 0.006179906893521547, 0.009083636105060577, -0.014456568285822868, 0.014691073447465897, -0.00967679638415575, -0.002957182005047798, -7.732427911832929e-05, -0.015008345246315002, -0.0059764389880001545, -0.009663001634180546, 0.04176954925060272, 0.014911784790456295, -0.004248686134815216, -0.003393431194126606, 0.01233222521841526, -0.010021657682955265, 0.007717986591160297, -0.016691267490386963, -0.02880278043448925, -0.012580525130033493, 0.005372932646423578, -0.0008186135091818869, -0.020264025777578354, 0.008462886326014996, -0.01917426474392414, 0.018677664920687675, 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+Information on partitions/nodes,https://docs.mila.quebec/Userguide.html#information-on-partitions-nodes,"ime formats.
+sacct -u $USER --start=2019-01-01
+scontrol (ref.) can be used to
+provide specific information on a job (currently running or recently terminated)
+scontrol show job 43123
+JobId=43123 JobName=python_script.py
+UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
+Priority=645895 Nice=0 Account=my_username QOS=normal
+JobState=RUNNING Reason=None Dependency=(null)
+Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
+RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
+SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
+AccrueTime=2019-03-26T08:49:18
+StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
+PreemptTime=None SuspendTime=None SecsPreSuspend=0
+LastSchedEval=2019-03-26T08:49:18
+Partition=slurm_partition AllocNode:Sid=login-node-1:14586
+ReqNodeList=(null) ExcNodeList=(null)
+NodeList=node2
+BatchHost=node2
+NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
+TRES=cpu=16,mem=32000M,node=1,billing=3
+Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
+MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
+Features=(null) DelayBoot=00:00:00
+OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
+WorkDir=/home/mila/my_username
+StdErr=/home/mila/my_username/slurm-43123.out
+StdIn=/dev/null
+StdOut=/home/mila/my_username/slurm-43123.out
+Power=
+Or more info on a node and its resources
+scontrol show node node9
+NodeName=node9 Arch=x86_64 CoresPerSocket=4
+CPUAlloc=16 CPUTot=16 CPULoad=1.38
+AvailableFeatures=(null)
+ActiveFeatures=(null)
+Gres=(null)
+NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
+OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
+RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
+State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
+Partitions=slurm_partition
+BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
+CfgTRES=cpu=16,mem=32000M,billing=3
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+Useful Commands,https://docs.mila.quebec/Userguide.html#useful-commands,"Useful Commands
 
+sallocGet an interactive job and give you a shell. (ssh like) CPU only
 
+salloc --gres=gpu:1 -c 2 --mem=12000Get an interactive job with one GPU, 2 CPUs and 12000 MB RAM
 
+sbatchstart a batch job (same options as salloc)
 
+sattach --pty <jobid>.0Re-attach a dropped interactive job
 
-Name
-GPU
-Cluster-wide
+sinfostatus of all nodes
 
-Model
-Memory
-Compute
-#
+sinfo -Ogres:27,nodelist,features -tidle,mix,allocList GPU type and FEATURES that you can request
 
+savail(Custom) List available gpu
 
+scancel <jobid>Cancel a job
 
-a100l.1g.10gb
-a100l.1
-A100
-10GB 
-(1/8th)
-1/7th
-of full
-72
+squeuesummary status of all active jobs
 
-a100l.2g.20gb
-a100l.2
-A100
-20GB 
-(2/8th)
-2/7th
-of full
-108
+squeue -u $USERsummary status of all YOUR active jobs
 
-a100l.3g.40gb
-a100l.3
-A100
-40GB 
-(4/8th)
-3/7th
-of full
-72
+squeue -j <jobid>summary status of a specific job
 
+squeue -Ojobid,name,username,partition,state,timeused,nodelist,gres,tresstatus of all jobs including requested resources (see the SLURM squeue doc for all output options)
 
+scontrol show job <jobid>Detailed status of a running job
 
-And can be requested using a SLURM flag such as --gres=gpu:a100l.1
-The partitioning may be revised as needs and SLURM capabilities evolve. Other
-MIG profiles exist and could be introduced.
+sacct -j <job_id> -o NodeListGet the node where a finished job ran
 
-Warning
-MIG has a number of important limitations,
-most notably that a GPU in MIG mode does not support graphics APIs
-(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
-limit every MIG job to exactly one MIG slice and no more. Thus,
---gres=gpu:a100l.3 will work (and request a size-3 slice of an
-a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
-three size-1 slices) will not.
+sacct -u $USER -S <start_time> -E <stop_time>Find info about old jobs
 
+sacct -oJobID,JobName,User,Partition,Node,StateList of current and recent jobs
 
 
-AMD
+",308,"[-0.01309230737388134, 0.0017999329138547182, 0.0111073674634099, -0.02485671080648899, -0.026599586009979248, -0.011342517100274563, -0.004948518704622984, -0.0062764231115579605, -0.030569465830922127, -0.028121143579483032, 0.03397222235798836, 0.011190361343324184, -0.00608277041465044, 0.018480002880096436, 0.002854648744687438, -0.026544256135821342, 0.01932377554476261, -0.0193376075476408, 0.00039487003232352436, 0.0025365049950778484, -0.006048189476132393, 0.004765240475535393, -0.00417736591771245, 0.0023878072388470173, -0.003976797219365835, 0.021370962262153625, 0.009440570138394833, -0.005062635522335768, -0.005259746219962835, 0.005857995245605707, -0.002266774419695139, 0.0065184892155230045, -0.0004385283391457051, -0.014115900732576847, -0.03773461654782295, 0.01567203924059868, 0.012615092098712921, -0.0026402475778013468, -0.005183668807148933, 0.005377321504056454, 0.025340842083096504, 0.020333535969257355, 0.0007694237865507603, -0.01394991297274828, 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+Special GPU requirements,https://docs.mila.quebec/Userguide.html#special-gpu-requirements,"Special GPU requirements
+Specific GPU architecture and memory can be easily requested through the
+--gres flag by using either
 
-Warning
-As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
-more samples. You can join the amd slack channels to get the latest
-information
+--gres=gpu:architecture:number
+--gres=gpu:memory:number
+--gres=gpu:model:number
 
-Mila has a few node equipped with MI50 GPUs.
-srun --gres=gpu -c 8 --reservation=AMD --pty bash
+Example:
+To request 1 GPU with at least 16GB of memory use
+sbatch -c 4 --gres=gpu:16gb:1
+The full list of GPU and their features can be accessed here.
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+Example script,https://docs.mila.quebec/Userguide.html#example-script,"Example script
+Here is a sbatch script that follows good practices on the Mila cluster:
+ 1#!/bin/bash
+ 2
+ 3#SBATCH --partition=unkillable                           # Ask for unkillable job
+ 4#SBATCH --cpus-per-task=2                                # Ask for 2 CPUs
+ 5#SBATCH --gres=gpu:1                                     # Ask for 1 GPU
+ 6#SBATCH --mem=10G                                        # Ask for 10 GB of RAM
+ 7#SBATCH --time=3:00:00                                   # The job will run for 3 hours
+ 8#SBATCH -o /network/scratch/<u>/<username>/slurm-%j.out  # Write the log on scratch
+ 9
+10# 1. Load the required modules
+11module --quiet load anaconda/3
+12
+13# 2. Load your environment
+14conda activate ""<env_name>""
+15
+16# 3. Copy your dataset on the compute node
+17cp /network/datasets/<dataset> $SLURM_TMPDIR
+18
+19# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
+20#    and look for the dataset into $SLURM_TMPDIR
+21python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
+22
+23# 5. Copy whatever you want to save on $SCRATCH
+24cp $SLURM_TMPDIR/<to_save> /network/scratch/<u>/<username>/
+
+
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+Portability concerns and solutions,https://docs.mila.quebec/Userguide.html#portability-concerns-and-solutions,"Portability concerns and solutions
+When working on a software project, it is important to be aware of all the
+software and libraries the project relies on and to list them explicitly and
+under a version control system in such a way that they can easily be
+installed and made available on different systems. The upsides are significant:
+
+Easily install and run on the cluster
+Ease of collaboration
+Better reproducibility
+
+To achieve this, try to always keep in mind the following aspects:
+
+Versions: For each dependency, make sure you have some record of the
+specific version you are using during development. That way, in the future, you
+will be able to reproduce the original environment which you know to be
+compatible. Indeed, the more time passes, the more likely it is that newer
+versions of some dependency have breaking changes. The pip freeze command can create
+such a record for Python dependencies.
+Isolation: Ideally, each of your software projects should be isolated from
+the others. What this means is that updating the environment for project A
+should not update the environment for project B. That way, you can freely
+install and upgrade software and libraries for the former without worrying about
+breaking the latter (which you might not notice until weeks later, the next time
+you work on project B!) Isolation can be achieved using Python Virtual environments and Containers.
+
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+Managing your environments,https://docs.mila.quebec/Userguide.html#managing-your-environments,"Managing your environments
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+Virtual environments,https://docs.mila.quebec/Userguide.html#virtual-environments,"Virtual environments
+A virtual environment in Python is a local, isolated environment in which you
+can install or uninstall Python packages without interfering with the global
+environment (or other virtual environments). It usually lives in a directory
+(location varies depending on whether you use venv, conda or poetry). In order
+to use a virtual environment, you have to activate it. Activating an
+environment essentially sets environment variables in your shell so that:
 
- first time setup of AMD stack
-conda create -n rocm python=3.6
-conda activate rocm
+python points to the right Python version for that environment (different
+virtual environments can use different versions of Python!)
+python looks for packages in the virtual environment
+pip install installs packages into the virtual environment
+Any shell commands installed via pip install are made available
 
-pip install tensorflow-rocm
-pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl",1239,"[-0.010523217730224133, -0.006171110551804304, 0.008128670044243336, -0.021032223477959633, -0.004956073127686977, 0.013727789744734764, -0.027285048738121986, 0.0019735475070774555, -0.028947729617357254, -0.03700534626841545, 0.016541561111807823, 0.019483229145407677, -0.02519603632390499, -0.011091656051576138, -0.01479361206293106, 0.009485817514359951, -0.00388669827952981, 0.004092757124453783, 0.014040431007742882, -0.025906585156917572, -0.006405591499060392, -0.005876232869923115, -0.018460040912032127, -0.007588653825223446, -0.0024744838010519743, 0.017294742166996002, 0.023035969585180283, -0.01359989121556282, 0.005005811341106892, 0.014409915544092655, 0.003398196306079626, 0.015603636391460896, -0.02687292918562889, -0.027597688138484955, -0.014210962690412998, 0.030212506651878357, 0.030098818242549896, -0.0009459171560592949, 0.02378915064036846, 0.004476453177630901, 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-41,"Special nodes and outliers
+To run experiments within a virtual environment, you can simply activate it
+in the script given to sbatch.
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+Pip/Virtualenv,https://docs.mila.quebec/Userguide.html#pip-virtualenv,"Pip/Virtualenv
+Pip is the preferred package manager for Python and each cluster provides
+several Python versions through the associated module which comes with pip. In
+order to install new packages, you will first have to create a personal space
+for them to be stored.  The preferred solution (as it is the preferred solution
+on Digital Research Alliance of Canada clusters) is to use virtual
+environments.
+First, load the Python module you want to use:
+module load python/3.8
+Then, create a virtual environment in your home directory:
+python -m venv $HOME/<env>
+Where <env> is the name of your environment. Finally, activate the environment:
+source $HOME/<env>/bin/activate
+You can now install any Python package you wish using the pip command, e.g.
+pytorch:
+pip install torch torchvision
+Or Tensorflow:
+pip install tensorflow-gpu
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+Conda,https://docs.mila.quebec/Userguide.html#conda,"Conda
+Another solution for Python is to use miniconda or anaconda which are also available through the module
+command: (the use of Conda is not recommended for Digital Research Alliance of
+Canada clusters due to the availability of custom-built packages for pip)
+module load miniconda/3
+=== Module miniconda/3 loaded ===]
+o enable conda environment functions, first use:
+To create an environment (see here
+for details) using a specific Python version, you may write:
+conda create -n <env> python=3.9
+Where <env> is the name of your environment. You can now activate it by doing:
+conda activate <env>
+You are now ready to install any Python package you want in this environment.
+For instance, to install PyTorch, you can find the Conda command of any version
+you want on pytorch’s website, e.g:
+conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
+If you make a lot of environments and install/uninstall a lot of packages, it
+can be good to periodically clean up Conda’s cache:
+conda clean --all
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+Using Modules,https://docs.mila.quebec/Userguide.html#using-modules,"Using Modules
+A lot of software, such as Python and Conda, is already compiled and available on
+the cluster through the module command and its sub-commands. In particular,
+if you wish to use Python 3.7 you can simply do:
+module load python/3.7
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+The module command,https://docs.mila.quebec/Userguide.html#the-module-command,"The module command
+For a list of available modules, simply use:
+module avail
+-------------------------------------------------------------------------------------------------------------- Global Aliases ---------------------------------------------------------------------------------------------------------------
+  cuda/10.0 -> cudatoolkit/10.0    cuda/9.2      -> cudatoolkit/9.2                                 pytorch/1.4.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1    tensorflow/1.15 -> python/3.7/tensorflow/1.15
+  cuda/10.1 -> cudatoolkit/10.1    mujoco-py     -> python/3.7/mujoco-py/2.0                        pytorch/1.5.0       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0    tensorflow/2.2  -> python/3.7/tensorflow/2.2
+  cuda/10.2 -> cudatoolkit/10.2    mujoco-py/2.0 -> python/3.7/mujoco-py/2.0                        pytorch/1.5.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1
+  cuda/11.0 -> cudatoolkit/11.0    pytorch       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1    tensorflow          -> python/3.7/tensorflow/2.2
+  cuda/9.0  -> cudatoolkit/9.0     pytorch/1.4.0 -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.0    tensorflow-cpu/1.15 -> python/3.7/tensorflow/1.15
 
-DGX A100
-DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
-GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
-interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
-In order to run jobs on a DGX A100, add the flags below to your Slurm
-commands:
---gres=gpu:a100:<number> --reservation=DGXA100
+-------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Core ---------------------------------------------------------------------------------------------------
+  Mila       (S,L)    anaconda/3 (D)    go/1.13.5        miniconda/2        mujoco/1.50        python/2.7    python/3.6        python/3.8           singularity/3.0.3    singularity/3.2.1    singularity/3.5.3 (D)
+  anaconda/2          go/1.12.4         go/1.14   (D)    miniconda/3 (D)    mujoco/2.0  (D)    python/3.5    python/3.7 (D)    singularity/2.6.1    singularity/3.1.1    singularity/3.4.2
 
+------------------------------------------------------------------------------------------------ /cvmfs/config.mila.quebec/modules/Compiler ---------------------------------------------------------------------------------------",625,"[-0.00842873752117157, -0.016716407611966133, 0.00813249684870243, -0.02381206303834915, -0.036733776330947876, 0.020144328474998474, -0.016448380425572395, -0.030498627573251724, -0.013189739547669888, -0.012308072298765182, 0.05315394327044487, 0.0004765410558320582, -0.0016310840146616101, 0.0007445678347721696, -0.025067558512091637, -0.0013154472690075636, 0.0075682299211621284, -0.009155230596661568, 0.014007925987243652, -0.013090992346405983, 0.007688136771321297, 0.005924802739173174, -0.017943687736988068, -0.016575340181589127, -0.031316813081502914, 0.031175747513771057, 0.003872281638905406, -0.020567528903484344, -0.0027349311858415604, 0.0015561423497274518, 0.017732087522745132, 0.019424889236688614, -0.019904514774680138, 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+The module command,https://docs.mila.quebec/Userguide.html#the-module-command,"----------
+  python/3.7/mujoco-py/2.0
 
+-------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Cuda ---------------------------------------------------------------------------------------------------
+  cuda/10.0/cudnn/7.3        cuda/10.0/nccl/2.4         cuda/10.1/nccl/2.4     cuda/11.0/nccl/2.7        cuda/9.0/nccl/2.4     cudatoolkit/9.0     cudatoolkit/10.1        cudnn/7.6/cuda/10.0/tensorrt/7.0
+  cuda/10.0/cudnn/7.5        cuda/10.1/cudnn/7.5        cuda/10.2/cudnn/7.6    cuda/9.0/cudnn/7.3        cuda/9.2/cudnn/7.6    cudatoolkit/9.2     cudatoolkit/10.2        cudnn/7.6/cuda/10.1/tensorrt/7.0
+  cuda/10.0/cudnn/7.6 (D)    cuda/10.1/cudnn/7.6 (D)    cuda/10.2/nccl/2.7     cuda/9.0/cudnn/7.5 (D)    cuda/9.2/nccl/2.4     cudatoolkit/10.0    cudatoolkit/11.0 (D)    cudnn/7.6/cuda/9.0/tensorrt/7.0
 
+------------------------------------------------------------------------------------------------ /cvmfs/config.mila.quebec/modules/Pytorch --------------------------------------------------------------------------------------------------
+  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.4.1    python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.1 (D)    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0
+  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.0    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1        python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1 (D)
 
-MIG
-MIG (Multi-Instance GPU)
-is an NVIDIA technology allowing certain GPUs to be
-partitioned into multiple instances, each of which has a roughly proportional
-amount of compute resources, device memory and bandwidth to that memory.
-NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
-instances. Although this can theoretically be done dynamically, the SLURM job
-scheduler does not support doing so in practice as it does not model
-reconfigurable resources very well. Therefore, the A100s must currently be
-statically partitioned into the required number of instances of every size
-expected to be used.
-The cn-g series of nodes include A100-80GB GPUs. One third have been
-configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
-have been configured in MIG mode, and offer the following profiles:
+----------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Tensorflow ------------------------------------------------------------------------------------------------
+  python/3.7/tensorflow/1.15    python/3.7/tensorflow/2.0    python/3.7/tensorflow/2.2 (D)
+Modules can be loaded using the load command:
+module load <module>
+To search for a module or a software, use the command spider:
+module spider search_term
+E.g.: by default, python2 will refer to the os-shipped installation of python2.7 and python3 to python3.6.
+If you want to use python3.7 you can type:
+module load python3.7
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+Available Software,https://docs.mila.quebec/Userguide.html#available-software,"Available Software
+Modules are divided in 5 main sections:
 
 
 
 
 
 
+Section
+Description
 
 
 
-Name
-GPU
-Cluster-wide
+Core
+Base interpreter and software (Python, go, etc…)
 
-Model
-Memory
-Compute
-#
+Compiler
+Interpreter-dependent software (see the note below)
 
+Cuda
+Toolkits, cudnn and related libraries
 
+Pytorch/Tensorflow
+Pytorch/TF built with a specific Cuda/Cudnn
+version for Mila’s GPUs (see the related paragraph)
 
-a100l.1g.10gb
-a100l.1
-A100
-10GB 
-(1/8th)
-1/7th
-of full
-72
 
-a100l.2g.20gb
-a100l.2
-A100
-20GB 
-(2/8th)
-2/7th
-of full
-108
 
-a100l.3g.40gb
-a100l.3
-A100
-40GB 
-(4/8th)
-3/7th
-of full
-72
 
+Note
+Modules which are nested (../../..) usually depend on other software/module
+loaded alongside the main module.  No need to load the dependent software,
+the complex naming scheme allows an automatic detection of the dependent
+module(s):
+i.e.: Loading cudnn/7.6/cuda/9.0/tensorrt/7.0 will load cudnn/7.6 and
+cuda/9.0 alongside
+python/3.X is a particular dependency which can be served through
+python/3.X or anaconda/3 and is not automatically loaded to let the
+user pick his favorite flavor.
+
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+Default package location,https://docs.mila.quebec/Userguide.html#default-package-location,"Default package location
+Python by default uses the user site package first and packages provided by
+module last to not interfere with your installation.  If you want to skip
+packages installed in your site-packages folder (in your /home directory), you
+have to start Python with the -s flag.
+To check which package is loaded at import, you can print package.__file__
+to get the full path of the package.
+Example:
+module load pytorch/1.5.0
+python -c 'import torch;print(torch.__file__)'
+home/mila/my_home/.local/lib/python3.7/site-packages/torch/__init__.py   <== package from your own site-package
+Now with the -s flag:
+module load pytorch/1.5.0
+python -s -c 'import torch;print(torch.__file__)'
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+On using containers,https://docs.mila.quebec/Userguide.html#on-using-containers,"On using containers
+Another option for creating portable code is Using containers on clusters.
+Containers are a popular approach at deploying applications by packaging a lot
+of the required dependencies together. The most popular tool for this is
+Docker, but Docker cannot be used on the Mila
+cluster (nor the other clusters from Digital Research Alliance of Canada).
+One popular mechanism for containerisation on a computational cluster is called
+Singularity.
+This is the recommended approach for running containers on the
+Mila cluster. See section Singularity for more details.
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+What is Singularity?,https://docs.mila.quebec/Userguide.html#what-is-singularity,"What is Singularity?
+Running Docker on SLURM is a security problem (e.g. running as root, being able
+to mount any directory).  The alternative is to use Singularity, which is a
+popular solution in the world of HPC.
+There is a good level of compatibility between Docker and Singularity,
+and we can find many exaggerated claims about able to convert containers
+from Docker to Singularity without any friction.
+Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
+and they can indeed be used without friction, but things get messy when
+we try to convert our own Docker build files to Singularity recipes.
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+Links to official documentation,https://docs.mila.quebec/Userguide.html#links-to-official-documentation,"Links to official documentation
 
+official Singularity user guide (this is the one you
+will use most often)
+official Singularity admin guide
 
-And can be requested using a SLURM flag such as --gres=gpu:a100l.1
-The partitioning may be revised as needs and SLURM capabilities evolve. Other
-MIG profiles exist and could be introduced.
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+Overview of the steps used in practice,https://docs.mila.quebec/Userguide.html#overview-of-the-steps-used-in-practice,"Overview of the steps used in practice
+Most often, the process to create and use a Singularity container is:
 
-Warning
-MIG has a number of important limitations,
-most notably that a GPU in MIG mode does not support graphics APIs
-(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
-limit every MIG job to exactly one MIG slice and no more. Thus,
---gres=gpu:a100l.3 will work (and request a size-3 slice of an
-a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
-three size-1 slices) will not.
+on your Linux computer (at home or work)
 
+select a Docker image from DockerHub (e.g. pytorch/pytorch)
+make a recipe file for Singularity that starts with that DockerHub image
+build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
+test your singularity container before send it over to the cluster
+rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images
 
 
-AMD
+on the login node for that cluster
 
-Warning
-As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
-more samples. You can join the amd slack channels to get the latest
-information
+queue your jobs with sbatch ...
+(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
+and will then launch Singularity with that image)
+do something else while you wait for them to finish
+queue more jobs with the same my-pytorch-image.sif,
+reusing it many times over
 
-Mila has a few node equipped with MI50 GPUs.
-srun --gres=gpu -c 8 --reservation=AMD --pty bash
 
- first time setup of AMD stack
-conda create -n rocm python=3.6
-conda activate rocm
 
-pip install tensorflow-rocm
-pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl",746,"[-0.005199730861932039, -0.008026232942938805, -0.0009968300582841039, -0.016671568155288696, -0.004510609898716211, 0.01237469632178545, -0.019310127943754196, 0.005678799003362656, -0.014431004412472248, -0.03584903106093407, 0.02567804791033268, 0.03198700770735741, -0.007093892432749271, -0.015949280932545662, -0.009146515280008316, 0.012971688993275166, -0.002848612377420068, 0.01690741814672947, 0.01869102567434311, -0.011387079022824764, -0.014666853472590446, 0.014991145581007004, -0.009522398933768272, -0.005266063380986452, 0.002233194187283516, 0.01112911943346262, 0.015094329603016376, -0.016406238079071045, 0.00034433018299750984, 0.017968738451600075, -0.0012529472587630153, 0.013465497642755508, -0.02267097495496273, -0.005476116668432951, -0.013126464560627937, 0.022862602025270462, 0.026282411068677902, -0.00813678652048111, 0.009198106825351715, -0.014578409492969513, 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-42,"DGX A100
-DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
-GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
-interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
-In order to run jobs on a DGX A100, add the flags below to your Slurm
-commands:
---gres=gpu:a100:<number> --reservation=DGXA100",108,"[0.0011326802195981145, -0.013014649972319603, -0.0020505122374743223, -0.01509781926870346, -0.00522167282178998, 0.005269798915833235, -0.02264672890305519, 0.0032038178760558367, 0.007218902464956045, -0.021986715495586395, 0.017847876995801926, 0.031433165073394775, 0.005713245831429958, -0.019195405766367912, -0.0039016450755298138, 0.023196740075945854, -0.007191401906311512, 0.01170837227255106, 0.009308947250247002, -0.022756731137633324, -0.009879584424197674, 0.00685108220204711, -0.010491471737623215, 0.0004834086576011032, -0.0012933866819366813, 0.006672328803688288, 0.022137967869639397, -0.012595266103744507, -0.00349944899789989, 0.01766912452876568, -0.00011429929145378992, 0.014135299250483513, -0.02371925115585327, -0.0026795880403369665, -0.0031281912233680487, 0.019649164751172066, 0.001854570466093719, -0.015414075925946236, 0.0031127221882343292, -0.010697726160287857, 0.02657931298017502, 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-43,"MIG
-MIG (Multi-Instance GPU)
-is an NVIDIA technology allowing certain GPUs to be
-partitioned into multiple instances, each of which has a roughly proportional
-amount of compute resources, device memory and bandwidth to that memory.
-NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
-instances. Although this can theoretically be done dynamically, the SLURM job
-scheduler does not support doing so in practice as it does not model
-reconfigurable resources very well. Therefore, the A100s must currently be
-statically partitioned into the required number of instances of every size
-expected to be used.
-The cn-g series of nodes include A100-80GB GPUs. One third have been
-configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
-have been configured in MIG mode, and offer the following profiles:
+In the following sections you will find specific examples or tips to accomplish
+in practice the steps highlighted above.
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+"Nope, not on MacOS",https://docs.mila.quebec/Userguide.html#nope-not-on-macos,"Nope, not on MacOS
+Singularity does not work on MacOS, as of the time of this writing in 2021.
+Docker does not actually run on MacOS, but there Docker silently installs a
+virtual machine running Linux, which makes it a pleasant experience,
+and the user does not need to care about the details of how Docker does it.
+Given its origins in HPC, Singularity does not provide that kind of seamless
+experience on MacOS, even though it’s technically possible to run it
+inside a Linux virtual machine on MacOS.
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+Where to build images,https://docs.mila.quebec/Userguide.html#where-to-build-images,"Where to build images
+Building Singularity images is a rather heavy task, which can take 20 minutes
+if you have a lot of steps in your recipe. This makes it a bad task to run on
+the login nodes of our clusters, especially if it needs to be run regularly.
+On the Mila cluster, we are lucky to have unrestricted internet access on the compute
+nodes, which means that anyone can request an interactive CPU node (no need for GPU)
+and build their images there without problem.
 
+Warning
+Do not build Singularity images from scratch every time your run a
+job in a large batch.  This will be a colossal waste of GPU time as well as
+internet bandwidth.  If you setup your workflow properly (e.g. using bind
+paths for your code and data), you can spend months reusing the same
+Singularity image my-pytorch-image.sif.
 
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+Building the containers,https://docs.mila.quebec/Userguide.html#building-the-containers,"Building the containers
+Building a container is like creating a new environment except that containers
+are much more powerful since they are self-contained systems.  With
+singularity, there are two ways to build containers.
+The first one is by yourself, it’s like when you got a new Linux laptop and you
+don’t really know what you need, if you see that something is missing, you
+install it. Here you can get a vanilla container with Ubuntu called a sandbox,
+you log in and you install each packages by yourself.  This procedure can take
+time but will allow you to understand how things work and what you need. This is
+recommended if you need to figure out how things will be compiled or if you want
+to install packages on the fly. We’ll refer to this procedure as singularity
+sandboxes.
+The second way is more like you know what you want, so you write a list of
+everything you need, you send it to singularity and it will install everything
+for you. Those lists are called singularity recipes.
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+First way: Build and use a sandbox,https://docs.mila.quebec/Userguide.html#first-way-build-and-use-a-sandbox,"First way: Build and use a sandbox
+You might ask yourself: On which machine should I build a container?
+First of all, you need to choose where you’ll build your container. This
+operation requires memory and high cpu usage.
 
+Warning
+Do NOT build containers on any login nodes !
 
 
+(Recommended for beginner) If you need to use apt-get, you should build
+the container on your laptop with sudo privileges. You’ll only need to
+install singularity on your laptop. Windows/Mac users can look there and
+Ubuntu/Debian users can use directly:
 
+sudo apt-get install singularity-container
 
 
+If you can’t install singularity on your laptop and you don’t need
+apt-get, you can reserve a cpu node on the Mila cluster to build your
+container.
 
-Name
-GPU
-Cluster-wide
+In this case, in order to avoid too much I/O over the network, you should define
+the singularity cache locally:
 
-Model
-Memory
-Compute
-#
+export SINGULARITY_CACHEDIR=$SLURM_TMPDIR
 
 
+If you can’t install singularity on your laptop and you want to use
+apt-get, you can use singularity-hub to build your containers and read
+Recipe_section.
 
-a100l.1g.10gb
-a100l.1
-A100
-10GB 
-(1/8th)
-1/7th
-of full
-72
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+Download containers from the web,https://docs.mila.quebec/Userguide.html#download-containers-from-the-web,"Download containers from the web
+Hopefully, you may not need to create containers from scratch as many have been
+already built for the most common deep learning software. You can find most of
+them on dockerhub.
+Go on dockerhub and select the container you want to pull.
+For example, if you want to get the latest PyTorch version with GPU support
+(Replace runtime by devel if you need the full Cuda toolkit):
+singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
+Or the latest TensorFlow:
+singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
+Currently the pulled image pytorch.simg or tensorflow.simg is read-only
+meaning that you won’t be able to install anything on it.  Starting now, PyTorch
+will be taken as example. If you use TensorFlow, simply replace every
+pytorch occurrences by tensorflow.
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+How to add or install stuff in a container,https://docs.mila.quebec/Userguide.html#how-to-add-or-install-stuff-in-a-container,"How to add or install stuff in a container
+The first step is to transform your read only container
+pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
+allow you to add packages.
 
-a100l.2g.20gb
-a100l.2
-A100
-20GB 
-(2/8th)
-2/7th
-of full
-108
+Warning
+Depending on the version of singularity you are using, singularity
+will build a container with the extension .simg or .sif. If you’re using
+.sif files, replace every occurences of .simg by .sif.
 
-a100l.3g.40gb
-a100l.3
-A100
-40GB 
-(4/8th)
-3/7th
-of full
-72
 
+Tip
+If you want to use apt-get you have to put sudo ahead of the
+following commands
 
-
-And can be requested using a SLURM flag such as --gres=gpu:a100l.1
-The partitioning may be revised as needs and SLURM capabilities evolve. Other
-MIG profiles exist and could be introduced.
-
-Warning
-MIG has a number of important limitations,
-most notably that a GPU in MIG mode does not support graphics APIs
-(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
-limit every MIG job to exactly one MIG slice and no more. Thus,
---gres=gpu:a100l.3 will work (and request a size-3 slice of an
-a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
-three size-1 slices) will not.",487,"[-0.0166629608720541, -0.0032609880436211824, 0.00907926820218563, -0.02819017507135868, -0.004821817856281996, 0.005664842203259468, -0.025340117514133453, 0.012881696224212646, -0.03163281828165054, -0.06411218643188477, 0.017495403066277504, 0.035103682428598404, -0.0012742368271574378, -0.0034179529175162315, 0.0012980460887774825, 0.008444353938102722, 0.004412651062011719, 0.01842661015689373, 0.007590747904032469, 0.0035572811029851437, -0.013220317661762238, 0.009297960437834263, 0.0056295692920684814, -0.003495553508400917, -0.022151438519358635, 0.004663089290261269, 0.008169224485754967, -0.01626790314912796, 0.003142823465168476, 0.02031724341213703, 0.00724507262930274, 0.0069558341056108475, -0.014885202050209045, -0.0039787935093045235, -0.016225576400756836, 0.007470819633454084, 0.037389371544122696, -0.0027054385282099247, -0.0013685920275747776, -0.0064584845677018166, 0.030645174905657768, -0.003493789816275239, 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-44,"AMD
+This command will create a writable image in the folder pytorch.
+singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
+Then you’ll need the following command to log inside the container.
+singularity shell --writable -H $HOME:/home pytorch
+Once you get into the container, you can use pip and install anything you need
+(Or with apt-get if you built the container with sudo).
 
 Warning
-As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
-more samples. You can join the amd slack channels to get the latest
-information
+Singularity mounts your home folder, so if you install things into
+the $HOME of your container, they will be installed in your real
+$HOME!
 
-Mila has a few node equipped with MI50 GPUs.
-srun --gres=gpu -c 8 --reservation=AMD --pty bash
+You should install your stuff in /usr/local instead.
+",275,"[0.007603994105011225, -0.017576109617948532, -0.0010933736339211464, -0.003877284238114953, 0.004647265654057264, -4.456802344066091e-05, -0.016111433506011963, -0.021025625988841057, -0.010355394333600998, -0.004387183114886284, 0.05628735199570656, 0.026090392842888832, -0.016398893669247627, 0.009096046909689903, 0.009664122015237808, -0.007932519540190697, 0.006680016405880451, -0.0016340716974809766, -0.022038578987121582, -0.03512484207749367, -0.01849324256181717, 0.0010069645941257477, 0.004383760970085859, -0.02335268072783947, -0.025228014215826988, 0.00884280912578106, 0.010451214388012886, -0.014290855266153812, 0.0016725707100704312, -0.0017555576050654054, 0.011163019575178623, -0.01171740610152483, -0.02926613762974739, -0.03290729597210884, -0.02139521762728691, 0.012744047679007053, 0.007809322793036699, -0.016439959406852722, 0.0018034676322713494, -0.01760348677635193, 0.012011710554361343, -0.009684654884040356, 0.017699306830763817, -0.023818092420697212, 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+Creating useful directories,https://docs.mila.quebec/Userguide.html#creating-useful-directories,"Creating useful directories
+One of the benefits of containers is that you’ll be able to use them across
+different clusters. However for each cluster the datasets and experiments
+folder location can be different. In order to be invariant to those locations,
+we will create some useful mount points inside the container:
+mkdir /dataset
+mkdir /tmp_log
+mkdir /final_log
+From now, you won’t need to worry anymore when you write your code to specify
+where to pick up your dataset. Your dataset will always be in /dataset
+independently of the cluster you are using.
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+Testing,https://docs.mila.quebec/Userguide.html#testing,"Testing
+If you have some code that you want to test before finalizing your container,
+you have two choices.  You can either log into your container and run Python
+code inside it with:
+singularity shell --nv pytorch
+Or you can execute your command directly with
+singularity exec --nv pytorch Python YOUR_CODE.py
 
- first time setup of AMD stack
-conda create -n rocm python=3.6
-conda activate rocm
+Tip
+—nv allows the container to use gpus. You don’t need this if you
+don’t plan to use a gpu.
 
-pip install tensorflow-rocm
-pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl",143,"[-0.018515771254897118, -0.017366820946335793, -0.010163788683712482, -0.018987135961651802, -0.015186762437224388, 0.014737493358552456, -0.03326062858104706, -0.010988675989210606, -0.019222818315029144, -0.011047596111893654, 0.05299900099635124, 0.008838077075779438, 0.002509645652025938, -0.01900186575949192, -0.020150816068053246, 0.008521378971636295, 0.005623226519674063, 0.003907166421413422, 0.013971527107059956, -0.007873253896832466, -0.010664612986147404, 0.009383091703057289, -0.007725952193140984, 0.0031835490372031927, -0.010583597235381603, 0.006462844088673592, 0.013846321031451225, -0.024437284097075462, 0.0011066008592024446, -0.01764669455587864, 0.007748047821223736, 0.0037653890904039145, -0.014317684806883335, -0.0240248404443264, -0.025085408240556717, -0.003973452374339104, 0.012726831249892712, -0.0191049762070179, 0.016512474045157433, -0.01713113859295845, 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-45,"Data sharing policies
 
-Note
-/network/scratch aims to support
-Access Control Lists (ACLs)
-to allow collaborative work on rapidly changing data, e.g. work in process
-datasets, model checkpoints, etc…
+Warning
+Don’t forget to clear the cache of the packages you installed in
+the containers.
 
-/network/projects aims to offer a collaborative
-space for long-term projects. Data that should be kept for a longer period then
-90 days can be stored in that location but first a request to Mila’s helpdesk has to be made to create the project
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-46,"Monitoring
-Every compute node on the Mila cluster has a Netdata
-monitoring daemon allowing you to get a sense of the state of the node.
-This information is exposed in two ways:
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+Creating a new image from the sandbox,https://docs.mila.quebec/Userguide.html#creating-a-new-image-from-the-sandbox,"Creating a new image from the sandbox
+Once everything you need is installed inside the container, you need to convert
+it back to a read-only singularity image with:
+singularity build pytorch_final.simg pytorch
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+Second way: Use recipes,https://docs.mila.quebec/Userguide.html#second-way-use-recipes,"Second way: Use recipes
+A singularity recipe is a file including specifics about installation software,
+environment variables, files to add, and container metadata.  It is a starting
+point for designing any custom container. Instead of pulling a container and
+installing your packages manually, you can specify in this file the packages
+you want and then build your container from this file.
+Here is a toy example of a singularity recipe installing some stuff:
+################# Header: Define the base system you want to use ################
+# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
+Bootstrap: docker
+# Select the docker image you want to use (Here we choose tensorflow)
+From: tensorflow/tensorflow:latest-gpu-py3
 
-For every node, there is a web interface from Netdata itself at <node>.server.mila.quebec:19999.
-This is accessible only when using the Mila wifi or through SSH tunnelling.
+################# Section: Defining the system #################################
+# Commands in the %post section are executed within the container.
+%post
+        echo ""Installing Tools with apt-get""
+        apt-get update
+        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
+        apt-get clean
+        echo ""Installing things with pip""
+        pip install tqdm
+        echo ""Creating mount points""
+        mkdir /dataset
+        mkdir /tmp_log
+        mkdir /final_log
 
-SSH tunnelling: on your local machine, run
 
-ssh -L 19999:<node>.server.mila.quebec:19999 -p 2222
-login.server.mila.quebec
-or ssh -L 19999:<node>.server.mila.quebec:19999 mila if you have
-already setup your SSH Login,
+# Environment variables that should be sourced at runtime.
+%environment
+        # use bash as default shell
+        SHELL=/bin/bash
+        export SHELL
 
 
-then open http://localhost:19999 in your browser.
+A recipe file contains two parts: the header and sections. In the
+header you specify which base system you want to use, it can be any docker
+or singularity container. In sections, you can list the things you want to
+install in the subsection post or list the environment’s variable you need
+to source at each runtime in the subsection environment. For a more detailed
+description, please look at the singularity documentation.
+In order to build a singularity container from a singularity recipe file, you
+should use:
+sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>
 
+Warning
+You always need to use sudo when you build a container from a
+recipe. As there is no access to sudo on the cluster, a personal computer or
+the use singularity hub is needed to build a container
 
-The Mila dashboard at dashboard.server.mila.quebec
-exposes aggregated statistics with the use of grafana.
-These are collected internally to an instance of prometheus.
+",463,"[0.004745601210743189, -0.008753226138651371, -0.002162682358175516, -0.005890148691833019, 0.015552179887890816, -0.012381612323224545, -0.013932730071246624, -0.01818976365029812, -0.01735612377524376, -0.014076225459575653, 0.055074941366910934, 0.010270178318023682, -0.02807045355439186, 0.018777411431074142, -0.011513805948197842, -0.011629968881607056, 0.016167160123586655, -0.0005739820189774036, 0.0022959280759096146, -0.02200264483690262, -0.007735091261565685, 0.008206576108932495, 0.005090673919767141, -0.01215611957013607, -0.024339571595191956, 0.02204364351928234, 0.023123275488615036, -0.01630382239818573, 0.004260449670255184, 0.018927739933133125, 0.02382025308907032, -0.01746545359492302, -0.023123275488615036, -0.009908023290336132, -0.023246271535754204, 0.021196335554122925, -0.006450464483350515, -0.011834962293505669, -0.011028654873371124, -0.001037779962643981, 0.02768779918551445, -0.0038504628464579582, 0.012812098488211632, -0.018435755744576454, 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+Build recipe on singularity hub,https://docs.mila.quebec/Userguide.html#build-recipe-on-singularity-hub,"Build recipe on singularity hub
+Singularity hub allows users to build containers from recipes directly on
+singularity-hub’s cloud meaning that you don’t need to build containers by
+yourself.  You need to register on singularity-hub and link your
+singularity-hub account to your GitHub account, then:
 
-In both cases, those graphs are not editable by individual users,
-but they provide valuable insight into the state of the whole cluster
-or the individual nodes.
-One of the important uses is to collect data about the health
-of the Mila cluster and to sound the alarm if outages occur
-(e.g. if the nodes crash or if GPUs mysteriously become unavailable for SLURM).
 
-Example with Netdata on cn-c001
-For example, if we have a job running on cn-c001, we can type
-cn-c001.server.mila.quebec:19999 in a browser address bar and the following
-page will appear.
+Create a new github repository.
+Add a collection on singularity-hub and select the github repository your created.
+Clone the github repository on your computer.
+$ git clone <url>
 
 
 
-Example watching the CPU/RAM/GPU usage
-Given that compute nodes are generally shared
-with other users who are also running jobs at the same time and
-consuming resources, this is not generally a good way to profile your code
-in fine details.
-However, it can still be a very useful source of information
-for getting an idea of whether the machine that you requested is being
-used in its full capacity.
-Given how expensive the GPUs are, it generally makes sense to try to
-make sure that this resources is always kept busy.
+Write the singularity recipe and save it as a file named Singularity.
+Git add Singularity, commit and push on the master branch
+$ git add Singularity
+$ git commit
+$ git push origin master
 
 
-CPU
-iowait (pink line): High values means your model is waiting on IO a lot (disk or network).
 
 
 
+At this point, robots from singularity-hub will build the container for you, you
+will be able to download your container from the website or directly with:
+singularity pull shub://<github_username>/<repository_name>
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+"Example: Recipe with OpenAI gym, MuJoCo and Miniworld",https://docs.mila.quebec/Userguide.html#example-recipe-with-openai-gym-mujoco-and-miniworld,"Example: Recipe with OpenAI gym, MuJoCo and Miniworld
+Here is an example on how you can use a singularity recipe to install complex
+environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
+container. In order to use MuJoCo, you’ll need to copy the key stored on the
+Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
 
+# Here we ll build our container upon the pytorch container
+From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
 
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
 
+# Then we put everything we need to install
+%post
+        export PATH=$PATH:/opt/conda/bin
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libhdf5-dev \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        libhdf5-dev \
+        openjdk-8-jdk \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+        pip install h5py
 
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
 
-CPU RAM
-You can see how much CPU RAM is being used by your script in practice,
-considering the amount that you requested (e.g. `sbatch --mem=8G ...`).
-GPU usage is generally more important to monitor than CPU RAM.
-You should not cut it so close to the limit that your experiments randomly fail
-because they run out of RAM. However, you should not request blindly 32GB of RAM
-when you actually require only 8GB.
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym",542,"[-0.00032589846523478627, -0.023443259298801422, 0.008378715254366398, -0.016214564442634583, -0.0015000257408246398, 0.011814489029347897, -0.020171774551272392, -0.011693058535456657, -0.010621611028909683, -0.021928947418928146, 0.04194357618689537, -0.002226823940873146, 0.012850221246480942, -0.0006134033901616931, -0.012535929679870605, 0.010185888968408108, 0.003582204459235072, -0.01585741527378559, -0.005092944484204054, -0.020214634016156197, -0.013243084773421288, 0.006985834334045649, 0.0030946959741413593, -0.03694349154829979, -0.0007196552469395101, 0.01189306192100048, 0.010328749194741249, -0.030343377962708473, -0.012550216168165207, 0.015985989943146706, 0.028829066082835197, -0.017414584755897522, -0.020386064425110817, -0.02680046111345291, -0.010135888122022152, -0.00447864830493927, 0.023828981444239616, -0.016928862780332565, 0.019586050882935524, -0.00734298350289464, 0.016914576292037964, 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+"Example: Recipe with OpenAI gym, MuJoCo and Miniworld",https://docs.mila.quebec/Userguide.html#example-recipe-with-openai-gym-mujoco-and-miniworld,"/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
+        pip install --upgrade minerl
 
+# Export global environment variables
+%environment
+        export SHELL=/bin/sh
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
 
+%runscript
+        exec /bin/sh ""$@""
 
 
+Here is the same recipe but written for TensorFlow:
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
 
+# Here we ll build our container upon the tensorflow container
+From: tensorflow/tensorflow:latest-gpu-py3
 
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
 
+# Then we put everything we need to install
+%post
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-setuptools \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
 
-GPU
-Monitors the GPU usage using an nvidia-smi plugin for Netdata.
-Under the plugin interface, select the GPU number which was allocated to
-you. You can figure this out by running echo $SLURM_JOB_GPUS on the
-allocated node or, if you have the job ID,
-scontrol show -d job YOUR_JOB_ID | grep 'GRES' and checking IDX
-You should make sure you use the GPUs to their fullest capacity.
-Select the biggest batch size if possible to increase GPU memory usage and
-the GPU computational load.
-Spawn multiple experiments if you can fit many on a single GPU.
-Running 10 independent MNIST experiments on a single GPU will probably take
-less than 10x the time to run a single one. This assumes that you have more
-experiments to run, because nothing is gained by gratuitously running experiments.
-You can request a less powerful GPU and leave the more powerful GPUs
-to other researchers who have experiments that can make best use of them.
-Sometimes you really just need a k80 and not a v100.
-
-
-
-
-
-
-
-
-Other users or jobs
-If the node seems unresponsive or slow,
-it may be useful to check what other tasks are
-running at the same time on that node.
-This should not be an issue in general,
-but in practice it is useful to be able to
-inspect this to diagnose certain problems.
-
-
-
-
-
-
-
-
-Example with Mila dashboard",864,"[-0.004716416355222464, 0.006024593953043222, 0.019674992188811302, -0.03332190215587616, -0.0034099831245839596, 0.0067536854185163975, -0.03683828189969063, -0.015181837603449821, -0.01486089825630188, -0.03396378085017204, 0.01957731507718563, 0.010423559695482254, -0.0068060122430324554, 0.006450187880545855, -0.011065438389778137, 0.004231518600136042, 0.021475045010447502, -0.007360679563134909, 0.014902760274708271, -0.026135645806789398, -0.018084248527884483, 0.015335330739617348, -0.00916770938783884, -0.006390884052962065, -0.02319137379527092, 0.006512980442494154, 0.01643768884241581, -0.03558243066072464, -0.018879620358347893, 0.006293206475675106, 0.010611937381327152, -0.013528301380574703, -0.004719905089586973, -0.005044333171099424, -0.008818862028419971, 0.02857757732272148, 0.037982501089572906, -0.01733073778450489, 0.005372249521315098, 0.01890752837061882, 0.018167970702052116, 0.015628362074494362, 0.010995669290423393, 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-47,"Example with Netdata on cn-c001
-For example, if we have a job running on cn-c001, we can type
-cn-c001.server.mila.quebec:19999 in a browser address bar and the following
-page will appear.",54,"[-0.014151128008961678, 0.0012711969902738929, 0.01999969594180584, -0.009886695072054863, -0.018641864880919456, 0.01455423329025507, -0.03425690159201622, -0.025374436751008034, -0.028684144839644432, -0.0012544009368866682, 0.021399956196546555, 0.024158047512173653, 0.005360597278922796, -0.014278423972427845, -0.008182336576282978, -0.004603890236467123, 0.000803117174655199, -0.02352156490087509, 0.01766592636704445, -0.04308279603719711, -0.024285344406962395, 0.0145259452983737, 0.017255747690796852, -0.016689985990524292, -0.03470951318740845, -0.0003082962066400796, -0.0014895459171384573, -0.013535861857235432, -0.010806058533489704, 0.009907911531627178, -0.011081867851316929, -0.004904451314359903, -0.015827199444174767, -0.02004212699830532, 0.002786379074677825, 0.024455072358250618, 0.014377432875335217, -0.00530048506334424, 0.005975863430649042, -0.004667538218200207, 0.032220158725976944, 0.012022447772324085, 0.022503193467855453, 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-48,"Example watching the CPU/RAM/GPU usage
-Given that compute nodes are generally shared
-with other users who are also running jobs at the same time and
-consuming resources, this is not generally a good way to profile your code
-in fine details.
-However, it can still be a very useful source of information
-for getting an idea of whether the machine that you requested is being
-used in its full capacity.
-Given how expensive the GPUs are, it generally makes sense to try to
-make sure that this resources is always kept busy.
-
-
-CPU
-iowait (pink line): High values means your model is waiting on IO a lot (disk or network).
-
-
-
-
-
-
-
-
-CPU RAM
-You can see how much CPU RAM is being used by your script in practice,
-considering the amount that you requested (e.g. `sbatch --mem=8G ...`).
-GPU usage is generally more important to monitor than CPU RAM.
-You should not cut it so close to the limit that your experiments randomly fail
-because they run out of RAM. However, you should not request blindly 32GB of RAM
-when you actually require only 8GB.
-
-
-
-
-
-
-
-
-GPU
-Monitors the GPU usage using an nvidia-smi plugin for Netdata.
-Under the plugin interface, select the GPU number which was allocated to
-you. You can figure this out by running echo $SLURM_JOB_GPUS on the
-allocated node or, if you have the job ID,
-scontrol show -d job YOUR_JOB_ID | grep 'GRES' and checking IDX
-You should make sure you use the GPUs to their fullest capacity.
-Select the biggest batch size if possible to increase GPU memory usage and
-the GPU computational load.
-Spawn multiple experiments if you can fit many on a single GPU.
-Running 10 independent MNIST experiments on a single GPU will probably take
-less than 10x the time to run a single one. This assumes that you have more
-experiments to run, because nothing is gained by gratuitously running experiments.
-You can request a less powerful GPU and leave the more powerful GPUs
-to other researchers who have experiments that can make best use of them.
-Sometimes you really just need a k80 and not a v100.
-
-
-
-
-
-
-
-
-Other users or jobs
-If the node seems unresponsive or slow,
-it may be useful to check what other tasks are
-running at the same time on that node.
-This should not be an issue in general,
-but in practice it is useful to be able to
-inspect this to diagnose certain problems.",517,"[-0.008570767939090729, -0.013921541161835194, 0.014377649873495102, -0.026318179443478584, -0.0031706395093351603, -0.013764966279268265, -0.019183816388249397, 0.0011096387170255184, -0.025215348228812218, -0.04618275538086891, 0.011777147650718689, 0.018516670912504196, -0.006582948844879866, -0.008121466264128685, -0.0046257637441158295, 0.0031263900455087423, 0.030198512598872185, 0.0011955846566706896, 0.008768187835812569, -0.021239710971713066, -0.02119886502623558, -0.002306074369698763, -0.02461628057062626, -0.00846184603869915, -0.01838051900267601, 0.022151928395032883, 0.01644716039299965, -0.03597135469317436, -0.015630248934030533, 0.002033770317211747, 0.006317452527582645, -0.010490510612726212, 0.001478100079111755, -0.0194697342813015, -0.011075964197516441, 0.005582231562584639, 0.02566464990377426, 0.015780014917254448, 0.015711938962340355, 0.0018686860566958785, 0.018503054976463318, 0.016297392547130585, 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-0.026670265942811966, -0.002067629946395755, -0.017059748992323875, -0.02910531498491764, -0.011109008453786373, -0.02798144519329071, -0.02521499991416931, -0.028327250853180885, -0.010338149964809418, 0.002678193151950836, 0.014307711273431778, 0.029451120644807816, -0.004988967906683683, 0.005878697149455547, -0.006141653750091791, -0.02450897917151451, 0.01544598862528801, 0.03135305270552635, 0.009156647138297558, -0.01589265465736389, -0.00862353015691042, -0.00581746082752943, 0.0011679948074743152, -0.0004781844618264586, 0.00031631317688152194, 0.016641899943351746, -0.011137825436890125, -0.0010167048312723637, -0.08109144121408463, 0.020690709352493286, 0.01684362068772316, 0.014228464104235172, 0.0025701289996504784, -0.017189426347613335, -0.00456031272187829, -0.014278894290328026, -0.004686387721449137, 0.025575214996933937, -0.023341886699199677, 0.029436711221933365, -0.014812011271715164, 0.004693591967225075, -0.020546622574329376, -0.018025122582912445, 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-50,"Storage
-
-
-
-
-
-
-
-
-
-
-Path
-Performance
-Usage
-Quota (Space/Files)
-Backup
-Auto-cleanup
-
-
-
-/network/datasets/
-High
-
-Curated raw datasets (read only)
-
-
-
-
-
-
-$HOME or /home/mila/<u>/<username>/
-Low
-
-Personal user space
-Specific libraries, code, binaries
-
-
-100GB/1000K
-Daily
-no
-
-$SCRATCH or /network/scratch/<u>/<username>/
-High
-
-Temporary job results
-Processed datasets
-Optimized for small Files
-
-
-no
-no
-90 days
-
-$SLURM_TMPDIR
-Highest
-
-High speed disk for temporary job
-results
-
-
-4TB/-
-no
-at job end
-
-/network/projects/<groupname>/
-Fair
-
-Shared space to facilitate
-collaboration between researchers
-Long-term project storage
-
-
-200GB/1000K
-Daily
-no
-
-$ARCHIVE or /network/archive/<u>/<username>/
-Low
-
-Long-term personal storage
-
-
-500GB
-no
-no
-
-
-
-
-Note
-The $HOME file system is backed up once a day. For any file
-restoration request, file a request to Mila’s IT support with the path to the file or directory to
-restore, with the required date.
-
-
-Warning
-Currently there is no backup system for any other file systems of
-the Mila cluster. Storage local to personal computers, Google Drive and other
-related solutions should be used to backup important data
-
-
-$HOME
-$HOME is appropriate for codes and libraries which are small and read once,
-as well as the experimental results that would be needed at a later time (e.g.
-the weights of a network referenced in a paper).
-Quotas are enabled on $HOME for both disk capacity (blocks) and number of
-files (inodes). The limits for blocks and inodes are respectively 100GiB and 1
-million per user. The command to check the quota usage from a login node is:
-beegfs-ctl --cfgFile=/etc/beegfs/home.d/beegfs-client.conf --getquota --uid $USER
-
-
-$SCRATCH
-$SCRATCH can be used to store processed datasets, work in progress datasets
-or temporary job results. Its block size is optimized for small files which
-minimizes the performance hit of working on extracted datasets.
-
-Note
-Auto-cleanup: this file system is cleared on a weekly basis,
-files not used for more than 90 days will be deleted.
-
-
-
-$SLURM_TMPDIR
-$SLURM_TMPDIR points to the local disk of the node on which a job is
-running. It should be used to copy the data on the node at the beginning of the
-job and write intermediate checkpoints. This folder is cleared after each job.
-
-
-projects
-projects can be used for collaborative projects. It aims to ease the
-sharing of data between users working on a long-term project.
-Quotas are enabled on projects for both disk capacity (blocks) and number
-of files (inodes). The limits for blocks and inodes are respectively 200GiB and
-1 million per user and per group.
-
-Note
-It is possible to request higher quota limits if the project requires
-it. File a request to Mila’s IT support.
-
-
-
-$ARCHIVE
-$ARCHIVE purpose is to store data other than datasets that has to be kept
-long-term (e.g.  generated samples, logs, data relevant for paper submission).
-$ARCHIVE is only available on the login nodes. Because this file system
-is tuned for large files, it is recommended to archive your directories. For
-example, to archive the results of an experiment in
-$SCRATCH/my_experiment_results/, run the commands below from a login node:
-cd $SCRATCH
-tar cJf $ARCHIVE/my_experiment_results.tar.xz --xattrs my_experiment_results
-Disk capacity quotas are enabled on $ARCHIVE. The soft limit per user is
-500GB, the hard limit is 550GB. The grace time is 7 days. This means that one
-can use more than 500GB for 7 days before the file system enforces quota.
-However, it is not possible to use more than 550GB.
-The command to check the quota usage from a login node is df:
-df -h $ARCHIVE
-
-Note
-There is NO backup of this file system.
-
-
-
-datasets
-datasets contains curated datasets to the benefit of the Mila community.
-To request the addition of a dataset or a preprocessed dataset you think could
-benefit the research of others, you can fill this form. Datasets can also be browsed from the
-web : Mila Datasets
-Datasets in datasets/restricted are restricted and require an explicit
-request to gain access. Please submit a support ticket mentioning the dataset’s
-access group (ex.: scannet_users), your cluster’s username and the
-approbation of the group owner. You can find the dataset’s access group by
-listing the content of /network/datasets/restricted with the ls command.
-Those datasets are mirrored to the Alliance clusters in
-~/projects/rrg-bengioy-ad/data/curated/ if they follow Digital Research
-Alliance of Canada’s good practices on data.
-To list the local datasets on an Alliance cluster, you can execute the
-following command:
-ssh [CLUSTER_LOGIN] -C ""projects/rrg-bengioy-ad/data/curated/list_datasets_cc.sh""",1124,"[0.009135674685239792, -0.004360368009656668, 0.007658775895833969, -0.03811805695295334, -0.034911077469587326, 0.006691758520901203, -0.01972011663019657, -0.01917155459523201, -0.006857030559331179, -0.02903161197900772, 0.029931817203760147, -0.0196638535708189, 0.0037449935916811228, 0.0018724967958405614, -0.014290754683315754, -0.005126948934048414, 0.02019835077226162, -0.019129356369376183, 0.012785724364221096, -0.01597863994538784, 0.006589782424271107, 0.027146808803081512, -0.02474157325923443, -0.020240547135472298, 0.00038680684519931674, -0.006621430162340403, 0.014740857295691967, -0.02111262083053589, 0.014248558320105076, 0.003998176194727421, 0.004093119874596596, -0.020184284076094627, -0.0027955586556345224, 0.0021837004460394382, -0.023574119433760643, 0.0036219186149537563, 0.022125352174043655, -0.014192295260727406, 0.010401587933301926, 0.01132289133965969, 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-51,"$HOME
-$HOME is appropriate for codes and libraries which are small and read once,
-as well as the experimental results that would be needed at a later time (e.g.
-the weights of a network referenced in a paper).
-Quotas are enabled on $HOME for both disk capacity (blocks) and number of
-files (inodes). The limits for blocks and inodes are respectively 100GiB and 1
-million per user. The command to check the quota usage from a login node is:
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-52,"$SCRATCH
-$SCRATCH can be used to store processed datasets, work in progress datasets
-or temporary job results. Its block size is optimized for small files which
-minimizes the performance hit of working on extracted datasets.
-
-Note
-Auto-cleanup: this file system is cleared on a weekly basis,
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-53,"$SLURM_TMPDIR
-$SLURM_TMPDIR points to the local disk of the node on which a job is
-running. It should be used to copy the data on the node at the beginning of the
-job and write intermediate checkpoints. This folder is cleared after each job.",62,"[-0.011303759180009365, -0.003932048100978136, -0.002720428630709648, -0.01489817164838314, -0.0293461624532938, 0.01941404864192009, -0.04839443787932396, -0.017936892807483673, 0.011866485700011253, -0.01329440250992775, 0.02598387561738491, 0.023536017164587975, -0.01941404864192009, 0.021918179467320442, 0.0035170377232134342, -0.013709412887692451, 0.013610935769975185, -0.014328411780297756, -0.00956634245812893, -0.007238063495606184, -0.01623464561998844, 0.002208699705079198, -0.0252523310482502, -0.015137330628931522, -0.007392813451588154, -0.008335379883646965, 0.012794983573257923, -0.014103321358561516, 0.007329506799578667, 0.016206510365009308, -0.018738776445388794, -0.024323832243680954, -0.0054303063079714775, -0.021144431084394455, -0.04026304930448532, -2.0470258732530056e-06, -0.009728126227855682, -0.034720197319984436, -0.000932894239667803, -0.016431599855422974, 0.015840737149119377, -0.01219005323946476, 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-54,"projects
-projects can be used for collaborative projects. It aims to ease the
-sharing of data between users working on a long-term project.
-Quotas are enabled on projects for both disk capacity (blocks) and number
-of files (inodes). The limits for blocks and inodes are respectively 200GiB and
-1 million per user and per group.
-
-Note
-It is possible to request higher quota limits if the project requires
-it. File a request to Mila’s IT support.",102,"[0.003945981152355671, -0.02248307690024376, -0.01136636734008789, -0.04363464564085007, -0.027184966951608658, 0.033454153686761856, -0.053315822035074234, -0.01693512499332428, -0.026241814717650414, -0.02156766504049301, 0.028793873265385628, 0.0018273568712174892, 0.03198394551873207, -0.024591298773884773, -0.017600879073143005, 0.01871046982705593, 0.010513369925320148, -0.025756368413567543, -0.0008547314209863544, -0.015340087935328484, 0.010409345850348473, 0.010326126590371132, -0.02534027211368084, -0.00667834747582674, -0.021470576524734497, -0.0067962417379021645, 0.027725892141461372, -0.028141988441348076, 0.012039056979119778, -0.0023821520153433084, 0.015603615902364254, -0.014369197189807892, -0.003928643651306629, 0.013266541063785553, -0.031234972178936005, -0.018433071672916412, 0.01951492205262184, -0.023551058024168015, 0.004372479859739542, 0.002503513591364026, 0.018918517976999283, 0.0003309267049189657, 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-55,"$ARCHIVE
-$ARCHIVE purpose is to store data other than datasets that has to be kept
-long-term (e.g.  generated samples, logs, data relevant for paper submission).
-$ARCHIVE is only available on the login nodes. Because this file system
-is tuned for large files, it is recommended to archive your directories. For
-example, to archive the results of an experiment in
-$SCRATCH/my_experiment_results/, run the commands below from a login node:
-cd $SCRATCH
-tar cJf $ARCHIVE/my_experiment_results.tar.xz --xattrs my_experiment_results
-Disk capacity quotas are enabled on $ARCHIVE. The soft limit per user is
-500GB, the hard limit is 550GB. The grace time is 7 days. This means that one
-can use more than 500GB for 7 days before the file system enforces quota.
-However, it is not possible to use more than 550GB.
-The command to check the quota usage from a login node is df:
-df -h $ARCHIVE
-
-Note
-There is NO backup of this file system.",231,"[0.011372470296919346, -0.011699385941028595, 0.008214610628783703, -0.04866165295243263, -0.027113081887364388, 0.005919249262660742, -0.02843465469777584, -0.00925795640796423, -0.0006812179344706237, -0.029714491218328476, 0.02031046710908413, -0.012054122984409332, -0.0015511076198890805, -0.006642635446041822, -0.012346260249614716, -0.0020953863859176636, 0.013139203190803528, -0.0033700072672218084, -0.003575198585167527, -0.0222163125872612, 0.011476805433630943, -0.0042220731265842915, -0.034750375896692276, -0.0028605067636817694, -0.011998478323221207, 0.004510732367634773, 0.0008581519941799343, -0.020213088020682335, 0.016164906322956085, 0.00026192329823970795, -0.00033908741897903383, -0.015371963381767273, 0.013083557598292828, -0.010934265330433846, -0.033609651029109955, 0.007449490018188953, -0.0067087141796946526, -0.02160421572625637, 0.026695745065808296, -0.007331244181841612, 0.004319452214986086, 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-56,"datasets
-datasets contains curated datasets to the benefit of the Mila community.
-To request the addition of a dataset or a preprocessed dataset you think could
-benefit the research of others, you can fill this form. Datasets can also be browsed from the
-web : Mila Datasets
-Datasets in datasets/restricted are restricted and require an explicit
-request to gain access. Please submit a support ticket mentioning the dataset’s
-access group (ex.: scannet_users), your cluster’s username and the
-approbation of the group owner. You can find the dataset’s access group by
-listing the content of /network/datasets/restricted with the ls command.
-Those datasets are mirrored to the Alliance clusters in
-~/projects/rrg-bengioy-ad/data/curated/ if they follow Digital Research
-Alliance of Canada’s good practices on data.
-To list the local datasets on an Alliance cluster, you can execute the
-following command:
-ssh [CLUSTER_LOGIN] -C ""projects/rrg-bengioy-ad/data/curated/list_datasets_cc.sh""",227,"[-0.018440071493387222, -0.007025305647403002, 0.013211769051849842, -0.04714873060584068, -0.020349161699414253, 0.0313553549349308, -0.0039230347611010075, -0.006981917191296816, -0.013197306543588638, -0.04321485012769699, 0.02124585583806038, -0.004722104407846928, 3.135490260319784e-05, -0.023429738357663155, -0.0223161019384861, 0.003660896560177207, 0.02314048260450363, -0.019192136824131012, 0.019929740577936172, -0.004906505346298218, 0.001793387345969677, 0.00522830244153738, -0.010630158707499504, -0.026278909295797348, -0.015547511167824268, -0.0034240682143718004, 0.0055030956864356995, -0.01653098128736019, 0.0016008513048291206, 0.0053693149238824844, 0.014556809328496456, -0.00453408807516098, -0.010904951952397823, -0.005485017318278551, -0.014057843014597893, 0.00870660599321127, 0.022894615307450294, 0.00021400426339823753, -0.011888423003256321, -0.0027171988040208817, 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-57,"this is the favored method since the bandwidth can
-be limited to prevent impacting the usage of the cluster: rsync
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-58,"Quick Start
-Users first need login access to the cluster. It is
-recommended to install milatools which will help in the set up of the
-ssh configuration needed to securely and easily connect to the
-cluster.
-
-mila code
-milatools also makes it easy to run and debug code on the Mila cluster. Using
-the mila code command will allow you to use VSCode on the server. Simply run:
-mila code path/on/cluster
-
-
-The details of the command can be found on the github page of the package. Note that you need to
-first setup your ssh configuration using mila init before the mila code
-command can be used. The initialisation of the ssh configuration is explained
-here and on the github page of the package.",162,"[-0.001446299604140222, 0.00430583069100976, 0.0038776842411607504, -0.056974805891513824, -0.03522635996341705, 0.014884180389344692, -0.030408840626478195, -0.02921142242848873, 0.0008188735810108483, -0.018740979954600334, 0.021609213203191757, -0.019896626472473145, -0.01218302920460701, -0.016443608328700066, -0.02458883449435234, -0.008374962024390697, 0.014153199270367622, -0.014605711214244366, 0.027791230008006096, -0.0066902232356369495, -0.02687228098511696, 0.01485633384436369, 0.009279987774789333, -0.031773339956998825, -0.0023704692721366882, -0.005301358178257942, 0.0069164796732366085, -0.011319774203002453, -0.00992046669125557, 0.004901058506220579, 0.010630562901496887, -0.015942363068461418, -0.009579341858625412, -0.010428672656416893, -0.010804606601595879, 0.013192480430006981, 0.032831523567438126, -0.02280663140118122, -0.028417786583304405, -0.0053814179264009, 0.018532127141952515, 0.009328719228506088, 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-59,"mila code
-milatools also makes it easy to run and debug code on the Mila cluster. Using
-the mila code command will allow you to use VSCode on the server. Simply run:
-mila code path/on/cluster
-
-
-The details of the command can be found on the github page of the package. Note that you need to
-first setup your ssh configuration using mila init before the mila code
-command can be used. The initialisation of the ssh configuration is explained
-here and on the github page of the package.",116,"[-0.0061481851153075695, 0.004416301846504211, -0.0006918872823007405, -0.04422536492347717, -0.019688047468662262, 0.02736375294625759, -0.030342591926455498, -0.030785953626036644, -0.0017786439275369048, -0.02139221876859665, 0.02680955082178116, -0.02229279838502407, -0.001258213073015213, -0.023747580125927925, -0.018039293587207794, -0.01853807643055916, 0.010903935879468918, -0.010897008702158928, 0.02964983880519867, -0.015337556600570679, -0.015074309892952442, 0.007592575158923864, 0.00857628509402275, -0.02992693893611431, -0.021558480337262154, 0.003250744426622987, 0.008728690445423126, -0.01748509146273136, -0.004405910614877939, 0.006671213544905186, 0.014464687556028366, -0.03818455711007118, -0.012635818682610989, -0.011222601868212223, -0.00989251583814621, -0.004755750764161348, 0.02680955082178116, -0.017734482884407043, -0.02647702768445015, 0.0005446771974675357, 0.025036102160811424, 0.01197077613323927, 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-60,"Logging in to the cluster
-To access the Mila Cluster clusters, you will need a Mila account. Please contact
-Mila systems administrators if you don’t have it already. Our IT support service
-is available here: https://it-support.mila.quebec/
-You will also need to complete and return an IT Onboarding Training to get
-access to the cluster.  Please refer to the Mila Intranet for more
-informations:
-https://sites.google.com/mila.quebec/mila-intranet/it-infrastructure/it-onboarding-training
-IMPORTANT : Your access to the Cluster is granted based on your status at
-Mila (for students, your status is the same as your main supervisor’ status),
-and on the duration of your stay, set during the creation of your account. The
-following have access to the cluster : Current Students of Core Professors -
-Core Professors - Staff
-
-SSH Login
-You can access the Mila cluster via ssh:
-# Generic login, will send you to one of the 4 login nodes to spread the load
-ssh <user>@login.server.mila.quebec -p 2222
-
-# To connect to a specific login node, X in [1, 2, 3, 4]
-ssh <user>@login-X.login.server.mila.quebec -p 2222
-Four login nodes are available and accessible behind a load balancer. At each
-connection, you will be redirected to the least loaded login-node.
-The ECDSA, RSA and ED25519 fingerprints for Mila’s login nodes are:
-SHA256:baEGIa311fhnxBWsIZJ/zYhq2WfCttwyHRKzAb8zlp8 (ECDSA)
-SHA256:Xr0/JqV/+5DNguPfiN5hb8rSG+nBAcfVCJoSyrR0W0o (RSA)
-SHA256:gfXZzaPiaYHcrPqzHvBi6v+BWRS/lXOS/zAjOKeoBJg (ED25519)
-
-
-
-Important
-Login nodes are merely entry points to the cluster. They give you access
-to the compute nodes and to the filesystem, but they are not meant to run
-anything heavy. Do not run compute-heavy programs on these nodes,
-because in doing so you could bring them down, impeding cluster access for
-everyone.
-This means no training or experiments, no compiling programs, no Python
-scripts, but also no zip of a large folder or anything that demands a
-sustained amount of computation.
-Rule of thumb: never run a program that takes more than a few seconds on
-a login node.
-
-Note
-In a similar vein, you should not run VSCode remote SSH instances directly
-on login nodes, because even though they are typically not very
-computationally expensive, when many people do it, they add up! See
-Visual Studio Code for specific instructions.
-
-
-
-
-mila init
-To make it easier to set up a productive environment, Mila publishes the
-milatools package, which defines a mila init command which will
-automatically perform some of the below steps for you. You can install it with
-pip and use it, provided your Python version is at least 3.8:
-$ pip install milatools
-$ mila init
-
-
-
-
-SSH Config
-The login nodes support the following authentication mechanisms:
-publickey,keyboard-interactive.  If you would like to set an entry in your
-.ssh/config file, please use the following recommendation:
-Host mila
-    User YOUR-USERNAME
-    Hostname login.server.mila.quebec
-    PreferredAuthentications publickey,keyboard-interactive
-    Port 2222
-    ServerAliveInterval 120
-    ServerAliveCountMax 5
-
-
-Then you can simply write ssh mila to connect to a login node. You will also
-be able to use mila with scp, rsync and other such programs.
-
-Tip
-You can run commands on the login node with ssh directly, for example
-ssh mila squeue -u '$USER' (remember to put single quotes around any
-$VARIABLE you want to evaluate on the remote side, otherwise it will be
-evaluated locally before ssh is even executed).
-
-
-
-Passwordless login
-To save you some repetitive typing it is highly recommended to set up public
-key authentication, which means you won’t have to enter your password every time
-you connect to the cluster.
-# ON YOUR LOCAL MACHINE
-# You might already have done this in the past, but if you haven't:
-ssh-keygen  # Press ENTER 3x
-
-# Copy your public key over to the cluster
-# You will need to enter your password
-ssh-copy-id mila
-
-
-
-
-Connecting to compute nodes
-If (and only if) you have a job running on compute node “cnode”, you are
-allowed to SSH to it directly, if for some reason you need a second terminal.
-That session will be automatically ended when your job is relinquished.
-First, however, you need to have
-password-less ssh either with a key present in your home or with an
-ssh-agent. To generate a key pair on the login node:
-# ON A LOGIN NODE
-ssh-keygen  # Press ENTER 3x
-cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
-chmod 600 ~/.ssh/authorized_keys
-chmod 700 ~/.ssh
-
-
-Then from the login node you can write ssh <node>. From your local
-machine, you can use ssh -J mila USERNAME@<node> (-J represents a “jump”
-through the login node, necessary because the compute nodes are behind a
-firewall).
-If you wish, you may also add the following wildcard rule in your .ssh/config:
-Host *.server.mila.quebec !*login.server.mila.quebec
-    HostName %h
-    User YOUR-USERNAME
-    ProxyJump mila
-
-
-This will let you connect to a compute node with ssh <node>.server.mila.quebec.",1269,"[-0.0010284606833010912, -0.00547266798093915, 0.006620875094085932, -0.05209735035896301, -0.02978544495999813, 0.02083078771829605, -0.03331838920712471, -0.03171497583389282, 0.0036994011607021093, -0.015232428908348083, 0.028453797101974487, -0.02743467688560486, -0.0013732624938711524, -0.013486338779330254, -0.015789546072483063, -0.020192140713334084, 0.02047749236226082, -0.02739391289651394, 0.032013919204473495, -0.003407253883779049, -0.014512251131236553, 0.006811110302805901, 0.019499139860272408, -0.017284253612160683, -0.016523312777280807, -0.002420407021418214, 0.01069395337253809, -0.014118191786110401, -0.006641257554292679, 0.0004632744239643216, 0.002792385406792164, -0.01585748791694641, -0.014974252320826054, 0.008356774225831032, -0.01795007847249508, 0.006491786800324917, 0.04734146595001221, -0.028127677738666534, -0.03291074186563492, 0.0018259211210533977, 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-61,"SSH Login
-You can access the Mila cluster via ssh:
-# Generic login, will send you to one of the 4 login nodes to spread the load
-ssh <user>@login.server.mila.quebec -p 2222
-
-# To connect to a specific login node, X in [1, 2, 3, 4]
-ssh <user>@login-X.login.server.mila.quebec -p 2222
-Four login nodes are available and accessible behind a load balancer. At each
-connection, you will be redirected to the least loaded login-node.
-The ECDSA, RSA and ED25519 fingerprints for Mila’s login nodes are:
-SHA256:baEGIa311fhnxBWsIZJ/zYhq2WfCttwyHRKzAb8zlp8 (ECDSA)
-SHA256:Xr0/JqV/+5DNguPfiN5hb8rSG+nBAcfVCJoSyrR0W0o (RSA)
-SHA256:gfXZzaPiaYHcrPqzHvBi6v+BWRS/lXOS/zAjOKeoBJg (ED25519)
-
-
-
-Important
-Login nodes are merely entry points to the cluster. They give you access
-to the compute nodes and to the filesystem, but they are not meant to run
-anything heavy. Do not run compute-heavy programs on these nodes,
-because in doing so you could bring them down, impeding cluster access for
-everyone.
-This means no training or experiments, no compiling programs, no Python
-scripts, but also no zip of a large folder or anything that demands a
-sustained amount of computation.
-Rule of thumb: never run a program that takes more than a few seconds on
-a login node.
-
-Note
-In a similar vein, you should not run VSCode remote SSH instances directly
-on login nodes, because even though they are typically not very
-computationally expensive, when many people do it, they add up! See
-Visual Studio Code for specific instructions.",426,"[-0.0021864166483283043, -0.007259916514158249, 0.010467786341905594, -0.04192517325282097, -0.03849444165825844, 0.015222188085317612, -0.044734593480825424, -0.021921571344137192, 0.00079690245911479, -0.02340732142329216, 0.022583404555916786, -0.003300729440525174, -0.022326774895191193, -0.022488858550786972, -0.020854532718658447, -0.008570077829062939, 0.020841024816036224, -0.024244744330644608, 0.03838638588786125, -0.0064427535980939865, -0.0121493861079216, 0.01059610117226839, 0.013554095290601254, -0.019801000133156776, -0.01339876651763916, 0.0018808249151334167, 0.016491828486323357, -0.013979559764266014, -0.012892261147499084, -0.005980145186185837, 0.020638423040509224, -0.008846968412399292, -0.023744992911815643, 0.005534420255571604, -0.016316240653395653, 0.02267795242369175, 0.04046643525362015, -0.018585385754704475, -0.033199768513441086, -0.011203908361494541, 0.02454189397394657, 0.006520417984575033, 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-62,"mila init
-To make it easier to set up a productive environment, Mila publishes the
-milatools package, which defines a mila init command which will
-automatically perform some of the below steps for you. You can install it with
-pip and use it, provided your Python version is at least 3.8:
-$ pip install milatools
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-63,"SSH Config
-The login nodes support the following authentication mechanisms:
-publickey,keyboard-interactive.  If you would like to set an entry in your
-.ssh/config file, please use the following recommendation:
-Host mila
-    User YOUR-USERNAME
-    Hostname login.server.mila.quebec
-    PreferredAuthentications publickey,keyboard-interactive
-    Port 2222
-    ServerAliveInterval 120
-    ServerAliveCountMax 5
-
-
-Then you can simply write ssh mila to connect to a login node. You will also
-be able to use mila with scp, rsync and other such programs.
-
-Tip
-You can run commands on the login node with ssh directly, for example
-ssh mila squeue -u '$USER' (remember to put single quotes around any
-$VARIABLE you want to evaluate on the remote side, otherwise it will be
-evaluated locally before ssh is even executed).",195,"[-0.007288321852684021, 0.0018439926207065582, 0.019215278327465057, -0.03870024532079697, -0.037432711571455, 0.015790240839123726, -0.03670455515384674, -0.02675306238234043, 0.00016950319695752114, -0.02036145329475403, 0.019889499992132187, -0.0013846800429746509, -0.013046163134276867, -0.0075175566598773, -0.018932105973362923, -0.023611195385456085, 0.017246555536985397, -0.015250863507390022, 0.03327951580286026, -0.008535629138350487, 0.0018979301676154137, 0.01062571257352829, -0.004004868678748608, -0.029369037598371506, -0.026550795882940292, 0.008805317804217339, 0.01731397770345211, -0.028263315558433533, 0.004254330415278673, -0.004877984058111906, 0.014778909273445606, -0.001002902747131884, 0.005363422445952892, 0.0177859328687191, -0.023044850677251816, 0.003103099064901471, 0.031068071722984314, -0.026928359642624855, -0.018689386546611786, 0.0025468673557043076, 0.016370069235563278, 0.007059086579829454, 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-64,"Passwordless login
-To save you some repetitive typing it is highly recommended to set up public
-key authentication, which means you won’t have to enter your password every time
-you connect to the cluster.
-# ON YOUR LOCAL MACHINE
-# You might already have done this in the past, but if you haven't:
-ssh-keygen  # Press ENTER 3x
-
-# Copy your public key over to the cluster
-# You will need to enter your password
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-65,"Connecting to compute nodes
-If (and only if) you have a job running on compute node “cnode”, you are
-allowed to SSH to it directly, if for some reason you need a second terminal.
-That session will be automatically ended when your job is relinquished.
-First, however, you need to have
-password-less ssh either with a key present in your home or with an
-ssh-agent. To generate a key pair on the login node:
-# ON A LOGIN NODE
-ssh-keygen  # Press ENTER 3x
-cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
-chmod 600 ~/.ssh/authorized_keys
-chmod 700 ~/.ssh
-
-
-Then from the login node you can write ssh <node>. From your local
-machine, you can use ssh -J mila USERNAME@<node> (-J represents a “jump”
-through the login node, necessary because the compute nodes are behind a
-firewall).
-If you wish, you may also add the following wildcard rule in your .ssh/config:
-Host *.server.mila.quebec !*login.server.mila.quebec
-    HostName %h
-    User YOUR-USERNAME
-    ProxyJump mila
-
-
-This will let you connect to a compute node with ssh <node>.server.mila.quebec.",270,"[-0.00836926233023405, -0.01904125139117241, 0.011708888225257397, -0.03961765021085739, -0.014328069984912872, 0.009264767169952393, -0.036439619958400726, -0.03072993829846382, -0.011331832967698574, -0.024885594844818115, 0.026191819459199905, 0.003243678482249379, -0.014812854118645191, -0.0008134444942697883, -0.018542999401688576, -0.01676545850932598, 0.010685454122722149, -0.03277680650353432, 0.025060655549168587, -0.01777542568743229, -0.025114521384239197, -0.001231318456120789, 0.009796682745218277, -0.013600893318653107, -0.02180182747542858, 0.0022337110713124275, 0.02795589528977871, -0.018287140876054764, -0.009857281111180782, -0.009823615662753582, 0.0022589601576328278, 0.009089705534279346, -0.01633453741669655, -0.0010554158361628652, -0.03169950842857361, 0.004514554049819708, 0.025181852281093597, -0.022461673244833946, -0.012584192678332329, -0.013977947644889355, 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-66,"Running your code
-
-SLURM commands guide
-
-Basic Usage
-The SLURM documentation
-provides extensive information on the available commands to query the cluster
-status or submit jobs.
-Below are some basic examples of how to use SLURM.
-
-
-Submitting jobs
-
-Batch job
-In order to submit a batch job, you have to create a script containing the main
-command(s) you would like to execute on the allocated resources/nodes.
- 1#!/bin/bash
- 2#SBATCH --job-name=test
- 3#SBATCH --output=job_output.txt
- 4#SBATCH --error=job_error.txt
- 5#SBATCH --ntasks=1
- 6#SBATCH --time=10:00
- 7#SBATCH --mem=100Gb
- 8
- 9module load python/3.5
-10python my_script.py
-
-
-Your job script is then submitted to SLURM with sbatch (ref.)
-sbatch job_script
-sbatch: Submitted batch job 4323674
-The working directory of the job will be the one where your executed sbatch.
-
-Tip
-Slurm directives can be specified on the command line alongside sbatch or
-inside the job script with a line starting with #SBATCH.
-
-
-
-Interactive job
-Workload managers usually run batch jobs to avoid having to watch its
-progression and let the scheduler run it as soon as resources are available. If
-you want to get access to a shell while leveraging cluster resources, you can
-submit an interactive jobs where the main executable is a shell with the
-srun/salloc (srun/salloc) commands
-salloc
-Will start an interactive job on the first node available with the default
-resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
-sbatch with the exception that the environment is not passed.
-
-Tip
-To pass your current environment to an interactive job, add
---preserve-env to srun.
-
-salloc can also be used and is mostly a wrapper around srun if provided
-without more info but it gives more flexibility if for example you want to get
-an allocation on multiple nodes.
-
-
-
-Job submission arguments
-In order to accurately select the resources for your job, several arguments are
-available. The most important ones are:
-
-
-
-
-
-
-Argument
-Description
-
-
-
--n, –ntasks=<number>
-The number of task in your script, usually =1
-
--c, –cpus-per-task=<ncpus>
-The number of cores for each task
-
--t, –time=<time>
-Time requested for your job
-
-–mem=<size[units]>
-Memory requested for all your tasks
-
-–gres=<list>
-Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL
-
-
-
-
-Tip
-Always consider requesting the adequate amount of resources to improve the
-scheduling of your job (small jobs always run first).
-
-
-
-Checking job status
-To display jobs currently in queue, use squeue and to get only your jobs type
-squeue -u $USER
-JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
-133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)
-
-Note
-The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
-at any given time from the given association. If this limit is reached, new submission requests
-will be denied until existing jobs in this association complete.
-
-
-
-Removing a job
-To cancel your job simply use scancel
-scancel 4323674
-
-
-
-Partitioning
-Since we don’t have many GPUs on the cluster, resources must be shared as fairly
-as possible.  The --partition=/-p flag of SLURM allows you to set the
-priority you need for a job.  Each job assigned with a priority can preempt jobs
-with a lower priority: unkillable > main > long. Once preempted, your job is
-killed without notice and is automatically re-queued on the same partition until
-resources are available. (To leverage a different preemption mechanism, see the
-Handling preemption)
-
-
-
-
-
-
-
-
-Flag
-Max Resource Usage
-Max Time
-Note
-
-
-
---partition=unkillable
-6  CPUs, mem=32G,  1 GPU
-2 days
-
-
---partition=unkillable-cpu
-2  CPUs, mem=16G
-2 days
-CPU-only jobs
-
---partition=short-unkillable
-24 CPUs, mem=128G, 4 GPUs
-3 hours (!)
-Large but short jobs
-
---partition=main
-8  CPUs, mem=48G,  2 GPUs
-5 days
-
-
---partition=main-cpu
-8  CPUs, mem=64G
-5 days
-CPU-only jobs
-
---partition=long
-no limit of resources
-7 days
-
-
---partition=long-cpu
-no limit of resources
-7 days
-CPU-only jobs
-
-
-
-
-Warning
-Historically, before the 2022 introduction of CPU-only nodes (e.g. the cn-f
-series), CPU jobs ran side-by-side with the GPU jobs on GPU nodes. To prevent
-them obstructing any GPU job, they were always lowest-priority and preemptible.
-This was implemented by automatically assigning them to one of the now-obsolete
-partitions cpu_jobs, cpu_jobs_low or cpu_jobs_low-grace.
-Do not use these partition names anymore. Prefer the *-cpu partition
-names defined above.
-For backwards-compatibility purposes, the legacy partition names are translated
-to their effective equivalent long-cpu, but they will eventually be removed
-entirely.
-
-
-Note
-As a convenience, should you request the unkillable, main or long
-partition for a CPU-only job, the partition will be translated to its -cpu
-equivalent automatically.
-
-For instance, to request an unkillable job with 1 GPU, 4 CPUs, 10G of RAM and
-12h of computation do:
-sbatch --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable <job.sh>
-You can also make it an interactive job using salloc:
-salloc --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable
-The Mila cluster has many different types of nodes/GPUs. To request a specific
-type of node/GPU, you can add specific feature requirements to your job
-submission command.
-To access those special nodes you need to request them explicitly by adding the
-flag --constraint=<name>.  The full list of nodes in the Mila Cluster can be
-accessed Node profile description.
-Example:
-To request a machine with 2 GPUs using NVLink, you can use
-sbatch -c 4 --gres=gpu:2 --constraint=nvlink
-
-
-
-
-
-
-Feature
-Particularities
-
-
-
-12GB/16GB/24GB/32GB/48GB
-Request a specific amount of GPU memory
-
-volta/turing/ampere
-Request a specific GPU architecture
-
-nvlink
-Machine with GPUs using the NVLink interconnect technology
-
-
-
-
-Information on partitions/nodes
-sinfo (ref.) provides most of the
-information about available nodes and partitions/queues to submit jobs to.
-Partitions are a group of nodes usually sharing similar features. On a
-partition, some job limits can be applied which will override those asked for a
-job (i.e. max time, max CPUs, etc…)
-To display available partitions, simply use
-sinfo
-PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
-batch     up     infinite     2 alloc  node[1,3,5-9]
-batch     up     infinite     6 idle   node[10-15]
-cpu       up     infinite     6 idle   cpu_node[1-15]
-gpu       up     infinite     6 idle   gpu_node[1-15]
-To display available nodes and their status, you can use
-sinfo -N -l
-NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
-node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
-node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
-node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
-...
-And to get statistics on a job running or terminated, use sacct with some of
-the fields you want to display
-sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
-     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
---------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
-my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
-my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
-Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional time formats.
-sacct -u $USER --start=2019-01-01
-scontrol (ref.) can be used to
-provide specific information on a job (currently running or recently terminated)
-scontrol show job 43123
-JobId=43123 JobName=python_script.py
-UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
-Priority=645895 Nice=0 Account=my_username QOS=normal
-JobState=RUNNING Reason=None Dependency=(null)
-Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
-RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
-SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
-AccrueTime=2019-03-26T08:49:18
-StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
-PreemptTime=None SuspendTime=None SecsPreSuspend=0
-LastSchedEval=2019-03-26T08:49:18
-Partition=slurm_partition AllocNode:Sid=login-node-1:14586
-ReqNodeList=(null) ExcNodeList=(null)
-NodeList=node2
-BatchHost=node2
-NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
-TRES=cpu=16,mem=32000M,node=1,billing=3
-Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
-MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
-Features=(null) DelayBoot=00:00:00
-OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
-WorkDir=/home/mila/my_username
-StdErr=/home/mila/my_username/slurm-43123.out
-StdIn=/dev/null
-StdOut=/home/mila/my_username/slurm-43123.out
-Power=
-Or more info on a node and its resources
-scontrol show node node9
-NodeName=node9 Arch=x86_64 CoresPerSocket=4
-CPUAlloc=16 CPUTot=16 CPULoad=1.38
-AvailableFeatures=(null)
-ActiveFeatures=(null)
-Gres=(null)
-NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
-OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
-RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
-State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
-Partitions=slurm_partition
-BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
-CfgTRES=cpu=16,mem=32000M,billing=3
-AllocTRES=cpu=16,mem=32000M
-CapWatts=n/a
-CurrentWatts=0 LowestJoules=0 ConsumedJoules=0
-ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s
-
-
-
-Useful Commands
-
-sallocGet an interactive job and give you a shell. (ssh like) CPU only
-
-salloc --gres=gpu:1 -c 2 --mem=12000Get an interactive job with one GPU, 2 CPUs and 12000 MB RAM
-
-sbatchstart a batch job (same options as salloc)
-
-sattach --pty <jobid>.0Re-attach a dropped interactive job
-
-sinfostatus of all nodes
-
-sinfo -Ogres:27,nodelist,features -tidle,mix,allocList GPU type and FEATURES that you can request
-
-savail(Custom) List available gpu
-
-scancel <jobid>Cancel a job
-
-squeuesummary status of all active jobs
-
-squeue -u $USERsummary status of all YOUR active jobs
-
-squeue -j <jobid>summary status of a specific job
-
-squeue -Ojobid,name,username,partition,state,timeused,nodelist,gres,tresstatus of all jobs including requested resources (see the SLURM squeue doc for all output options)
-
-scontrol show job <jobid>Detailed status of a running job
-
-sacct -j <job_id> -o NodeListGet the node where a finished job ran
-
-sacct -u $USER -S <start_time> -E <stop_time>Find info about old jobs
-
-sacct -oJobID,JobName,User,Partition,Node,StateList of current and recent jobs
-
-
-
-
-Special GPU requirements
-Specific GPU architecture and memory can be easily requested through the
---gres flag by using either
-
---gres=gpu:architecture:number
---gres=gpu:memory:number
---gres=gpu:model:number
-
-Example:
-To request 1 GPU with at least 16GB of memory use
-sbatch -c 4 --gres=gpu:16gb:1
-The full list of GPU and their features can be accessed here.
-
-
-Example script
-Here is a sbatch script that follows good practices on the Mila cluster:
- 1#!/bin/bash
- 2
- 3#SBATCH --partition=unkillable                           # Ask for unkillable job
- 4#SBATCH --cpus-per-task=2                                # Ask for 2 CPUs
- 5#SBATCH --gres=gpu:1                                     # Ask for 1 GPU
- 6#SBATCH --mem=10G                                        # Ask for 10 GB of RAM
- 7#SBATCH --time=3:00:00                                   # The job will run for 3 hours
- 8#SBATCH -o /network/scratch/<u>/<username>/slurm-%j.out  # Write the log on scratch
- 9
-10# 1. Load the required modules
-11module --quiet load anaconda/3
-12
-13# 2. Load your environment
-14conda activate ""<env_name>""
-15
-16# 3. Copy your dataset on the compute node
-17cp /network/datasets/<dataset> $SLURM_TMPDIR
-18
-19# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
-20#    and look for the dataset into $SLURM_TMPDIR
-21python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
-22
-23# 5. Copy whatever you want to save on $SCRATCH
-24cp $SLURM_TMPDIR/<to_save> /network/scratch/<u>/<username>/",3623,"[0.0028114602901041508, -0.0005426594289019704, -0.008118218742311, -0.02147180587053299, -0.02455863170325756, 0.009369268082082272, -0.017269913107156754, -0.0025564911775290966, -0.019078494980931282, -0.0241914764046669, 0.031085843220353127, -0.0032143115531653166, -0.0030817275401204824, 0.01400630734860897, 0.004552050027996302, -0.017691461369395256, 0.021063854917883873, -0.012456094846129417, 0.016413217410445213, -0.012354107573628426, -0.009444058872759342, -0.007186731789261103, -0.007050747983157635, -0.007866648957133293, -0.023416370153427124, 0.028393369168043137, 0.016875561326742172, -0.008981714956462383, -0.0016555999172851443, -0.007574284914880991, -0.011096259579062462, -0.002311720745638013, -0.013068021275103092, -0.0022097330074757338, -0.02602725476026535, 0.0016403017798438668, 0.008471776731312275, -0.008172612637281418, -0.009008911438286304, -0.012748459354043007, 0.024055493995547295, 0.026312820613384247, 0.010851488448679447, -0.009444058872759342, 0.006401426624506712, 0.018738536164164543, -0.0029304458294063807, -0.011986951343715191, -0.008689350448548794, 0.002602385589852929, 0.011307033710181713, 0.02673436887562275, -0.057602640241384506, 0.0020074574276804924, -0.009484853595495224, 0.013605155982077122, -0.022777248173952103, 0.023525157943367958, -0.0009153393912129104, -0.014876602217555046, -0.005714709404855967, -0.016997946426272392, -0.023375574499368668, 0.00250039785169065, 0.01042993925511837, -0.0048716114833951, -0.011259439401328564, -0.006812776904553175, -0.012870844453573227, -0.02303561568260193, 0.02022075653076172, 0.015882881358265877, -0.012585279531776905, 0.016032462939620018, 0.02519775554537773, -0.005884688813239336, -0.02168937958776951, -0.002505497308447957, -0.010695107281208038, 0.006007073912769556, 0.023647543042898178, -0.010164772160351276, -0.007519891485571861, 0.006326635368168354, 0.02764545939862728, 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-67,"SLURM commands guide
-
-Basic Usage
-The SLURM documentation
-provides extensive information on the available commands to query the cluster
-status or submit jobs.
-Below are some basic examples of how to use SLURM.
-
-
-Submitting jobs
-
-Batch job
-In order to submit a batch job, you have to create a script containing the main
-command(s) you would like to execute on the allocated resources/nodes.
- 1#!/bin/bash
- 2#SBATCH --job-name=test
- 3#SBATCH --output=job_output.txt
- 4#SBATCH --error=job_error.txt
- 5#SBATCH --ntasks=1
- 6#SBATCH --time=10:00
- 7#SBATCH --mem=100Gb
- 8
- 9module load python/3.5
-10python my_script.py
-
-
-Your job script is then submitted to SLURM with sbatch (ref.)
-sbatch job_script
-sbatch: Submitted batch job 4323674
-The working directory of the job will be the one where your executed sbatch.
-
-Tip
-Slurm directives can be specified on the command line alongside sbatch or
-inside the job script with a line starting with #SBATCH.
-
-
-
-Interactive job
-Workload managers usually run batch jobs to avoid having to watch its
-progression and let the scheduler run it as soon as resources are available. If
-you want to get access to a shell while leveraging cluster resources, you can
-submit an interactive jobs where the main executable is a shell with the
-srun/salloc (srun/salloc) commands
-salloc
-Will start an interactive job on the first node available with the default
-resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
-sbatch with the exception that the environment is not passed.
-
-Tip
-To pass your current environment to an interactive job, add
---preserve-env to srun.
-
-salloc can also be used and is mostly a wrapper around srun if provided
-without more info but it gives more flexibility if for example you want to get
-an allocation on multiple nodes.
-
-
-
-Job submission arguments
-In order to accurately select the resources for your job, several arguments are
-available. The most important ones are:
-
-
-
-
-
-
-Argument
-Description
-
-
-
--n, –ntasks=<number>
-The number of task in your script, usually =1
-
--c, –cpus-per-task=<ncpus>
-The number of cores for each task
-
--t, –time=<time>
-Time requested for your job
-
-–mem=<size[units]>
-Memory requested for all your tasks
-
-–gres=<list>
-Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL
-
-
-
-
-Tip
-Always consider requesting the adequate amount of resources to improve the
-scheduling of your job (small jobs always run first).
-
-
-
-Checking job status
-To display jobs currently in queue, use squeue and to get only your jobs type
-squeue -u $USER
-JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
-133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)
-
-Note
-The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
-at any given time from the given association. If this limit is reached, new submission requests
-will be denied until existing jobs in this association complete.
-
-
-
-Removing a job
-To cancel your job simply use scancel
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-68,"Basic Usage
-The SLURM documentation
-provides extensive information on the available commands to query the cluster
-status or submit jobs.
-Below are some basic examples of how to use SLURM.",42,"[-0.002638529986143112, -0.001455569756217301, 0.0016406138893216848, -0.023104073479771614, -0.027677305042743683, 0.02014336735010147, -0.020288759842514992, -0.0068598478101193905, -0.009291855618357658, -0.027915218845009804, 0.014076566323637962, -0.0010326119372621179, 5.163059904589318e-05, 0.00805602502077818, -0.014116218313574791, -0.015081090852618217, 0.017513098195195198, -0.014089783653616905, -0.0021907894406467676, 0.007540545426309109, -0.005101928953081369, -0.0030284442473202944, -0.009741248562932014, -0.016468921676278114, -0.017116576433181763, 0.020487021654844284, 0.011618123389780521, -0.017037270590662956, -0.00010274488886352628, -0.002740965224802494, -0.005253929644823074, -0.0014150914503261447, -0.0059247142635285854, -0.0011912211775779724, -0.02005084604024887, 0.004979667719453573, 0.013772564940154552, -0.007659502327442169, -0.019244583323597908, -0.008208026178181171, 0.01564943976700306, 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-69,"Submitting jobs
-
-Batch job
-In order to submit a batch job, you have to create a script containing the main
-command(s) you would like to execute on the allocated resources/nodes.
- 1#!/bin/bash
- 2#SBATCH --job-name=test
- 3#SBATCH --output=job_output.txt
- 4#SBATCH --error=job_error.txt
- 5#SBATCH --ntasks=1
- 6#SBATCH --time=10:00
- 7#SBATCH --mem=100Gb
- 8
- 9module load python/3.5
-10python my_script.py
-
-
-Your job script is then submitted to SLURM with sbatch (ref.)
-sbatch job_script
-sbatch: Submitted batch job 4323674
-The working directory of the job will be the one where your executed sbatch.
-
-Tip
-Slurm directives can be specified on the command line alongside sbatch or
-inside the job script with a line starting with #SBATCH.
-
-
-
-Interactive job
-Workload managers usually run batch jobs to avoid having to watch its
-progression and let the scheduler run it as soon as resources are available. If
-you want to get access to a shell while leveraging cluster resources, you can
-submit an interactive jobs where the main executable is a shell with the
-srun/salloc (srun/salloc) commands
-salloc
-Will start an interactive job on the first node available with the default
-resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
-sbatch with the exception that the environment is not passed.
-
-Tip
-To pass your current environment to an interactive job, add
---preserve-env to srun.
-
-salloc can also be used and is mostly a wrapper around srun if provided
-without more info but it gives more flexibility if for example you want to get
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-70,"Batch job
-In order to submit a batch job, you have to create a script containing the main
-command(s) you would like to execute on the allocated resources/nodes.
- 1#!/bin/bash
- 2#SBATCH --job-name=test
- 3#SBATCH --output=job_output.txt
- 4#SBATCH --error=job_error.txt
- 5#SBATCH --ntasks=1
- 6#SBATCH --time=10:00
- 7#SBATCH --mem=100Gb
- 8
- 9module load python/3.5
-10python my_script.py
-
-
-Your job script is then submitted to SLURM with sbatch (ref.)
-sbatch job_script
-sbatch: Submitted batch job 4323674
-The working directory of the job will be the one where your executed sbatch.
-
-Tip
-Slurm directives can be specified on the command line alongside sbatch or
-inside the job script with a line starting with #SBATCH.",196,"[-0.012385226786136627, -0.014706618152558804, -0.01936281844973564, 0.002692075911909342, -0.020033741369843483, 0.004002051893621683, -0.02681005746126175, -0.01263346802443266, -0.00935265701264143, -0.0012848163023591042, 0.019268888980150223, 0.01619606465101242, -0.0030023774597793818, 0.001722593093290925, -0.003790711285546422, -0.0007443044451065361, 0.013203751295804977, -0.009634444490075111, 0.009976615197956562, -0.007004429120570421, -0.01941649243235588, 0.0049514067359268665, -0.006353634409606457, -0.002101664198562503, -0.02031552791595459, 0.016679128631949425, 0.007675351575016975, -0.008943394757807255, 0.008205380290746689, 0.006866890005767345, -0.02014108933508396, -0.006501237396150827, 0.0044616335071623325, -0.010050416924059391, -0.02847394347190857, 0.008829337544739246, -0.011539864353835583, -0.011942417360842228, 0.011928998865187168, -0.006762897130101919, 0.02175130322575569, 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-71,"Interactive job
-Workload managers usually run batch jobs to avoid having to watch its
-progression and let the scheduler run it as soon as resources are available. If
-you want to get access to a shell while leveraging cluster resources, you can
-submit an interactive jobs where the main executable is a shell with the
-srun/salloc (srun/salloc) commands
-salloc
-Will start an interactive job on the first node available with the default
-resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
-sbatch with the exception that the environment is not passed.
-
-Tip
-To pass your current environment to an interactive job, add
---preserve-env to srun.
-
-salloc can also be used and is mostly a wrapper around srun if provided
-without more info but it gives more flexibility if for example you want to get
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-72,"Job submission arguments
-In order to accurately select the resources for your job, several arguments are
-available. The most important ones are:
-
-
-
-
-
-
-Argument
-Description
-
-
-
--n, –ntasks=<number>
-The number of task in your script, usually =1
-
--c, –cpus-per-task=<ncpus>
-The number of cores for each task
-
--t, –time=<time>
-Time requested for your job
-
-–mem=<size[units]>
-Memory requested for all your tasks
-
-–gres=<list>
-Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL
-
-
-
-
-Tip
-Always consider requesting the adequate amount of resources to improve the
-scheduling of your job (small jobs always run first).",149,"[0.0016386127099394798, -0.023764993995428085, 0.004891991149634123, -0.03493890166282654, -0.01896839030086994, -0.0006340681575238705, -0.021911760792136192, -0.004449123051017523, -0.018954763188958168, -0.025073157623410225, 0.0324588380753994, 0.01821891963481903, -0.0022943983785808086, -0.023056404665112495, -0.013803863897919655, 0.0044763763435184956, 0.015711603686213493, 0.004939684644341469, -0.0014793503796681762, -0.006820171605795622, -0.009913436137139797, -0.006748631596565247, -0.0033589855302125216, -0.004483189899474382, -0.010696972720324993, 0.03869987651705742, 0.020821623504161835, -0.009790795855224133, 0.021857254207134247, -0.0024630289990454912, -0.013190661557018757, 0.016706354916095734, -0.0065476372838020325, -0.02264760248363018, -0.03270411863923073, 0.0026282528415322304, -0.009606835432350636, -0.018450574949383736, 0.014730480499565601, -0.00041923444950953126, 0.011160280555486679, 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-73,"Checking job status
-To display jobs currently in queue, use squeue and to get only your jobs type
-squeue -u $USER
-JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
-133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)
-
-Note
-The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
-at any given time from the given association. If this limit is reached, new submission requests
-will be denied until existing jobs in this association complete.",168,"[-0.011936421506106853, -0.02893262356519699, -0.005028929095715284, -0.023694586008787155, -0.05068008601665497, -0.011360511183738708, -0.0363646075129509, 0.004161635879427195, -0.023680873215198517, 0.0003766554582398385, 0.006890352349728346, 0.0010686902096495032, -0.004740973934531212, -0.008919749408960342, -0.012964832596480846, -0.020239124074578285, 0.018689652904868126, -0.008631794713437557, 0.010428085923194885, -0.025532012805342674, -0.00466212909668684, 0.00692463293671608, -0.02702663652598858, -0.01970435120165348, -0.018689652904868126, 0.025449739769101143, 0.015316464938223362, 0.009790470823645592, 0.021404657512903214, 0.001787720830179751, -0.0029395408928394318, 0.018991319462656975, -0.017633818089962006, -0.0036062938161194324, -0.03422551229596138, -0.020266549661755562, 0.00098041829187423, -0.0063452948816120625, 0.0027355728670954704, -0.00699319364503026, 0.0297005046159029, 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-74,"Removing a job
-To cancel your job simply use scancel
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-75,"Partitioning
-Since we don’t have many GPUs on the cluster, resources must be shared as fairly
-as possible.  The --partition=/-p flag of SLURM allows you to set the
-priority you need for a job.  Each job assigned with a priority can preempt jobs
-with a lower priority: unkillable > main > long. Once preempted, your job is
-killed without notice and is automatically re-queued on the same partition until
-resources are available. (To leverage a different preemption mechanism, see the
-Handling preemption)
-
-
-
-
-
-
-
-
-Flag
-Max Resource Usage
-Max Time
-Note
-
-
-
---partition=unkillable
-6  CPUs, mem=32G,  1 GPU
-2 days
-
-
---partition=unkillable-cpu
-2  CPUs, mem=16G
-2 days
-CPU-only jobs
-
---partition=short-unkillable
-24 CPUs, mem=128G, 4 GPUs
-3 hours (!)
-Large but short jobs
-
---partition=main
-8  CPUs, mem=48G,  2 GPUs
-5 days
-
-
---partition=main-cpu
-8  CPUs, mem=64G
-5 days
-CPU-only jobs
-
---partition=long
-no limit of resources
-7 days
-
-
---partition=long-cpu
-no limit of resources
-7 days
-CPU-only jobs
-
-
-
-
-Warning
-Historically, before the 2022 introduction of CPU-only nodes (e.g. the cn-f
-series), CPU jobs ran side-by-side with the GPU jobs on GPU nodes. To prevent
-them obstructing any GPU job, they were always lowest-priority and preemptible.
-This was implemented by automatically assigning them to one of the now-obsolete
-partitions cpu_jobs, cpu_jobs_low or cpu_jobs_low-grace.
-Do not use these partition names anymore. Prefer the *-cpu partition
-names defined above.
-For backwards-compatibility purposes, the legacy partition names are translated
-to their effective equivalent long-cpu, but they will eventually be removed
-entirely.
-
-
-Note
-As a convenience, should you request the unkillable, main or long
-partition for a CPU-only job, the partition will be translated to its -cpu
-equivalent automatically.
-
-For instance, to request an unkillable job with 1 GPU, 4 CPUs, 10G of RAM and
-12h of computation do:
-sbatch --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable <job.sh>
-You can also make it an interactive job using salloc:
-salloc --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable
-The Mila cluster has many different types of nodes/GPUs. To request a specific
-type of node/GPU, you can add specific feature requirements to your job
-submission command.
-To access those special nodes you need to request them explicitly by adding the
-flag --constraint=<name>.  The full list of nodes in the Mila Cluster can be
-accessed Node profile description.
-Example:
-To request a machine with 2 GPUs using NVLink, you can use
-sbatch -c 4 --gres=gpu:2 --constraint=nvlink
-
-
-
-
-
-
-Feature
-Particularities
-
-
-
-12GB/16GB/24GB/32GB/48GB
-Request a specific amount of GPU memory
-
-volta/turing/ampere
-Request a specific GPU architecture
-
-nvlink
-Machine with GPUs using the NVLink interconnect technology
-
-
-
-
-Information on partitions/nodes
-sinfo (ref.) provides most of the
-information about available nodes and partitions/queues to submit jobs to.
-Partitions are a group of nodes usually sharing similar features. On a
-partition, some job limits can be applied which will override those asked for a
-job (i.e. max time, max CPUs, etc…)
-To display available partitions, simply use
-sinfo
-PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
-batch     up     infinite     2 alloc  node[1,3,5-9]
-batch     up     infinite     6 idle   node[10-15]
-cpu       up     infinite     6 idle   cpu_node[1-15]
-gpu       up     infinite     6 idle   gpu_node[1-15]
-To display available nodes and their status, you can use
-sinfo -N -l
-NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
-node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
-node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
-node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
-...
-And to get statistics on a job running or terminated, use sacct with some of
-the fields you want to display
-sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
-     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
---------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
-my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
-my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
-Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional time formats.
-sacct -u $USER --start=2019-01-01
-scontrol (ref.) can be used to
-provide specific information on a job (currently running or recently terminated)
-scontrol show job 43123
-JobId=43123 JobName=python_script.py
-UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
-Priority=645895 Nice=0 Account=my_username QOS=normal
-JobState=RUNNING Reason=None Dependency=(null)
-Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
-RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
-SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
-AccrueTime=2019-03-26T08:49:18
-StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
-PreemptTime=None SuspendTime=None SecsPreSuspend=0
-LastSchedEval=2019-03-26T08:49:18
-Partition=slurm_partition AllocNode:Sid=login-node-1:14586
-ReqNodeList=(null) ExcNodeList=(null)
-NodeList=node2
-BatchHost=node2
-NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
-TRES=cpu=16,mem=32000M,node=1,billing=3
-Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
-MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
-Features=(null) DelayBoot=00:00:00
-OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
-WorkDir=/home/mila/my_username
-StdErr=/home/mila/my_username/slurm-43123.out
-StdIn=/dev/null
-StdOut=/home/mila/my_username/slurm-43123.out
-Power=
-Or more info on a node and its resources
-scontrol show node node9
-NodeName=node9 Arch=x86_64 CoresPerSocket=4
-CPUAlloc=16 CPUTot=16 CPULoad=1.38
-AvailableFeatures=(null)
-ActiveFeatures=(null)
-Gres=(null)
-NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
-OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
-RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
-State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
-Partitions=slurm_partition
-BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
-CfgTRES=cpu=16,mem=32000M,billing=3
-AllocTRES=cpu=16,mem=32000M
-CapWatts=n/a
-CurrentWatts=0 LowestJoules=0 ConsumedJoules=0
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-76,"Information on partitions/nodes
-sinfo (ref.) provides most of the
-information about available nodes and partitions/queues to submit jobs to.
-Partitions are a group of nodes usually sharing similar features. On a
-partition, some job limits can be applied which will override those asked for a
-job (i.e. max time, max CPUs, etc…)
-To display available partitions, simply use
-sinfo
-PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
-batch     up     infinite     2 alloc  node[1,3,5-9]
-batch     up     infinite     6 idle   node[10-15]
-cpu       up     infinite     6 idle   cpu_node[1-15]
-gpu       up     infinite     6 idle   gpu_node[1-15]
-To display available nodes and their status, you can use
-sinfo -N -l
-NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
-node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
-node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
-node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
-...
-And to get statistics on a job running or terminated, use sacct with some of
-the fields you want to display
-sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
-     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
---------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
-my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
-my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
-Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional time formats.
-sacct -u $USER --start=2019-01-01
-scontrol (ref.) can be used to
-provide specific information on a job (currently running or recently terminated)
-scontrol show job 43123
-JobId=43123 JobName=python_script.py
-UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
-Priority=645895 Nice=0 Account=my_username QOS=normal
-JobState=RUNNING Reason=None Dependency=(null)
-Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
-RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
-SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
-AccrueTime=2019-03-26T08:49:18
-StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
-PreemptTime=None SuspendTime=None SecsPreSuspend=0
-LastSchedEval=2019-03-26T08:49:18
-Partition=slurm_partition AllocNode:Sid=login-node-1:14586
-ReqNodeList=(null) ExcNodeList=(null)
-NodeList=node2
-BatchHost=node2
-NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
-TRES=cpu=16,mem=32000M,node=1,billing=3
-Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
-MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
-Features=(null) DelayBoot=00:00:00
-OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
-WorkDir=/home/mila/my_username
-StdErr=/home/mila/my_username/slurm-43123.out
-StdIn=/dev/null
-StdOut=/home/mila/my_username/slurm-43123.out
-Power=
-Or more info on a node and its resources
-scontrol show node node9
-NodeName=node9 Arch=x86_64 CoresPerSocket=4
-CPUAlloc=16 CPUTot=16 CPULoad=1.38
-AvailableFeatures=(null)
-ActiveFeatures=(null)
-Gres=(null)
-NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
-OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
-RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
-State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
-Partitions=slurm_partition
-BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
-CfgTRES=cpu=16,mem=32000M,billing=3
-AllocTRES=cpu=16,mem=32000M
-CapWatts=n/a
-CurrentWatts=0 LowestJoules=0 ConsumedJoules=0
-ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s",1362,"[0.006787808146327734, 0.0031490011606365442, 0.016924360767006874, -0.027331870049238205, -0.03662776201963425, 0.0015519208973273635, -0.02182936854660511, 0.016840990632772446, -0.01347140222787857, -0.03562730923295021, 0.01778586395084858, 0.031792230904102325, -0.015159670263528824, 0.008580289781093597, 0.0004309685027692467, 0.004227616358548403, 0.015479260124266148, -0.016785409301519394, 0.01075488980859518, -0.0038454984314739704, -0.001848062383942306, 0.002179810544475913, -0.02121797949075699, -0.0030361025128513575, -0.010365824215114117, 0.040101565420627594, 0.015812745317816734, -0.002945783780887723, 0.0035484882537275553, 0.016312971711158752, -0.00984475389122963, 0.0030065751634538174, -0.024872418493032455, -0.02321888878941536, -0.017438484355807304, 0.00624589528888464, 0.012519581243395805, -0.024552829563617706, 0.008747031912207603, -0.021162400022149086, 0.01718837022781372, 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-77,"Useful Commands
-
-sallocGet an interactive job and give you a shell. (ssh like) CPU only
-
-salloc --gres=gpu:1 -c 2 --mem=12000Get an interactive job with one GPU, 2 CPUs and 12000 MB RAM
-
-sbatchstart a batch job (same options as salloc)
-
-sattach --pty <jobid>.0Re-attach a dropped interactive job
-
-sinfostatus of all nodes
-
-sinfo -Ogres:27,nodelist,features -tidle,mix,allocList GPU type and FEATURES that you can request
-
-savail(Custom) List available gpu
-
-scancel <jobid>Cancel a job
-
-squeuesummary status of all active jobs
-
-squeue -u $USERsummary status of all YOUR active jobs
-
-squeue -j <jobid>summary status of a specific job
-
-squeue -Ojobid,name,username,partition,state,timeused,nodelist,gres,tresstatus of all jobs including requested resources (see the SLURM squeue doc for all output options)
-
-scontrol show job <jobid>Detailed status of a running job
-
-sacct -j <job_id> -o NodeListGet the node where a finished job ran
-
-sacct -u $USER -S <start_time> -E <stop_time>Find info about old jobs
-
-sacct -oJobID,JobName,User,Partition,Node,StateList of current and recent jobs",307,"[-0.012057056650519371, 0.0006974077550694346, 0.012245121411979198, -0.02439272776246071, -0.029226696118712425, -0.011625204235315323, -0.004799140617251396, -0.0040747420862317085, -0.032792966812849045, -0.029310280457139015, 0.03368453308939934, 0.010608259588479996, -0.005704638548195362, 0.017301980406045914, 0.0030369015876203775, -0.028725188225507736, 0.021341895684599876, -0.021258311346173286, -0.0020791632123291492, 0.003446117276325822, -0.006366348825395107, 0.004642419517040253, -0.003216259879991412, 0.001859753974713385, -0.0013704367447644472, 0.02088218182325363, 0.00812858808785677, -0.003775231074541807, -0.00719522824510932, 0.006188732106238604, -0.004461320117115974, 0.00766887329518795, -0.0032110358588397503, -0.012711801566183567, -0.03630351275205612, 0.015727806836366653, 0.013011313043534756, -0.001974682556465268, -0.006049424409866333, 0.0037717483937740326, 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-78,"Special GPU requirements
-Specific GPU architecture and memory can be easily requested through the
---gres flag by using either
-
---gres=gpu:architecture:number
---gres=gpu:memory:number
---gres=gpu:model:number
-
-Example:
-To request 1 GPU with at least 16GB of memory use
-sbatch -c 4 --gres=gpu:16gb:1
-The full list of GPU and their features can be accessed here.",94,"[0.0016877787420526147, 0.0009171421988867223, 0.025906937196850777, -0.03308825567364693, 0.005182742606848478, 0.003495810553431511, -0.00585006270557642, -0.005230166483670473, -0.01689641736447811, -0.017858443781733513, 0.033657338470220566, 0.016666073352098465, -0.00285897683352232, -0.023982886224985123, -0.00921376422047615, 0.005182742606848478, 0.0029047068674117327, 0.018034588545560837, 0.027383849024772644, 0.00849563255906105, -0.00904439389705658, -0.015487253665924072, -0.0088546983897686, 0.012689250521361828, -0.004244429059326649, 0.0006808700854890049, 0.008536281995475292, -0.016760921105742455, -0.004583170171827078, 0.006378499325364828, -0.0025456412695348263, 0.0042105549946427345, -0.01849527657032013, -0.012018542736768723, -0.03799322992563248, 0.012506330385804176, 0.004369763191789389, 0.010792299173772335, 0.024321628734469414, -0.003055446781218052, 0.017736496403813362, 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-79,"Example script
-Here is a sbatch script that follows good practices on the Mila cluster:
- 1#!/bin/bash
- 2
- 3#SBATCH --partition=unkillable                           # Ask for unkillable job
- 4#SBATCH --cpus-per-task=2                                # Ask for 2 CPUs
- 5#SBATCH --gres=gpu:1                                     # Ask for 1 GPU
- 6#SBATCH --mem=10G                                        # Ask for 10 GB of RAM
- 7#SBATCH --time=3:00:00                                   # The job will run for 3 hours
- 8#SBATCH -o /network/scratch/<u>/<username>/slurm-%j.out  # Write the log on scratch
- 9
-10# 1. Load the required modules
-11module --quiet load anaconda/3
-12
-13# 2. Load your environment
-14conda activate ""<env_name>""
-15
-16# 3. Copy your dataset on the compute node
-17cp /network/datasets/<dataset> $SLURM_TMPDIR
-18
-19# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
-20#    and look for the dataset into $SLURM_TMPDIR
-21python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
-22
-23# 5. Copy whatever you want to save on $SCRATCH
-24cp $SLURM_TMPDIR/<to_save> /network/scratch/<u>/<username>/",324,"[-0.013378001749515533, -0.0004840043548028916, 0.015805350616574287, -0.019542915746569633, -0.010771360248327255, 0.0011197180720046163, -0.015874309465289116, -0.017474152147769928, -0.016398396342992783, -0.02681116946041584, 0.02937643602490425, 0.010716192424297333, -0.0005589970387518406, -0.00018855913367588073, -0.01327456347644329, 0.00200670026242733, 0.0251423679292202, -0.014895094558596611, 0.012309140525758266, -0.010185210034251213, -0.004475424066185951, 0.016591480001807213, -0.00026376728783361614, -0.01348833553493023, -0.01563984900712967, 0.002129102125763893, 0.005040885880589485, -0.01730865053832531, 0.002123930025845766, 0.019404998049139977, -0.008357803337275982, -0.01377796195447445, 0.002384249586611986, 0.0009050838416442275, -0.03740323707461357, 0.0021032425574958324, -0.00180844368878752, -0.0077302781865000725, 0.006137330550700426, -0.0106955049559474, 0.02478378266096115, 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-80,"print(torch.__file__)'
-cvmfs/ai.mila.quebec/apps/x86_64/debian/pytorch/python3.7-cuda10.1-cudnn7.6-v1.5.0/lib/python3.7/site-packages/torch/__init__.py'
-
-
-
-On using containers
-Another option for creating portable code is Using containers on clusters.
-Containers are a popular approach at deploying applications by packaging a lot
-of the required dependencies together. The most popular tool for this is
-Docker, but Docker cannot be used on the Mila
-cluster (nor the other clusters from Digital Research Alliance of Canada).
-One popular mechanism for containerisation on a computational cluster is called
-Singularity.
-This is the recommended approach for running containers on the
-Mila cluster. See section Singularity for more details.",170,"[-0.006494851782917976, -0.015869194641709328, 0.007536445278674364, -0.03725563734769821, -0.008624252863228321, 0.01931036449968815, -0.02634911984205246, -0.01611093059182167, -0.00784927885979414, -0.011759699322283268, 0.05741918459534645, 0.02275153435766697, 0.005623894743621349, 0.00863136351108551, -0.018784234300255775, 0.005101320333778858, 0.007195172365754843, -0.011240679770708084, 0.001445966656319797, -0.010060443542897701, -0.005769646726548672, 0.013231438584625721, 0.001662817201577127, -0.0207323357462883, -0.021656615659594536, 0.01871313713490963, 0.014440113678574562, -0.032762207090854645, -0.004802706651389599, -0.006014936603605747, 0.013572711497545242, -0.005382159724831581, -0.02475651167333126, -0.025439059361815453, -0.029548553749918938, 0.012015653774142265, 0.020774994045495987, -0.026989007368683815, -0.014141499996185303, -0.022708874195814133, 0.029463235288858414, 0.015542141161859035, 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-82,"Virtual environments
-A virtual environment in Python is a local, isolated environment in which you
-can install or uninstall Python packages without interfering with the global
-environment (or other virtual environments). It usually lives in a directory
-(location varies depending on whether you use venv, conda or poetry). In order
-to use a virtual environment, you have to activate it. Activating an
-environment essentially sets environment variables in your shell so that:
-
-python points to the right Python version for that environment (different
-virtual environments can use different versions of Python!)
-python looks for packages in the virtual environment
-pip install installs packages into the virtual environment
-Any shell commands installed via pip install are made available
-
-To run experiments within a virtual environment, you can simply activate it
-in the script given to sbatch.
-
-Pip/Virtualenv
-Pip is the preferred package manager for Python and each cluster provides
-several Python versions through the associated module which comes with pip. In
-order to install new packages, you will first have to create a personal space
-for them to be stored.  The preferred solution (as it is the preferred solution
-on Digital Research Alliance of Canada clusters) is to use virtual
-environments.
-First, load the Python module you want to use:
-module load python/3.8
-Then, create a virtual environment in your home directory:
-python -m venv $HOME/<env>
-Where <env> is the name of your environment. Finally, activate the environment:
-source $HOME/<env>/bin/activate
-You can now install any Python package you wish using the pip command, e.g.
-pytorch:
-pip install torch torchvision
-Or Tensorflow:
-pip install tensorflow-gpu
-
-
-Conda
-Another solution for Python is to use miniconda or anaconda which are also available through the module
-command: (the use of Conda is not recommended for Digital Research Alliance of
-Canada clusters due to the availability of custom-built packages for pip)
-module load miniconda/3
-=== Module miniconda/3 loaded ===]
-o enable conda environment functions, first use:
-To create an environment (see here
-for details) using a specific Python version, you may write:
-conda create -n <env> python=3.9
-Where <env> is the name of your environment. You can now activate it by doing:
-conda activate <env>
-You are now ready to install any Python package you want in this environment.
-For instance, to install PyTorch, you can find the Conda command of any version
-you want on pytorch’s website, e.g:
-conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
-If you make a lot of environments and install/uninstall a lot of packages, it
-can be good to periodically clean up Conda’s cache:
-conda clean --all",592,"[-0.005768963135778904, -0.014696724712848663, 0.017383364960551262, -0.009915301576256752, -0.021386727690696716, 0.011823880486190319, 0.0015877117402851582, -0.030377663671970367, -0.013266952708363533, -1.8070222722599283e-06, 0.012701693922281265, 0.013060799799859524, -0.017330164089798927, 0.0007414863212034106, -0.004841274116188288, -0.005802213679999113, 0.011956882663071156, 0.005998391658067703, -0.009270241484045982, 0.018726686015725136, -0.024552175775170326, 0.019245393574237823, 0.0006616851314902306, -0.015268633142113686, -0.036442555487155914, 0.008412378840148449, -0.0011729117250069976, -0.004817998502403498, 0.004182913806289434, -0.010094854049384594, 0.011624378152191639, -0.011345073580741882, -0.014483921229839325, -0.025523090735077858, -0.022104937583208084, 0.022557144984602928, 0.004036611877381802, -0.03362291306257248, 0.004455568268895149, 0.008977637626230717, 0.026228001341223717, 0.028595436364412308, -0.013446505181491375, 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-83,"Pip/Virtualenv
-Pip is the preferred package manager for Python and each cluster provides
-several Python versions through the associated module which comes with pip. In
-order to install new packages, you will first have to create a personal space
-for them to be stored.  The preferred solution (as it is the preferred solution
-on Digital Research Alliance of Canada clusters) is to use virtual
-environments.
-First, load the Python module you want to use:
-module load python/3.8
-Then, create a virtual environment in your home directory:
-python -m venv $HOME/<env>
-Where <env> is the name of your environment. Finally, activate the environment:
-source $HOME/<env>/bin/activate
-You can now install any Python package you wish using the pip command, e.g.
-pytorch:
-pip install torch torchvision
-Or Tensorflow:
-pip install tensorflow-gpu",187,"[-0.01617649756371975, -0.020919274538755417, 0.01052009779959917, -0.030579490587115288, -0.01205847691744566, 0.036410555243492126, 0.0038728199433535337, -0.022625599056482315, 0.008182298392057419, -0.005142487119883299, 0.013469218276441097, -0.0018977825529873371, -0.003112027421593666, 0.006452460773289204, -0.01007672119885683, -0.007221650332212448, 0.009398221969604492, 0.008518188260495663, -0.0051156156696379185, 0.013905876316130161, -0.01613618992269039, 0.013731212355196476, 0.021308908239006996, -0.020502770319581032, -0.03342784568667412, 0.009136227890849113, 0.00567655311897397, 0.000859880237840116, 0.008659263141453266, 0.0004463148070499301, 0.018809881061315536, -0.013361732475459576, 9.551892435410991e-05, -0.027811752632260323, -0.03079446218907833, 0.006862247362732887, 0.01009687501937151, -0.021940382197499275, -0.010231231339275837, -0.011554640717804432, 0.027758009731769562, 0.008833926171064377, -0.0026669723447412252, 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-84,"Conda
-Another solution for Python is to use miniconda or anaconda which are also available through the module
-command: (the use of Conda is not recommended for Digital Research Alliance of
-Canada clusters due to the availability of custom-built packages for pip)
-module load miniconda/3
-=== Module miniconda/3 loaded ===]
-o enable conda environment functions, first use:
-To create an environment (see here
-for details) using a specific Python version, you may write:
-conda create -n <env> python=3.9
-Where <env> is the name of your environment. You can now activate it by doing:
-conda activate <env>
-You are now ready to install any Python package you want in this environment.
-For instance, to install PyTorch, you can find the Conda command of any version
-you want on pytorch’s website, e.g:
-conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
-If you make a lot of environments and install/uninstall a lot of packages, it
-can be good to periodically clean up Conda’s cache:
-conda clean --all",240,"[-0.005291816778481007, -0.01600024104118347, 0.01169663481414318, -0.018496062606573105, -0.01560900453478098, 0.024351125583052635, 0.0023575397208333015, -0.023932907730340958, 0.0004506813420448452, 0.013605601154267788, 0.02522803656756878, 0.007042264565825462, -0.0002306105598108843, 0.00971346814185381, -0.010664578527212143, 0.011507761664688587, 0.0132615827023983, 0.028816623613238335, -0.006128254346549511, 0.018482571467757225, -0.0034317548852413893, 0.020087992772459984, 0.015069367364048958, -0.024823307991027832, -0.03248615562915802, 0.015244749374687672, 0.0032749229576438665, -0.005753880832344294, 0.006900609936565161, 0.00701528275385499, 0.03140688315033913, -0.009092886000871658, -0.009760687127709389, -0.031676698476076126, -0.022799668833613396, 0.007986629381775856, 0.00884330365806818, -0.011406579986214638, -0.0008946171728894114, -0.0010177219519391656, 0.023190906271338463, 0.011291907168924809, -0.002701558405533433, 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-85,"print(torch.__file__)'
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-86,"The module command
-For a list of available modules, simply use:
-module avail
--------------------------------------------------------------------------------------------------------------- Global Aliases ---------------------------------------------------------------------------------------------------------------
-  cuda/10.0 -> cudatoolkit/10.0    cuda/9.2      -> cudatoolkit/9.2                                 pytorch/1.4.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1    tensorflow/1.15 -> python/3.7/tensorflow/1.15
-  cuda/10.1 -> cudatoolkit/10.1    mujoco-py     -> python/3.7/mujoco-py/2.0                        pytorch/1.5.0       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0    tensorflow/2.2  -> python/3.7/tensorflow/2.2
-  cuda/10.2 -> cudatoolkit/10.2    mujoco-py/2.0 -> python/3.7/mujoco-py/2.0                        pytorch/1.5.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1
-  cuda/11.0 -> cudatoolkit/11.0    pytorch       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1    tensorflow          -> python/3.7/tensorflow/2.2
-  cuda/9.0  -> cudatoolkit/9.0     pytorch/1.4.0 -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.0    tensorflow-cpu/1.15 -> python/3.7/tensorflow/1.15
-
--------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Core ---------------------------------------------------------------------------------------------------
-  Mila       (S,L)    anaconda/3 (D)    go/1.13.5        miniconda/2        mujoco/1.50        python/2.7    python/3.6        python/3.8           singularity/3.0.3    singularity/3.2.1    singularity/3.5.3 (D)
-  anaconda/2          go/1.12.4         go/1.14   (D)    miniconda/3 (D)    mujoco/2.0  (D)    python/3.5    python/3.7 (D)    singularity/2.6.1    singularity/3.1.1    singularity/3.4.2
-
------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Compiler -------------------------------------------------------------------------------------------------
-  python/3.7/mujoco-py/2.0
-
--------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Cuda ---------------------------------------------------------------------------------------------------
-  cuda/10.0/cudnn/7.3        cuda/10.0/nccl/2.4         cuda/10.1/nccl/2.4     cuda/11.0/nccl/2.7        cuda/9.0/nccl/2.4     cudatoolkit/9.0     cudatoolkit/10.1        cudnn/7.6/cuda/10.0/tensorrt/7.0
-  cuda/10.0/cudnn/7.5        cuda/10.1/cudnn/7.5        cuda/10.2/cudnn/7.6    cuda/9.0/cudnn/7.3        cuda/9.2/cudnn/7.6    cudatoolkit/9.2     cudatoolkit/10.2        cudnn/7.6/cuda/10.1/tensorrt/7.0
-  cuda/10.0/cudnn/7.6 (D)    cuda/10.1/cudnn/7.6 (D)    cuda/10.2/nccl/2.7     cuda/9.0/cudnn/7.5 (D)    cuda/9.2/nccl/2.4     cudatoolkit/10.0    cudatoolkit/11.0 (D)    cudnn/7.6/cuda/9.0/tensorrt/7.0
-
------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Pytorch --------------------------------------------------------------------------------------------------
-  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.4.1    python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.1 (D)    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0
-  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.0    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1        python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1 (D)
-
------------------------------------------------------------------------------------------------ /cvmfs/config.mila.quebec/modules/Tensorflow ------------------------------------------------------------------------------------------------
-  python/3.7/tensorflow/1.15    python/3.7/tensorflow/2.0    python/3.7/tensorflow/2.2 (D)
-Modules can be loaded using the load command:
-module load <module>
-To search for a module or a software, use the command spider:
-module spider search_term
-E.g.: by default, python2 will refer to the os-shipped installation of python2.7 and python3 to python3.6.
-If you want to use python3.7 you can type:
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-87,"Available Software
-Modules are divided in 5 main sections:
-
-
-
-
-
-
-Section
-Description
-
-
-
-Core
-Base interpreter and software (Python, go, etc…)
-
-Compiler
-Interpreter-dependent software (see the note below)
-
-Cuda
-Toolkits, cudnn and related libraries
-
-Pytorch/Tensorflow
-Pytorch/TF built with a specific Cuda/Cudnn
-version for Mila’s GPUs (see the related paragraph)
-
-
-
-
-Note
-Modules which are nested (../../..) usually depend on other software/module
-loaded alongside the main module.  No need to load the dependent software,
-the complex naming scheme allows an automatic detection of the dependent
-module(s):
-i.e.: Loading cudnn/7.6/cuda/9.0/tensorrt/7.0 will load cudnn/7.6 and
-cuda/9.0 alongside
-python/3.X is a particular dependency which can be served through
-python/3.X or anaconda/3 and is not automatically loaded to let the
-user pick his favorite flavor.",212,"[-0.022183023393154144, -0.009789557196199894, 0.005524412263184786, -0.03881673514842987, -0.011561068706214428, 0.010621954686939716, -0.006765893194824457, -0.020062901079654694, -0.013161832466721535, -0.004446565173566341, 0.04541899263858795, 0.01815621368587017, -0.016263756901025772, 0.0007052252185530961, -0.028657222166657448, 0.0029205039609223604, 0.0022446259390562773, 0.012002168223261833, 0.013197405263781548, -0.03050699271261692, 0.008508946746587753, 0.02585410699248314, -0.011333405040204525, -0.044792916625738144, -0.013396610505878925, 0.01200928259640932, 0.002941847313195467, -0.039215147495269775, -0.0029329543467611074, -0.015068518929183483, 0.018341191112995148, -0.004812961909919977, -0.0183696486055851, -0.0578266866505146, -0.016078779473900795, -0.009334228932857513, 0.007470229174941778, -0.017885863780975342, 4.9245707486988977e-05, 0.013311237096786499, 0.03221447393298149, 0.015338870696723461, 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-88,"print(torch.__file__)'
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-89,"On using containers
-Another option for creating portable code is Using containers on clusters.
-Containers are a popular approach at deploying applications by packaging a lot
-of the required dependencies together. The most popular tool for this is
-Docker, but Docker cannot be used on the Mila
-cluster (nor the other clusters from Digital Research Alliance of Canada).
-One popular mechanism for containerisation on a computational cluster is called
-Singularity.
-This is the recommended approach for running containers on the
-Mila cluster. See section Singularity for more details.",110,"[-0.012206011451780796, -0.010396954603493214, 0.010786072351038456, -0.02960025891661644, -0.011782759800553322, 0.022473258897662163, -0.026091372594237328, -0.00895653571933508, -0.00023871865414548665, -0.02260979264974594, 0.03678187355399132, 0.022664405405521393, 0.015482792630791664, 0.0062907375395298, -0.02349725365638733, -0.0004034110461361706, 0.019933754578232765, -0.0015999909956008196, 0.005918686743825674, -0.01496396865695715, -0.01257464848458767, 0.010205809026956558, 0.01759904809296131, -0.017994992434978485, -0.028071094304323196, 0.018650347366929054, 0.02080756239593029, -0.029873324558138847, 0.00592210004106164, -0.0026760383043438196, 0.028071094304323196, -0.006376070436090231, -0.01825440302491188, -0.016861772164702415, -0.02510833740234375, -0.0007189292809925973, 0.019018985331058502, -0.026787688955664635, -0.029327193275094032, -0.01905994489789009, 0.02071199007332325, 0.005526155233383179, 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-90,"Singularity
-
-Overview
-
-What is Singularity?
-Running Docker on SLURM is a security problem (e.g. running as root, being able
-to mount any directory).  The alternative is to use Singularity, which is a
-popular solution in the world of HPC.
-There is a good level of compatibility between Docker and Singularity,
-and we can find many exaggerated claims about able to convert containers
-from Docker to Singularity without any friction.
-Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
-and they can indeed be used without friction, but things get messy when
-we try to convert our own Docker build files to Singularity recipes.
-
-
-Links to official documentation
-
-official Singularity user guide (this is the one you
-will use most often)
-official Singularity admin guide
-
-
-
-Overview of the steps used in practice
-Most often, the process to create and use a Singularity container is:
-
-on your Linux computer (at home or work)
-
-select a Docker image from DockerHub (e.g. pytorch/pytorch)
-make a recipe file for Singularity that starts with that DockerHub image
-build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
-test your singularity container before send it over to the cluster
-rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images
-
-
-on the login node for that cluster
-
-queue your jobs with sbatch ...
-(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
-and will then launch Singularity with that image)
-do something else while you wait for them to finish
-queue more jobs with the same my-pytorch-image.sif,
-reusing it many times over
-
-
-
-In the following sections you will find specific examples or tips to accomplish
-in practice the steps highlighted above.
-
-
-Nope, not on MacOS
-Singularity does not work on MacOS, as of the time of this writing in 2021.
-Docker does not actually run on MacOS, but there Docker silently installs a
-virtual machine running Linux, which makes it a pleasant experience,
-and the user does not need to care about the details of how Docker does it.
-Given its origins in HPC, Singularity does not provide that kind of seamless
-experience on MacOS, even though it’s technically possible to run it
-inside a Linux virtual machine on MacOS.
-
-
-Where to build images
-Building Singularity images is a rather heavy task, which can take 20 minutes
-if you have a lot of steps in your recipe. This makes it a bad task to run on
-the login nodes of our clusters, especially if it needs to be run regularly.
-On the Mila cluster, we are lucky to have unrestricted internet access on the compute
-nodes, which means that anyone can request an interactive CPU node (no need for GPU)
-and build their images there without problem.
-
-Warning
-Do not build Singularity images from scratch every time your run a
-job in a large batch.  This will be a colossal waste of GPU time as well as
-internet bandwidth.  If you setup your workflow properly (e.g. using bind
-paths for your code and data), you can spend months reusing the same
-Singularity image my-pytorch-image.sif.
-
-
-
-
-Building the containers
-Building a container is like creating a new environment except that containers
-are much more powerful since they are self-contained systems.  With
-singularity, there are two ways to build containers.
-The first one is by yourself, it’s like when you got a new Linux laptop and you
-don’t really know what you need, if you see that something is missing, you
-install it. Here you can get a vanilla container with Ubuntu called a sandbox,
-you log in and you install each packages by yourself.  This procedure can take
-time but will allow you to understand how things work and what you need. This is
-recommended if you need to figure out how things will be compiled or if you want
-to install packages on the fly. We’ll refer to this procedure as singularity
-sandboxes.
-The second way is more like you know what you want, so you write a list of
-everything you need, you send it to singularity and it will install everything
-for you. Those lists are called singularity recipes.
-
-First way: Build and use a sandbox
-You might ask yourself: On which machine should I build a container?
-First of all, you need to choose where you’ll build your container. This
-operation requires memory and high cpu usage.
-
-Warning
-Do NOT build containers on any login nodes !
-
-
-(Recommended for beginner) If you need to use apt-get, you should build
-the container on your laptop with sudo privileges. You’ll only need to
-install singularity on your laptop. Windows/Mac users can look there and
-Ubuntu/Debian users can use directly:
-
-sudo apt-get install singularity-container
-
-
-If you can’t install singularity on your laptop and you don’t need
-apt-get, you can reserve a cpu node on the Mila cluster to build your
-container.
-
-In this case, in order to avoid too much I/O over the network, you should define
-the singularity cache locally:
-
-export SINGULARITY_CACHEDIR=$SLURM_TMPDIR
-
-
-If you can’t install singularity on your laptop and you want to use
-apt-get, you can use singularity-hub to build your containers and read
-Recipe_section.
-
-
-Download containers from the web
-Hopefully, you may not need to create containers from scratch as many have been
-already built for the most common deep learning software. You can find most of
-them on dockerhub.
-Go on dockerhub and select the container you want to pull.
-For example, if you want to get the latest PyTorch version with GPU support
-(Replace runtime by devel if you need the full Cuda toolkit):
-singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
-Or the latest TensorFlow:
-singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
-Currently the pulled image pytorch.simg or tensorflow.simg is read-only
-meaning that you won’t be able to install anything on it.  Starting now, PyTorch
-will be taken as example. If you use TensorFlow, simply replace every
-pytorch occurrences by tensorflow.
-
-
-How to add or install stuff in a container
-The first step is to transform your read only container
-pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
-allow you to add packages.
-
-Warning
-Depending on the version of singularity you are using, singularity
-will build a container with the extension .simg or .sif. If you’re using
-.sif files, replace every occurences of .simg by .sif.
-
-
-Tip
-If you want to use apt-get you have to put sudo ahead of the
-following commands
-
-This command will create a writable image in the folder pytorch.
-singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
-Then you’ll need the following command to log inside the container.
-singularity shell --writable -H $HOME:/home pytorch
-Once you get into the container, you can use pip and install anything you need
-(Or with apt-get if you built the container with sudo).
-
-Warning
-Singularity mounts your home folder, so if you install things into
-the $HOME of your container, they will be installed in your real
-$HOME!
-
-You should install your stuff in /usr/local instead.
-
-
-Creating useful directories
-One of the benefits of containers is that you’ll be able to use them across
-different clusters. However for each cluster the datasets and experiments
-folder location can be different. In order to be invariant to those locations,
-we will create some useful mount points inside the container:
-mkdir /dataset
-mkdir /tmp_log
-mkdir /final_log
-From now, you won’t need to worry anymore when you write your code to specify
-where to pick up your dataset. Your dataset will always be in /dataset
-independently of the cluster you are using.
-
-
-Testing
-If you have some code that you want to test before finalizing your container,
-you have two choices.  You can either log into your container and run Python
-code inside it with:
-singularity shell --nv pytorch
-Or you can execute your command directly with
-singularity exec --nv pytorch Python YOUR_CODE.py
-
-Tip
-—nv allows the container to use gpus. You don’t need this if you
-don’t plan to use a gpu.
-
-
-Warning
-Don’t forget to clear the cache of the packages you installed in
-the containers.
-
-
-
-Creating a new image from the sandbox
-Once everything you need is installed inside the container, you need to convert
-it back to a read-only singularity image with:
-singularity build pytorch_final.simg pytorch
-
-
-
-Second way: Use recipes
-A singularity recipe is a file including specifics about installation software,
-environment variables, files to add, and container metadata.  It is a starting
-point for designing any custom container. Instead of pulling a container and
-installing your packages manually, you can specify in this file the packages
-you want and then build your container from this file.
-Here is a toy example of a singularity recipe installing some stuff:
-################# Header: Define the base system you want to use ################
-# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
-Bootstrap: docker
-# Select the docker image you want to use (Here we choose tensorflow)
-From: tensorflow/tensorflow:latest-gpu-py3
-
-################# Section: Defining the system #################################
-# Commands in the %post section are executed within the container.
-%post
-        echo ""Installing Tools with apt-get""
-        apt-get update
-        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
-        apt-get clean
-        echo ""Installing things with pip""
-        pip install tqdm
-        echo ""Creating mount points""
-        mkdir /dataset
-        mkdir /tmp_log
-        mkdir /final_log
-
-
-# Environment variables that should be sourced at runtime.
-%environment
-        # use bash as default shell
-        SHELL=/bin/bash
-        export SHELL
-
-
-A recipe file contains two parts: the header and sections. In the
-header you specify which base system you want to use, it can be any docker
-or singularity container. In sections, you can list the things you want to
-install in the subsection post or list the environment’s variable you need
-to source at each runtime in the subsection environment. For a more detailed
-description, please look at the singularity documentation.
-In order to build a singularity container from a singularity recipe file, you
-should use:
-sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>
-
-Warning
-You always need to use sudo when you build a container from a
-recipe. As there is no access to sudo on the cluster, a personal computer or
-the use singularity hub is needed to build a container
-
-
-Build recipe on singularity hub
-Singularity hub allows users to build containers from recipes directly on
-singularity-hub’s cloud meaning that you don’t need to build containers by
-yourself.  You need to register on singularity-hub and link your
-singularity-hub account to your GitHub account, then:
-
-
-Create a new github repository.
-Add a collection on singularity-hub and select the github repository your created.
-Clone the github repository on your computer.
-$ git clone <url>
-
-
-
-Write the singularity recipe and save it as a file named Singularity.
-Git add Singularity, commit and push on the master branch
-$ git add Singularity
-$ git commit
-$ git push origin master
-
-
-
-
-
-At this point, robots from singularity-hub will build the container for you, you
-will be able to download your container from the website or directly with:
-singularity pull shub://<github_username>/<repository_name>
-
-
-Example: Recipe with OpenAI gym, MuJoCo and Miniworld
-Here is an example on how you can use a singularity recipe to install complex
-environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
-container. In order to use MuJoCo, you’ll need to copy the key stored on the
-Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the pytorch container
-From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        export PATH=$PATH:/opt/conda/bin
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libhdf5-dev \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        libhdf5-dev \
-        openjdk-8-jdk \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-        pip install h5py
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
-        pip install --upgrade minerl
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/sh
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/sh ""$@""
-
-
-Here is the same recipe but written for TensorFlow:
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the tensorflow container
-From: tensorflow/tensorflow:latest-gpu-py3
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-setuptools \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
-
-        # Then install miniworld
-        cd /usr/local/
-        git clone https://github.com/maximecb/gym-miniworld.git
-        cd gym-miniworld
-        pip install -e .
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/bash
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/bash ""$@""
-
-
-Keep in mind that those environment variables are sourced at runtime and not at
-build time. This is why, you should also define them in the %post section
-since they are required to install MuJoCo.
-
-
-
-
-Using containers on clusters
-
-How to use containers on clusters
-On every cluster with Slurm, datasets and intermediate results should go in
-$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
-In order to use the container you built, you need to copy it on the cluster you
-want to use.
-
-Warning
-You should always store your container in $SCRATCH !
-
-Then reserve a node with srun/sbatch, copy the container and your dataset on the
-node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
-<YOUR_CODE> within the container <YOUR_CONTAINER> with:
-singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
-Remember that /dataset, /tmp_log and /final_log were created in the
-previous section. Now each time, we’ll use singularity, we are explicitly
-telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
-/dataset inside the container with the option -B such that each dataset
-downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
-This will allow us to have code and scripts that are invariant to the cluster
-environment. The option -H specify what will be the container’s home. For
-example, if you have your code in $HOME/Project12345/Version35/ you can
-specify -H $HOME/Project12345/Version35:/home, thus the container will only
-have access to the code inside Version35.
-If you want to run multiple commands inside the container you can use:
-singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
-   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
-   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'
-
-Example: Interactive case (srun/salloc)
-Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
-<YOUR_DATASET> to $SLURM_TMPDIR
-0. Get an interactive session
-srun --gres=gpu:1
-1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-Then use singularity shell to get a shell inside the container
-3. Get a shell in your environment
-singularity shell --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>
-4. Execute your code
-python <YOUR_CODE>
-or use singularity exec to execute <YOUR_CODE>.
-3. Execute your code
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python <YOUR_CODE>
-You can create also the following alias to make your life easier.
-alias my_env='singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>'
-This will allow you to run any code with:
-my_env python <YOUR_CODE>
-
-
-Example: sbatch case
-You can also create a sbatch script:
-:linenos:
-
-#!/bin/bash
-#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
-#SBATCH --gres=gpu:1              # Ask for 1 GPU
-#SBATCH --mem=10G                 # Ask for 10 GB of RAM
-#SBATCH --time=0:10:00            # The job will run for 10 minutes
-
-# 1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-# 2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-# 3. Executing your code with singularity
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python ""<YOUR_CODE>""
-# 4. Copy whatever you want to save on $SCRATCH
-rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
-
-
-
-
-Issue with PyBullet and OpenGL libraries
-If you are running certain gym environments that require pyglet, you may
-encounter a problem when running your singularity instance with the Nvidia
-drivers using the --nv flag. This happens because the --nv flag also
-provides the OpenGL libraries:
-libGL.so.1 => /.singularity.d/libs/libGL.so.1
-libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
-
-
-If you don’t experience those problems with pyglet, you probably don’t need
-to address this. Otherwise, you can resolve those problems by apt-get install
--y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
-sure that your LD_LIBRARY_PATH points to those libraries before the ones in
-/.singularity.d/libs.
-%environment
-        # ...
-        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH
-
-
-
-
-Mila cluster
-On the Mila cluster $SCRATCH is not yet defined, you should add the
-experiment results you want to keep in /network/scratch/<u>/<username>/. In
-order to use the sbatch script above and to match other cluster environment’s
-names, you can define $SCRATCH as an alias for
-/network/scratch/<u>/<username> with:
-echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
-Then, you can follow the general procedure explained above.
-
-
-Digital Research Alliance of Canada
-Using singularity on Digital Research Alliance of Canada is similar except that
-you need to add Yoshua’s account name and load singularity. Here is an example
-of a sbatch script using singularity on compute Canada cluster:
-
-Warning
-You should use singularity/2.6 or singularity/3.4. There is a bug
-in singularity/3.2 which makes gpu unusable.
-
- 1#!/bin/bash
- 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
- 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
- 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
- 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
- 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
- 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
- 8
- 9# 1. You have to load singularity
-10module load singularity
-11# 2. Then you copy the container to the local disk
-12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-13# 3. Copy your dataset on the compute node
-14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-15# 4. Executing your code with singularity
-16singularity exec --nv \
-17        -H $HOME:/home \
-18        -B $SLURM_TMPDIR:/dataset/ \
-19        -B $SLURM_TMPDIR:/tmp_log/ \
-20        -B $SCRATCH:/final_log/ \
-21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-22        python ""<YOUR_CODE>""
-23# 5. Copy whatever you want to save on $SCRATCH
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-91,"Overview
-
-What is Singularity?
-Running Docker on SLURM is a security problem (e.g. running as root, being able
-to mount any directory).  The alternative is to use Singularity, which is a
-popular solution in the world of HPC.
-There is a good level of compatibility between Docker and Singularity,
-and we can find many exaggerated claims about able to convert containers
-from Docker to Singularity without any friction.
-Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
-and they can indeed be used without friction, but things get messy when
-we try to convert our own Docker build files to Singularity recipes.
-
-
-Links to official documentation
-
-official Singularity user guide (this is the one you
-will use most often)
-official Singularity admin guide
-
-
-
-Overview of the steps used in practice
-Most often, the process to create and use a Singularity container is:
-
-on your Linux computer (at home or work)
-
-select a Docker image from DockerHub (e.g. pytorch/pytorch)
-make a recipe file for Singularity that starts with that DockerHub image
-build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
-test your singularity container before send it over to the cluster
-rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images
-
-
-on the login node for that cluster
-
-queue your jobs with sbatch ...
-(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
-and will then launch Singularity with that image)
-do something else while you wait for them to finish
-queue more jobs with the same my-pytorch-image.sif,
-reusing it many times over
-
-
-
-In the following sections you will find specific examples or tips to accomplish
-in practice the steps highlighted above.
-
-
-Nope, not on MacOS
-Singularity does not work on MacOS, as of the time of this writing in 2021.
-Docker does not actually run on MacOS, but there Docker silently installs a
-virtual machine running Linux, which makes it a pleasant experience,
-and the user does not need to care about the details of how Docker does it.
-Given its origins in HPC, Singularity does not provide that kind of seamless
-experience on MacOS, even though it’s technically possible to run it
-inside a Linux virtual machine on MacOS.
-
-
-Where to build images
-Building Singularity images is a rather heavy task, which can take 20 minutes
-if you have a lot of steps in your recipe. This makes it a bad task to run on
-the login nodes of our clusters, especially if it needs to be run regularly.
-On the Mila cluster, we are lucky to have unrestricted internet access on the compute
-nodes, which means that anyone can request an interactive CPU node (no need for GPU)
-and build their images there without problem.
-
-Warning
-Do not build Singularity images from scratch every time your run a
-job in a large batch.  This will be a colossal waste of GPU time as well as
-internet bandwidth.  If you setup your workflow properly (e.g. using bind
-paths for your code and data), you can spend months reusing the same
-Singularity image my-pytorch-image.sif.",694,"[0.005968751851469278, -0.02496994473040104, 0.003029441926628351, -0.010595535859465599, 0.006332618650048971, -0.0029743611812591553, -0.023834947496652603, -0.002638869220390916, -0.010815858840942383, -0.022018952295184135, 0.03995191305875778, 0.014808379113674164, -0.010308448225259781, 0.017251962795853615, 0.007110425736755133, 0.00291093485429883, 0.025196945294737816, -0.02049672044813633, -0.0011875745840370655, -0.019108017906546593, -0.01114968117326498, -0.011036181822419167, -0.0002812457096297294, -0.0055815172381699085, -0.04473225399851799, 0.028628643602132797, 0.02599811926484108, -0.02123112976551056, -0.0021181057672947645, 0.00035301761818118393, -0.0023083847481757402, -0.00968753732740879, -0.02233942225575447, 0.00021615026344079524, -0.013780205510556698, 0.009867802262306213, 0.008552540093660355, -0.02431565336883068, -0.02229936234652996, -0.031138990074396133, 0.02342100813984871, 0.0018944443436339498, 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-92,"What is Singularity?
-Running Docker on SLURM is a security problem (e.g. running as root, being able
-to mount any directory).  The alternative is to use Singularity, which is a
-popular solution in the world of HPC.
-There is a good level of compatibility between Docker and Singularity,
-and we can find many exaggerated claims about able to convert containers
-from Docker to Singularity without any friction.
-Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
-and they can indeed be used without friction, but things get messy when
-we try to convert our own Docker build files to Singularity recipes.",138,"[0.01754847541451454, -0.02661094255745411, 0.0029577023815363646, -0.0005467141163535416, -0.00929707195609808, 2.668111483217217e-05, -0.034533899277448654, 0.0006510296370834112, -0.018178557977080345, -0.02277682162821293, 0.04721599072217941, 0.01848689652979374, -0.0031705228611826897, 0.017173107713460922, 0.014237189665436745, 0.011830809526145458, 0.029814980924129486, -0.02043076790869236, -0.0075073689222335815, -0.00977298617362976, -0.010858872905373573, -0.005576903000473976, 0.0037637390196323395, -0.0022455076687037945, -0.03195994347333908, 0.021382596343755722, 0.029922228306531906, -0.02301812916994095, 0.005322188604623079, -0.005834968760609627, 0.009846718981862068, -0.0011889460729435086, -0.01805790327489376, 0.00174780935049057, -0.005466303322464228, 0.0015207449905574322, -0.005707611329853535, -0.023138783872127533, -0.023876115679740906, -0.01451871544122696, 0.010275711305439472, 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-93,"Links to official documentation
-
-official Singularity user guide (this is the one you
-will use most often)
-official Singularity admin guide",29,"[0.008163072168827057, 0.005415040533989668, -0.004810743499547243, -0.026683591306209564, 0.018446248024702072, -0.00325948977842927, -0.02811499871313572, 0.005678365472704172, -0.028871214017271996, -0.007649926468729973, 0.032733313739299774, 0.011066398583352566, -0.007467624731361866, 0.03475888818502426, -0.018419239670038223, -0.008412893861532211, 0.009770029224455357, -0.03797280415892601, -0.002081279642879963, -0.027574844658374786, -0.01003335416316986, 0.0016525326063856483, -0.008453404530882835, -0.01498926430940628, -0.014003483578562737, 0.01660972461104393, 0.012909673154354095, -0.03891807049512863, -0.0037371881771832705, -0.013166246004402637, 0.011113662272691727, -0.01693381741642952, -0.0055905906483531, 0.0119779072701931, -0.0027784155681729317, 0.017919598147273064, -0.002324348781257868, -0.010634275153279305, -0.0013993355678394437, -0.03573116660118103, 0.01268685981631279, 0.0038249632343649864, 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-94,"Overview of the steps used in practice
-Most often, the process to create and use a Singularity container is:
-
-on your Linux computer (at home or work)
-
-select a Docker image from DockerHub (e.g. pytorch/pytorch)
-make a recipe file for Singularity that starts with that DockerHub image
-build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
-test your singularity container before send it over to the cluster
-rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images
-
-
-on the login node for that cluster
-
-queue your jobs with sbatch ...
-(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
-and will then launch Singularity with that image)
-do something else while you wait for them to finish
-queue more jobs with the same my-pytorch-image.sif,
-reusing it many times over
-
-
-
-In the following sections you will find specific examples or tips to accomplish
-in practice the steps highlighted above.",224,"[0.014468112960457802, -0.010657438077032566, 0.007261720020323992, -0.014813908375799656, 0.008333687670528889, -0.009972762316465378, -0.02637733332812786, -0.00021331301832105964, -0.015768306329846382, -0.032698486000299454, 0.02381844073534012, 0.00822994951158762, -0.022255443036556244, 0.011217628605663776, 0.014274466782808304, -0.006383397616446018, 0.011736322194337845, -0.024510033428668976, 0.000864058209117502, -0.017815420404076576, -0.03908880054950714, 0.0037311408668756485, 0.00451609818264842, -0.009716873057186604, -0.031840912997722626, 0.011833145283162594, 0.03214521333575249, -0.021065903827548027, 0.002845902694389224, 0.013610538095235825, -0.0029306227806955576, -0.009703041054308414, -0.010076500475406647, -0.013292405754327774, -0.033307090401649475, 0.011791650205850601, 0.008956121280789375, -0.01758027821779251, -0.010636691004037857, -0.04404060170054436, 0.02751154452562332, -0.00799480825662613, 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-95,"Nope, not on MacOS
-Singularity does not work on MacOS, as of the time of this writing in 2021.
-Docker does not actually run on MacOS, but there Docker silently installs a
-virtual machine running Linux, which makes it a pleasant experience,
-and the user does not need to care about the details of how Docker does it.
-Given its origins in HPC, Singularity does not provide that kind of seamless
-experience on MacOS, even though it’s technically possible to run it
-inside a Linux virtual machine on MacOS.",113,"[-0.0020315968431532383, -0.04913690313696861, -0.01149443257600069, -0.021487604826688766, 0.016070012003183365, 0.007401865907013416, -0.030599601566791534, -0.014033520594239235, -0.016905495896935463, -0.013152346014976501, 0.04161754623055458, 0.021200407296419144, -0.013328581117093563, -0.006703453604131937, 0.004963949788361788, 0.009575430303812027, 0.02173563838005066, -0.03746623545885086, -0.004121938720345497, -0.020404085516929626, -0.00704939616844058, 0.0010419073514640331, 0.0009864259045571089, -0.002586410380899906, -0.029294157400727272, 0.026356909424066544, 0.012147153727710247, -0.01657913625240326, 0.017218803986907005, 0.01035869587212801, 0.009196851402521133, 0.002214358886703849, -0.03216613456606865, -0.00031187868444249034, -0.007917515933513641, -0.0033647813834249973, 0.0025178745854645967, -0.017349347472190857, -0.02505146525800228, -0.03665686026215553, 0.0151170389726758, 0.009157688356935978, 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-96,"Where to build images
-Building Singularity images is a rather heavy task, which can take 20 minutes
-if you have a lot of steps in your recipe. This makes it a bad task to run on
-the login nodes of our clusters, especially if it needs to be run regularly.
-On the Mila cluster, we are lucky to have unrestricted internet access on the compute
-nodes, which means that anyone can request an interactive CPU node (no need for GPU)
-and build their images there without problem.
-
-Warning
-Do not build Singularity images from scratch every time your run a
-job in a large batch.  This will be a colossal waste of GPU time as well as
-internet bandwidth.  If you setup your workflow properly (e.g. using bind
-paths for your code and data), you can spend months reusing the same
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-97,"Building the containers
-Building a container is like creating a new environment except that containers
-are much more powerful since they are self-contained systems.  With
-singularity, there are two ways to build containers.
-The first one is by yourself, it’s like when you got a new Linux laptop and you
-don’t really know what you need, if you see that something is missing, you
-install it. Here you can get a vanilla container with Ubuntu called a sandbox,
-you log in and you install each packages by yourself.  This procedure can take
-time but will allow you to understand how things work and what you need. This is
-recommended if you need to figure out how things will be compiled or if you want
-to install packages on the fly. We’ll refer to this procedure as singularity
-sandboxes.
-The second way is more like you know what you want, so you write a list of
-everything you need, you send it to singularity and it will install everything
-for you. Those lists are called singularity recipes.
-
-First way: Build and use a sandbox
-You might ask yourself: On which machine should I build a container?
-First of all, you need to choose where you’ll build your container. This
-operation requires memory and high cpu usage.
-
-Warning
-Do NOT build containers on any login nodes !
-
-
-(Recommended for beginner) If you need to use apt-get, you should build
-the container on your laptop with sudo privileges. You’ll only need to
-install singularity on your laptop. Windows/Mac users can look there and
-Ubuntu/Debian users can use directly:
-
-sudo apt-get install singularity-container
-
-
-If you can’t install singularity on your laptop and you don’t need
-apt-get, you can reserve a cpu node on the Mila cluster to build your
-container.
-
-In this case, in order to avoid too much I/O over the network, you should define
-the singularity cache locally:
-
-export SINGULARITY_CACHEDIR=$SLURM_TMPDIR
-
-
-If you can’t install singularity on your laptop and you want to use
-apt-get, you can use singularity-hub to build your containers and read
-Recipe_section.
-
-
-Download containers from the web
-Hopefully, you may not need to create containers from scratch as many have been
-already built for the most common deep learning software. You can find most of
-them on dockerhub.
-Go on dockerhub and select the container you want to pull.
-For example, if you want to get the latest PyTorch version with GPU support
-(Replace runtime by devel if you need the full Cuda toolkit):
-singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
-Or the latest TensorFlow:
-singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
-Currently the pulled image pytorch.simg or tensorflow.simg is read-only
-meaning that you won’t be able to install anything on it.  Starting now, PyTorch
-will be taken as example. If you use TensorFlow, simply replace every
-pytorch occurrences by tensorflow.
-
-
-How to add or install stuff in a container
-The first step is to transform your read only container
-pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
-allow you to add packages.
-
-Warning
-Depending on the version of singularity you are using, singularity
-will build a container with the extension .simg or .sif. If you’re using
-.sif files, replace every occurences of .simg by .sif.
-
-
-Tip
-If you want to use apt-get you have to put sudo ahead of the
-following commands
-
-This command will create a writable image in the folder pytorch.
-singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
-Then you’ll need the following command to log inside the container.
-singularity shell --writable -H $HOME:/home pytorch
-Once you get into the container, you can use pip and install anything you need
-(Or with apt-get if you built the container with sudo).
-
-Warning
-Singularity mounts your home folder, so if you install things into
-the $HOME of your container, they will be installed in your real
-$HOME!
-
-You should install your stuff in /usr/local instead.
-
-
-Creating useful directories
-One of the benefits of containers is that you’ll be able to use them across
-different clusters. However for each cluster the datasets and experiments
-folder location can be different. In order to be invariant to those locations,
-we will create some useful mount points inside the container:
-mkdir /dataset
-mkdir /tmp_log
-mkdir /final_log
-From now, you won’t need to worry anymore when you write your code to specify
-where to pick up your dataset. Your dataset will always be in /dataset
-independently of the cluster you are using.
-
-
-Testing
-If you have some code that you want to test before finalizing your container,
-you have two choices.  You can either log into your container and run Python
-code inside it with:
-singularity shell --nv pytorch
-Or you can execute your command directly with
-singularity exec --nv pytorch Python YOUR_CODE.py
-
-Tip
-—nv allows the container to use gpus. You don’t need this if you
-don’t plan to use a gpu.
-
-
-Warning
-Don’t forget to clear the cache of the packages you installed in
-the containers.
-
-
-
-Creating a new image from the sandbox
-Once everything you need is installed inside the container, you need to convert
-it back to a read-only singularity image with:
-singularity build pytorch_final.simg pytorch
-
-
-
-Second way: Use recipes
-A singularity recipe is a file including specifics about installation software,
-environment variables, files to add, and container metadata.  It is a starting
-point for designing any custom container. Instead of pulling a container and
-installing your packages manually, you can specify in this file the packages
-you want and then build your container from this file.
-Here is a toy example of a singularity recipe installing some stuff:
-################# Header: Define the base system you want to use ################
-# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
-Bootstrap: docker
-# Select the docker image you want to use (Here we choose tensorflow)
-From: tensorflow/tensorflow:latest-gpu-py3
-
-################# Section: Defining the system #################################
-# Commands in the %post section are executed within the container.
-%post
-        echo ""Installing Tools with apt-get""
-        apt-get update
-        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
-        apt-get clean
-        echo ""Installing things with pip""
-        pip install tqdm
-        echo ""Creating mount points""
-        mkdir /dataset
-        mkdir /tmp_log
-        mkdir /final_log
-
-
-# Environment variables that should be sourced at runtime.
-%environment
-        # use bash as default shell
-        SHELL=/bin/bash
-        export SHELL
-
-
-A recipe file contains two parts: the header and sections. In the
-header you specify which base system you want to use, it can be any docker
-or singularity container. In sections, you can list the things you want to
-install in the subsection post or list the environment’s variable you need
-to source at each runtime in the subsection environment. For a more detailed
-description, please look at the singularity documentation.
-In order to build a singularity container from a singularity recipe file, you
-should use:
-sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>
-
-Warning
-You always need to use sudo when you build a container from a
-recipe. As there is no access to sudo on the cluster, a personal computer or
-the use singularity hub is needed to build a container
-
-
-Build recipe on singularity hub
-Singularity hub allows users to build containers from recipes directly on
-singularity-hub’s cloud meaning that you don’t need to build containers by
-yourself.  You need to register on singularity-hub and link your
-singularity-hub account to your GitHub account, then:
-
-
-Create a new github repository.
-Add a collection on singularity-hub and select the github repository your created.
-Clone the github repository on your computer.
-$ git clone <url>
-
-
-
-Write the singularity recipe and save it as a file named Singularity.
-Git add Singularity, commit and push on the master branch
-$ git add Singularity
-$ git commit
-$ git push origin master
-
-
-
-
-
-At this point, robots from singularity-hub will build the container for you, you
-will be able to download your container from the website or directly with:
-singularity pull shub://<github_username>/<repository_name>
-
-
-Example: Recipe with OpenAI gym, MuJoCo and Miniworld
-Here is an example on how you can use a singularity recipe to install complex
-environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
-container. In order to use MuJoCo, you’ll need to copy the key stored on the
-Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the pytorch container
-From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        export PATH=$PATH:/opt/conda/bin
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libhdf5-dev \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        libhdf5-dev \
-        openjdk-8-jdk \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-        pip install h5py
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
-        pip install --upgrade minerl
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/sh
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/sh ""$@""
-
-
-Here is the same recipe but written for TensorFlow:
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the tensorflow container
-From: tensorflow/tensorflow:latest-gpu-py3
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-setuptools \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
-
-        # Then install miniworld
-        cd /usr/local/
-        git clone https://github.com/maximecb/gym-miniworld.git
-        cd gym-miniworld
-        pip install -e .
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/bash
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/bash ""$@""
-
-
-Keep in mind that those environment variables are sourced at runtime and not at
-build time. This is why, you should also define them in the %post section
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-98,"First way: Build and use a sandbox
-You might ask yourself: On which machine should I build a container?
-First of all, you need to choose where you’ll build your container. This
-operation requires memory and high cpu usage.
-
-Warning
-Do NOT build containers on any login nodes !
-
-
-(Recommended for beginner) If you need to use apt-get, you should build
-the container on your laptop with sudo privileges. You’ll only need to
-install singularity on your laptop. Windows/Mac users can look there and
-Ubuntu/Debian users can use directly:
-
-sudo apt-get install singularity-container
-
-
-If you can’t install singularity on your laptop and you don’t need
-apt-get, you can reserve a cpu node on the Mila cluster to build your
-container.
-
-In this case, in order to avoid too much I/O over the network, you should define
-the singularity cache locally:
-
-export SINGULARITY_CACHEDIR=$SLURM_TMPDIR
-
-
-If you can’t install singularity on your laptop and you want to use
-apt-get, you can use singularity-hub to build your containers and read
-Recipe_section.
-
-
-Download containers from the web
-Hopefully, you may not need to create containers from scratch as many have been
-already built for the most common deep learning software. You can find most of
-them on dockerhub.
-Go on dockerhub and select the container you want to pull.
-For example, if you want to get the latest PyTorch version with GPU support
-(Replace runtime by devel if you need the full Cuda toolkit):
-singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
-Or the latest TensorFlow:
-singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
-Currently the pulled image pytorch.simg or tensorflow.simg is read-only
-meaning that you won’t be able to install anything on it.  Starting now, PyTorch
-will be taken as example. If you use TensorFlow, simply replace every
-pytorch occurrences by tensorflow.
-
-
-How to add or install stuff in a container
-The first step is to transform your read only container
-pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
-allow you to add packages.
-
-Warning
-Depending on the version of singularity you are using, singularity
-will build a container with the extension .simg or .sif. If you’re using
-.sif files, replace every occurences of .simg by .sif.
-
-
-Tip
-If you want to use apt-get you have to put sudo ahead of the
-following commands
-
-This command will create a writable image in the folder pytorch.
-singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
-Then you’ll need the following command to log inside the container.
-singularity shell --writable -H $HOME:/home pytorch
-Once you get into the container, you can use pip and install anything you need
-(Or with apt-get if you built the container with sudo).
-
-Warning
-Singularity mounts your home folder, so if you install things into
-the $HOME of your container, they will be installed in your real
-$HOME!
-
-You should install your stuff in /usr/local instead.
-
-
-Creating useful directories
-One of the benefits of containers is that you’ll be able to use them across
-different clusters. However for each cluster the datasets and experiments
-folder location can be different. In order to be invariant to those locations,
-we will create some useful mount points inside the container:
-mkdir /dataset
-mkdir /tmp_log
-mkdir /final_log
-From now, you won’t need to worry anymore when you write your code to specify
-where to pick up your dataset. Your dataset will always be in /dataset
-independently of the cluster you are using.
-
-
-Testing
-If you have some code that you want to test before finalizing your container,
-you have two choices.  You can either log into your container and run Python
-code inside it with:
-singularity shell --nv pytorch
-Or you can execute your command directly with
-singularity exec --nv pytorch Python YOUR_CODE.py
-
-Tip
-—nv allows the container to use gpus. You don’t need this if you
-don’t plan to use a gpu.
-
-
-Warning
-Don’t forget to clear the cache of the packages you installed in
-the containers.
-
-
-
-Creating a new image from the sandbox
-Once everything you need is installed inside the container, you need to convert
-it back to a read-only singularity image with:
-singularity build pytorch_final.simg pytorch",994,"[0.014942701905965805, -0.010020559653639793, 0.015390169806778431, -0.02768874540925026, -0.008203570730984211, 0.0013627419248223305, -0.023431025445461273, -0.00736287422478199, -0.010095138102769852, -0.013308768160641193, 0.05231301859021187, 0.025966674089431763, -0.02358018048107624, 0.001830548862926662, 0.014522354118525982, -0.00701032392680645, 0.023620858788490295, -0.012034163810312748, 0.004786546342074871, -0.0263056643307209, -0.02138352207839489, -0.004311959259212017, 0.009939202107489109, -0.025993792340159416, -0.033438023179769516, 0.026264986023306847, 0.020298752933740616, -0.017939377576112747, -0.003230579663068056, 0.008800193667411804, 0.0027949768118560314, -0.020814018324017525, -0.02173607237637043, -0.02358018048107624, -0.028339607641100883, 0.0188478734344244, 0.011146008037030697, -0.01113244891166687, 0.0018797024386003613, -0.022495411336421967, 0.03140408173203468, 0.004691628739237785, 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-99,"Download containers from the web
-Hopefully, you may not need to create containers from scratch as many have been
-already built for the most common deep learning software. You can find most of
-them on dockerhub.
-Go on dockerhub and select the container you want to pull.
-For example, if you want to get the latest PyTorch version with GPU support
-(Replace runtime by devel if you need the full Cuda toolkit):
-singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
-Or the latest TensorFlow:
-singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
-Currently the pulled image pytorch.simg or tensorflow.simg is read-only
-meaning that you won’t be able to install anything on it.  Starting now, PyTorch
-will be taken as example. If you use TensorFlow, simply replace every
-pytorch occurrences by tensorflow.",196,"[-0.012158196419477463, -0.024535460397601128, 0.020222585648298264, -0.026110000908374786, 0.0012348168529570103, 0.0062057459726929665, -0.03683057427406311, -0.006380314938724041, -0.002052037976682186, -0.020756561309099197, 0.04964596778154373, 0.039486754685640335, -0.015060828998684883, -0.007263427134603262, -0.009632084518671036, -0.0018312599277123809, 0.006886906456202269, -0.019483236595988274, -0.011672142893075943, -0.0484137199819088, -0.010179751552641392, -0.004744160920381546, 0.01039197202771902, -0.010159214027225971, -0.04813988506793976, 0.019154636189341545, 0.01203497126698494, -0.019811835139989853, -0.020455343648791313, -0.009967530146241188, 0.019893985241651535, -0.010166059248149395, -0.02940969169139862, -0.021975118666887283, -0.018702810630202293, 0.003789167618378997, 0.0036009072791785, -0.014691154472529888, 0.0037857447750866413, -0.03485897183418274, 0.003902123775333166, -0.0029830709099769592, 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-100,"How to add or install stuff in a container
-The first step is to transform your read only container
-pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
-allow you to add packages.
-
-Warning
-Depending on the version of singularity you are using, singularity
-will build a container with the extension .simg or .sif. If you’re using
-.sif files, replace every occurences of .simg by .sif.
-
-
-Tip
-If you want to use apt-get you have to put sudo ahead of the
-following commands
-
-This command will create a writable image in the folder pytorch.
-singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
-Then you’ll need the following command to log inside the container.
-singularity shell --writable -H $HOME:/home pytorch
-Once you get into the container, you can use pip and install anything you need
-(Or with apt-get if you built the container with sudo).
-
-Warning
-Singularity mounts your home folder, so if you install things into
-the $HOME of your container, they will be installed in your real
-$HOME!
-
-You should install your stuff in /usr/local instead.",275,"[0.009190776385366917, -0.020408624783158302, 0.0005110774654895067, -0.0054744756780564785, 0.004884969908744097, -0.0013858559541404247, -0.01933303475379944, -0.01960882730782032, -0.009011510759592056, -0.0037197477649897337, 0.05518602579832077, 0.029013343155384064, -0.015389206819236279, 0.007508443668484688, 0.005829558242112398, -0.006543170660734177, 0.008177239447832108, -0.0010454249568283558, -0.023594025522470474, -0.034501608461141586, -0.01940198242664337, 0.0021115343552082777, 0.0038059328217059374, -0.022187484428286552, -0.02764817140996456, 0.00802555400878191, 0.010680054314434528, -0.014437722973525524, -0.00012529155355878174, -0.00558823999017477, 0.008687455207109451, -0.012327912263572216, -0.030337145552039146, -0.033067487180233, -0.02057410031557083, 0.014086088165640831, 0.008356504142284393, -0.0154857337474823, 0.0018667686963453889, -0.02078094519674778, 0.010321523994207382, 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-101,"Creating useful directories
-One of the benefits of containers is that you’ll be able to use them across
-different clusters. However for each cluster the datasets and experiments
-folder location can be different. In order to be invariant to those locations,
-we will create some useful mount points inside the container:
-mkdir /dataset
-mkdir /tmp_log
-mkdir /final_log
-From now, you won’t need to worry anymore when you write your code to specify
-where to pick up your dataset. Your dataset will always be in /dataset
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-102,"Testing
-If you have some code that you want to test before finalizing your container,
-you have two choices.  You can either log into your container and run Python
-code inside it with:
-singularity shell --nv pytorch
-Or you can execute your command directly with
-singularity exec --nv pytorch Python YOUR_CODE.py
-
-Tip
-—nv allows the container to use gpus. You don’t need this if you
-don’t plan to use a gpu.
-
-
-Warning
-Don’t forget to clear the cache of the packages you installed in
-the containers.",118,"[0.009339646436274052, -0.005070189945399761, 0.020994016900658607, -0.023766305297613144, 0.009265628643333912, -0.003667224198579788, -0.014561234973371029, -0.019204143434762955, -0.01419787760823965, 0.0017730524996295571, 0.04968584328889847, 0.03741241618990898, -0.024291153997182846, 0.0074757542461156845, 0.012374358251690865, -0.01193025428801775, 0.004501601681113243, -0.02278389222919941, 0.016014667227864265, -0.027372969314455986, -0.017172029241919518, -0.0007889578700996935, 0.004881781991571188, -0.01273098774254322, -0.03275604918599129, -0.00023256594431586564, 0.003295455127954483, -0.029068639501929283, 0.0004081469087395817, -0.001228856504894793, 0.017346980050206184, -0.013450974598526955, -0.008155368268489838, -0.035716746002435684, -0.013847976922988892, 0.015893548727035522, 0.005884381011128426, -0.01906956546008587, 0.004975985735654831, -0.007953503169119358, 0.015166832134127617, 0.010550839826464653, 0.008114995434880257, 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-103,"Creating a new image from the sandbox
-Once everything you need is installed inside the container, you need to convert
-it back to a read-only singularity image with:
-singularity build pytorch_final.simg pytorch",46,"[0.0029666260816156864, -0.009213080629706383, 0.009522764012217522, -0.01793348416686058, -0.008115112781524658, -0.0021607454400509596, -0.042933378368616104, -0.016385067254304886, 0.0009308097651228309, -0.0052012731321156025, 0.060866862535476685, 0.04023068770766258, -0.01190873421728611, -0.009016009978950024, 0.016314683482050896, 0.002225849311798811, 0.003856965806335211, 0.005929733160883188, -0.020143497735261917, -0.01914406381547451, -0.016483603045344353, 0.010177322663366795, 0.004117381293326616, -0.02184675633907318, -0.0037021241150796413, 0.01741265319287777, 0.0019214446656405926, -0.029222851619124413, -0.022480199113488197, -0.001028465572744608, -0.001055738888680935, -0.004427064675837755, -0.02172006666660309, -0.03414963185787201, -0.03203815594315529, -0.004226474557071924, 0.009199004620313644, -0.039977312088012695, 0.020551715046167374, -0.018806228414177895, 0.01130344346165657, -0.006158476695418358, 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-104,"Second way: Use recipes
-A singularity recipe is a file including specifics about installation software,
-environment variables, files to add, and container metadata.  It is a starting
-point for designing any custom container. Instead of pulling a container and
-installing your packages manually, you can specify in this file the packages
-you want and then build your container from this file.
-Here is a toy example of a singularity recipe installing some stuff:
-################# Header: Define the base system you want to use ################
-# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
-Bootstrap: docker
-# Select the docker image you want to use (Here we choose tensorflow)
-From: tensorflow/tensorflow:latest-gpu-py3
-
-################# Section: Defining the system #################################
-# Commands in the %post section are executed within the container.
-%post
-        echo ""Installing Tools with apt-get""
-        apt-get update
-        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
-        apt-get clean
-        echo ""Installing things with pip""
-        pip install tqdm
-        echo ""Creating mount points""
-        mkdir /dataset
-        mkdir /tmp_log
-        mkdir /final_log
-
-
-# Environment variables that should be sourced at runtime.
-%environment
-        # use bash as default shell
-        SHELL=/bin/bash
-        export SHELL
-
-
-A recipe file contains two parts: the header and sections. In the
-header you specify which base system you want to use, it can be any docker
-or singularity container. In sections, you can list the things you want to
-install in the subsection post or list the environment’s variable you need
-to source at each runtime in the subsection environment. For a more detailed
-description, please look at the singularity documentation.
-In order to build a singularity container from a singularity recipe file, you
-should use:
-sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>
-
-Warning
-You always need to use sudo when you build a container from a
-recipe. As there is no access to sudo on the cluster, a personal computer or
-the use singularity hub is needed to build a container
-
-
-Build recipe on singularity hub
-Singularity hub allows users to build containers from recipes directly on
-singularity-hub’s cloud meaning that you don’t need to build containers by
-yourself.  You need to register on singularity-hub and link your
-singularity-hub account to your GitHub account, then:
-
-
-Create a new github repository.
-Add a collection on singularity-hub and select the github repository your created.
-Clone the github repository on your computer.
-$ git clone <url>
-
-
-
-Write the singularity recipe and save it as a file named Singularity.
-Git add Singularity, commit and push on the master branch
-$ git add Singularity
-$ git commit
-$ git push origin master
-
-
-
-
-
-At this point, robots from singularity-hub will build the container for you, you
-will be able to download your container from the website or directly with:
-singularity pull shub://<github_username>/<repository_name>
-
-
-Example: Recipe with OpenAI gym, MuJoCo and Miniworld
-Here is an example on how you can use a singularity recipe to install complex
-environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
-container. In order to use MuJoCo, you’ll need to copy the key stored on the
-Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the pytorch container
-From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        export PATH=$PATH:/opt/conda/bin
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libhdf5-dev \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        libhdf5-dev \
-        openjdk-8-jdk \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-        pip install h5py
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
-        pip install --upgrade minerl
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/sh
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/sh ""$@""
-
-
-Here is the same recipe but written for TensorFlow:
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the tensorflow container
-From: tensorflow/tensorflow:latest-gpu-py3
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-setuptools \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
-
-        # Then install miniworld
-        cd /usr/local/
-        git clone https://github.com/maximecb/gym-miniworld.git
-        cd gym-miniworld
-        pip install -e .
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/bash
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/bash ""$@""
-
-
-Keep in mind that those environment variables are sourced at runtime and not at
-build time. This is why, you should also define them in the %post section
-since they are required to install MuJoCo.",2285,"[-0.0020977051462978125, -0.013706615194678307, -0.004519459791481495, -0.008894131518900394, 0.004881430417299271, -0.011017692275345325, -0.012141523882746696, -0.01603701524436474, -0.009859385900199413, -0.019594669342041016, 0.05609508231282234, 0.004991745110601187, -0.009969701059162617, 0.013927244581282139, -0.016050804406404495, -0.007080832030624151, 0.008445978164672852, -7.530278526246548e-05, 0.0012212194269523025, -0.02011866308748722, -0.0023700448218733072, 0.006708519533276558, -0.0007855620933696628, -0.01991182379424572, -0.019677404314279556, 0.020435819402337074, 0.0204909760504961, -0.013534248806536198, -0.0023252293467521667, 0.016712693497538567, 0.02555166743695736, -0.02036687172949314, -0.028709430247545242, -0.009631861932575703, -0.030998462811112404, 0.01828468032181263, 0.0039782277308404446, -0.008108139038085938, 0.0001556688512209803, -0.002704436657950282, 0.022476643323898315, 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-105,"Build recipe on singularity hub
-Singularity hub allows users to build containers from recipes directly on
-singularity-hub’s cloud meaning that you don’t need to build containers by
-yourself.  You need to register on singularity-hub and link your
-singularity-hub account to your GitHub account, then:
-
-
-Create a new github repository.
-Add a collection on singularity-hub and select the github repository your created.
-Clone the github repository on your computer.
-$ git clone <url>
-
-
-
-Write the singularity recipe and save it as a file named Singularity.
-Git add Singularity, commit and push on the master branch
-$ git add Singularity
-$ git commit
-$ git push origin master
-
-
-
-
-
-At this point, robots from singularity-hub will build the container for you, you
-will be able to download your container from the website or directly with:
-singularity pull shub://<github_username>/<repository_name>",194,"[0.013846042566001415, -0.031613562256097794, 0.0012137109879404306, -0.00784295704215765, 0.002830313751474023, -0.01638425886631012, -0.02323341742157936, -0.011656997725367546, -0.0036025226581841707, -0.033359427005052567, 0.043646592646837234, -0.0020564261358231306, -0.030378028750419617, -0.012744804844260216, -0.005694201681762934, -0.011186957359313965, 0.014812982641160488, -0.020372886210680008, 0.005378603469580412, -0.039295364171266556, -0.025529898703098297, 0.004284081049263477, -0.0007503856322728097, -0.010367744602262974, -0.04711146280169487, 0.02025201916694641, 0.02465696819126606, -0.030270591378211975, 0.016008228063583374, 0.013846042566001415, 0.012476210482418537, -0.011442122049629688, -0.013241704553365707, -0.007305768318474293, -0.01494727935642004, 0.011052660644054413, 0.005378603469580412, -0.0037099604960530996, -0.008138410747051239, -0.013564018532633781, 0.03301025554537773, 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-106,"Example: Recipe with OpenAI gym, MuJoCo and Miniworld
-Here is an example on how you can use a singularity recipe to install complex
-environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
-container. In order to use MuJoCo, you’ll need to copy the key stored on the
-Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the pytorch container
-From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        export PATH=$PATH:/opt/conda/bin
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libhdf5-dev \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        libhdf5-dev \
-        openjdk-8-jdk \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-        pip install h5py
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
-        pip install --upgrade minerl
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/sh
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/sh ""$@""
-
-
-Here is the same recipe but written for TensorFlow:
-#This is a dockerfile that sets up a full Gym install with test dependencies
-Bootstrap: docker
-
-# Here we ll build our container upon the tensorflow container
-From: tensorflow/tensorflow:latest-gpu-py3
-
-# Now we'll copy the mjkey file located in the current directory inside the container's root
-# directory
-%files
-        mjkey.txt
-
-# Then we put everything we need to install
-%post
-        apt -y update && \
-        apt install -y keyboard-configuration && \
-        apt install -y \
-        python3-setuptools \
-        python3-dev \
-        python-pyglet \
-        python3-opengl \
-        libjpeg-dev \
-        libboost-all-dev \
-        libsdl2-dev \
-        libosmesa6-dev \
-        patchelf \
-        ffmpeg \
-        xvfb \
-        wget \
-        git \
-        unzip && \
-        apt clean && \
-        rm -rf /var/lib/apt/lists/*
-
-        # Download Gym and MuJoCo
-        mkdir /Gym && cd /Gym
-        git clone https://github.com/openai/gym.git || true && \
-        mkdir /Gym/.mujoco && cd /Gym/.mujoco
-        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
-        unzip mjpro150_linux.zip && \
-        wget https://www.roboti.us/download/mujoco200_linux.zip && \
-        unzip mujoco200_linux.zip && \
-        mv mujoco200_linux mujoco200
-
-        # Export global environment variables
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
-
-        # Install Python dependencies
-        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
-        pip install -r requirements.txt
-        # Install Gym and MuJoCo
-        cd /Gym/gym
-        pip install -e '.[all]'
-        # Change permission to use mujoco_py as non sudoer user
-        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
-
-        # Then install miniworld
-        cd /usr/local/
-        git clone https://github.com/maximecb/gym-miniworld.git
-        cd gym-miniworld
-        pip install -e .
-
-# Export global environment variables
-%environment
-        export SHELL=/bin/bash
-        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
-        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
-        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
-        export PATH=/Gym/gym/.tox/py3/bin:$PATH
-
-%runscript
-        exec /bin/bash ""$@""
-
-
-Keep in mind that those environment variables are sourced at runtime and not at
-build time. This is why, you should also define them in the %post section
-since they are required to install MuJoCo.",1628,"[-0.0014408923452720046, -0.017131298780441284, 0.005926522426307201, -0.018661636859178543, -0.010960347019135952, 0.00708136148750782, -0.018293222412467003, -0.011165808886289597, -0.007481658831238747, -0.024017823860049248, 0.04602353647351265, 0.0035601642448455095, 0.016819562762975693, -0.002228202298283577, -0.008997827768325806, 0.013829732313752174, 0.002339789643883705, -0.011746770702302456, -0.006968003232032061, -0.02801370993256569, -0.01069820486009121, 0.015473430044949055, 0.003595588728785515, -0.03650142624974251, -0.007198262494057417, 0.01802399568259716, 0.01544509083032608, -0.024570446461439133, -0.009415837936103344, 0.013482572510838509, 0.030266709625720978, -0.018576618283987045, -0.02323848567903042, -0.022544164210557938, -0.013432977721095085, -0.004488286562263966, 0.023677749559283257, -0.014467374421656132, 0.021892353892326355, -0.0066208429634571075, 0.015941035002470016, 0.018845845013856888, 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-107,"Using containers on clusters
-
-How to use containers on clusters
-On every cluster with Slurm, datasets and intermediate results should go in
-$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
-In order to use the container you built, you need to copy it on the cluster you
-want to use.
-
-Warning
-You should always store your container in $SCRATCH !
-
-Then reserve a node with srun/sbatch, copy the container and your dataset on the
-node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
-<YOUR_CODE> within the container <YOUR_CONTAINER> with:
-singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
-Remember that /dataset, /tmp_log and /final_log were created in the
-previous section. Now each time, we’ll use singularity, we are explicitly
-telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
-/dataset inside the container with the option -B such that each dataset
-downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
-This will allow us to have code and scripts that are invariant to the cluster
-environment. The option -H specify what will be the container’s home. For
-example, if you have your code in $HOME/Project12345/Version35/ you can
-specify -H $HOME/Project12345/Version35:/home, thus the container will only
-have access to the code inside Version35.
-If you want to run multiple commands inside the container you can use:
-singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
-   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
-   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'
-
-Example: Interactive case (srun/salloc)
-Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
-<YOUR_DATASET> to $SLURM_TMPDIR
-0. Get an interactive session
-srun --gres=gpu:1
-1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-Then use singularity shell to get a shell inside the container
-3. Get a shell in your environment
-singularity shell --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>
-4. Execute your code
-python <YOUR_CODE>
-or use singularity exec to execute <YOUR_CODE>.
-3. Execute your code
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python <YOUR_CODE>
-You can create also the following alias to make your life easier.
-alias my_env='singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>'
-This will allow you to run any code with:
-my_env python <YOUR_CODE>
-
-
-Example: sbatch case
-You can also create a sbatch script:
-:linenos:
-
-#!/bin/bash
-#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
-#SBATCH --gres=gpu:1              # Ask for 1 GPU
-#SBATCH --mem=10G                 # Ask for 10 GB of RAM
-#SBATCH --time=0:10:00            # The job will run for 10 minutes
-
-# 1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-# 2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-# 3. Executing your code with singularity
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python ""<YOUR_CODE>""
-# 4. Copy whatever you want to save on $SCRATCH
-rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
-
-
-
-
-Issue with PyBullet and OpenGL libraries
-If you are running certain gym environments that require pyglet, you may
-encounter a problem when running your singularity instance with the Nvidia
-drivers using the --nv flag. This happens because the --nv flag also
-provides the OpenGL libraries:
-libGL.so.1 => /.singularity.d/libs/libGL.so.1
-libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
-
-
-If you don’t experience those problems with pyglet, you probably don��t need
-to address this. Otherwise, you can resolve those problems by apt-get install
--y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
-sure that your LD_LIBRARY_PATH points to those libraries before the ones in
-/.singularity.d/libs.
-%environment
-        # ...
-        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH
-
-
-
-
-Mila cluster
-On the Mila cluster $SCRATCH is not yet defined, you should add the
-experiment results you want to keep in /network/scratch/<u>/<username>/. In
-order to use the sbatch script above and to match other cluster environment’s
-names, you can define $SCRATCH as an alias for
-/network/scratch/<u>/<username> with:
-echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
-Then, you can follow the general procedure explained above.
-
-
-Digital Research Alliance of Canada
-Using singularity on Digital Research Alliance of Canada is similar except that
-you need to add Yoshua’s account name and load singularity. Here is an example
-of a sbatch script using singularity on compute Canada cluster:
-
-Warning
-You should use singularity/2.6 or singularity/3.4. There is a bug
-in singularity/3.2 which makes gpu unusable.
-
- 1#!/bin/bash
- 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
- 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
- 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
- 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
- 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
- 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
- 8
- 9# 1. You have to load singularity
-10module load singularity
-11# 2. Then you copy the container to the local disk
-12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-13# 3. Copy your dataset on the compute node
-14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-15# 4. Executing your code with singularity
-16singularity exec --nv \
-17        -H $HOME:/home \
-18        -B $SLURM_TMPDIR:/dataset/ \
-19        -B $SLURM_TMPDIR:/tmp_log/ \
-20        -B $SCRATCH:/final_log/ \
-21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-22        python ""<YOUR_CODE>""
-23# 5. Copy whatever you want to save on $SCRATCH
-24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",1879,"[0.0014658313011750579, -0.009101789444684982, 0.014303785748779774, -0.033352773636579514, -0.005989454686641693, 0.007213253527879715, -0.008045027032494545, -0.010928965173661709, 0.0006089165108278394, -0.021571582183241844, 0.03496177867054939, 0.0076836831867694855, -0.009299506433308125, 0.02038528211414814, -0.0003104238712694496, -0.013478829525411129, 0.012858408503234386, -0.015667350962758064, 0.009667668491601944, -0.02712128683924675, -0.013874263502657413, -0.0059178676456213, 0.012783411890268326, -0.011413030326366425, -0.029889322817325592, 0.008760898374021053, 0.018844453617930412, -0.014153794385492802, 0.004441809840500355, 0.007751861121505499, -0.00383161474019289, -0.0018766048597171903, -0.021803388372063637, -0.01150166243314743, -0.033625487238168716, 0.0007133143953979015, 0.001997621264308691, -0.018817182630300522, 0.008324557915329933, -0.015203738585114479, 0.019185345619916916, -0.00809275172650814, 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-108,"How to use containers on clusters
-On every cluster with Slurm, datasets and intermediate results should go in
-$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
-In order to use the container you built, you need to copy it on the cluster you
-want to use.
-
-Warning
-You should always store your container in $SCRATCH !
-
-Then reserve a node with srun/sbatch, copy the container and your dataset on the
-node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
-<YOUR_CODE> within the container <YOUR_CONTAINER> with:
-singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
-Remember that /dataset, /tmp_log and /final_log were created in the
-previous section. Now each time, we’ll use singularity, we are explicitly
-telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
-/dataset inside the container with the option -B such that each dataset
-downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
-This will allow us to have code and scripts that are invariant to the cluster
-environment. The option -H specify what will be the container’s home. For
-example, if you have your code in $HOME/Project12345/Version35/ you can
-specify -H $HOME/Project12345/Version35:/home, thus the container will only
-have access to the code inside Version35.
-If you want to run multiple commands inside the container you can use:
-singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
-   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
-   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'
-
-Example: Interactive case (srun/salloc)
-Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
-<YOUR_DATASET> to $SLURM_TMPDIR
-0. Get an interactive session
-srun --gres=gpu:1
-1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-Then use singularity shell to get a shell inside the container
-3. Get a shell in your environment
-singularity shell --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>
-4. Execute your code
-python <YOUR_CODE>
-or use singularity exec to execute <YOUR_CODE>.
-3. Execute your code
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python <YOUR_CODE>
-You can create also the following alias to make your life easier.
-alias my_env='singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>'
-This will allow you to run any code with:
-my_env python <YOUR_CODE>
-
-
-Example: sbatch case
-You can also create a sbatch script:
-:linenos:
-
-#!/bin/bash
-#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
-#SBATCH --gres=gpu:1              # Ask for 1 GPU
-#SBATCH --mem=10G                 # Ask for 10 GB of RAM
-#SBATCH --time=0:10:00            # The job will run for 10 minutes
-
-# 1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-# 2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-# 3. Executing your code with singularity
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python ""<YOUR_CODE>""
-# 4. Copy whatever you want to save on $SCRATCH
-rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
-
-
-
-
-Issue with PyBullet and OpenGL libraries
-If you are running certain gym environments that require pyglet, you may
-encounter a problem when running your singularity instance with the Nvidia
-drivers using the --nv flag. This happens because the --nv flag also
-provides the OpenGL libraries:
-libGL.so.1 => /.singularity.d/libs/libGL.so.1
-libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
-
-
-If you don’t experience those problems with pyglet, you probably don’t need
-to address this. Otherwise, you can resolve those problems by apt-get install
--y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
-sure that your LD_LIBRARY_PATH points to those libraries before the ones in
-/.singularity.d/libs.
-%environment
-        # ...
-        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH
-
-
-
-
-Mila cluster
-On the Mila cluster $SCRATCH is not yet defined, you should add the
-experiment results you want to keep in /network/scratch/<u>/<username>/. In
-order to use the sbatch script above and to match other cluster environment’s
-names, you can define $SCRATCH as an alias for
-/network/scratch/<u>/<username> with:
-echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
-Then, you can follow the general procedure explained above.
-
-
-Digital Research Alliance of Canada
-Using singularity on Digital Research Alliance of Canada is similar except that
-you need to add Yoshua’s account name and load singularity. Here is an example
-of a sbatch script using singularity on compute Canada cluster:
-
-Warning
-You should use singularity/2.6 or singularity/3.4. There is a bug
-in singularity/3.2 which makes gpu unusable.
-
- 1#!/bin/bash
- 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
- 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
- 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
- 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
- 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
- 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
- 8
- 9# 1. You have to load singularity
-10module load singularity
-11# 2. Then you copy the container to the local disk
-12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-13# 3. Copy your dataset on the compute node
-14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-15# 4. Executing your code with singularity
-16singularity exec --nv \
-17        -H $HOME:/home \
-18        -B $SLURM_TMPDIR:/dataset/ \
-19        -B $SLURM_TMPDIR:/tmp_log/ \
-20        -B $SCRATCH:/final_log/ \
-21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-22        python ""<YOUR_CODE>""
-23# 5. Copy whatever you want to save on $SCRATCH
-24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",1872,"[0.00385263585485518, -0.0055603645741939545, 0.015369557775557041, -0.0330343022942543, -0.006615740712732077, 0.010895309038460255, -0.008374701254069805, -0.01255522109568119, 0.001723098219372332, -0.02012387476861477, 0.03418189659714699, 0.0050138914957642555, -0.012288815341889858, 0.02429073303937912, -0.0008730762638151646, -0.016011664643883705, 0.010061937384307384, -0.01904458925127983, 0.00922173447906971, -0.027146054431796074, -0.01362084411084652, -0.00571406027302146, 0.01122319232672453, -0.01288993563503027, -0.025834519416093826, 0.008313222788274288, 0.017008977010846138, -0.012001916766166687, 0.007397880312055349, 0.00669429637491703, -0.006130745634436607, -0.002594039775431156, -0.016298562288284302, -0.01317683421075344, -0.03429119288921356, 0.0077257645316421986, 0.0013473979197442532, -0.018935294821858406, 0.007964846678078175, -0.015014350414276123, 0.016694754362106323, -0.0062639485113322735, 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-109,"Example: Interactive case (srun/salloc)
-Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
-<YOUR_DATASET> to $SLURM_TMPDIR
-0. Get an interactive session
-srun --gres=gpu:1
-1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-Then use singularity shell to get a shell inside the container
-3. Get a shell in your environment
-singularity shell --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>
-4. Execute your code
-python <YOUR_CODE>
-or use singularity exec to execute <YOUR_CODE>.
-3. Execute your code
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python <YOUR_CODE>
-You can create also the following alias to make your life easier.
-alias my_env='singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER>'
-This will allow you to run any code with:
-my_env python <YOUR_CODE>",399,"[-0.005500020459294319, -0.0020174901001155376, 0.024853238835930824, -0.014545508660376072, -0.018293773755431175, -0.01090213842689991, -0.01672733575105667, -0.013132915832102299, -0.025035057216882706, -0.02535673789680004, 0.031804315745830536, 0.014713341370224953, -0.005426593590527773, 0.015454602427780628, 0.00012838772090617567, -0.010014023631811142, 0.013202846050262451, -0.026615483686327934, 0.01267836894840002, -0.01784621924161911, -0.01868538372218609, -0.002611897885799408, 0.01843363419175148, -0.016811251640319824, -0.016433628275990486, -0.003437075763940811, 0.0026940659154206514, -0.016559502109885216, 0.017552513629198074, 0.0022482601925730705, 0.005604915786534548, -0.006951074581593275, -0.006440583150833845, -0.010167869739234447, -0.01812594197690487, 0.011041998863220215, -0.0011958086397498846, -0.0175385270267725, -0.006814710795879364, -0.005160858388990164, 0.009195838123559952, 0.006709815002977848, 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-110,"Example: sbatch case
-You can also create a sbatch script:
-:linenos:
-
-#!/bin/bash
-#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
-#SBATCH --gres=gpu:1              # Ask for 1 GPU
-#SBATCH --mem=10G                 # Ask for 10 GB of RAM
-#SBATCH --time=0:10:00            # The job will run for 10 minutes
-
-# 1. Copy your container on the compute node
-rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-# 2. Copy your dataset on the compute node
-rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-# 3. Executing your code with singularity
-singularity exec --nv \
-        -H $HOME:/home \
-        -B $SLURM_TMPDIR:/dataset/ \
-        -B $SLURM_TMPDIR:/tmp_log/ \
-        -B $SCRATCH:/final_log/ \
-        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-        python ""<YOUR_CODE>""
-# 4. Copy whatever you want to save on $SCRATCH
-rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",270,"[-0.009656386449933052, -0.0061024450697004795, 0.013622277416288853, -0.014439195394515991, -0.01073862798511982, -0.014774340204894543, -0.015570311807096004, -0.014648661017417908, -0.009425973519682884, -0.01932673715054989, 0.037955254316329956, 0.021114179864525795, -0.009265382774174213, 0.00971224345266819, 0.0006785828154534101, -0.0053902603685855865, 0.014830198138952255, -0.010864307172596455, 0.011129630729556084, -0.019117271527647972, -0.01869833841919899, -0.008818522095680237, 0.003178647719323635, 0.0002973981900140643, -0.02337641455233097, 0.011737082153558731, 0.00803651474416256, -0.02179843559861183, 0.0014505526050925255, 0.007484921254217625, 0.006818120833486319, -0.014173870906233788, 0.002462971955537796, -0.010333660058677197, -0.03276049345731735, -0.006992675829678774, 0.000928632915019989, -0.013782868161797523, 0.0026218171697109938, -0.01867040991783142, 0.019857384264469147, 0.03019104339182377, 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-111,"Issue with PyBullet and OpenGL libraries
-If you are running certain gym environments that require pyglet, you may
-encounter a problem when running your singularity instance with the Nvidia
-drivers using the --nv flag. This happens because the --nv flag also
-provides the OpenGL libraries:
-libGL.so.1 => /.singularity.d/libs/libGL.so.1
-libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
-
-
-If you don’t experience those problems with pyglet, you probably don’t need
-to address this. Otherwise, you can resolve those problems by apt-get install
--y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
-sure that your LD_LIBRARY_PATH points to those libraries before the ones in
-/.singularity.d/libs.
-%environment
-        # ...
-        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH",203,"[-0.006877453066408634, -0.009704811498522758, -0.007882969453930855, -0.04495745152235031, 0.013621770776808262, 0.011449575424194336, 0.005507567897439003, -0.01364979986101389, -0.0161583349108696, 0.008086174726486206, 0.046639151871204376, 0.005854418501257896, -0.0020443168468773365, 0.012556694447994232, -0.003832874819636345, 0.009410513564944267, -0.0007103431271389127, 0.0012551438994705677, 0.0361565537750721, -0.0005675738211721182, -0.0026153940707445145, -0.011078199371695518, 0.016102278605103493, -0.01276690699160099, -0.0002975820971187204, -0.012206340208649635, 0.007126204203814268, -0.03276512399315834, -0.004011555574834347, 0.031840190291404724, 0.02926158346235752, -3.476170604699291e-05, -0.01917138136923313, -0.0007536994526162744, -0.019325537607073784, -0.009417520835995674, -0.0017587781185284257, -0.01656474731862545, 0.020516742020845413, 0.006859935354441404, 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-112,"Mila cluster
-On the Mila cluster $SCRATCH is not yet defined, you should add the
-experiment results you want to keep in /network/scratch/<u>/<username>/. In
-order to use the sbatch script above and to match other cluster environment’s
-names, you can define $SCRATCH as an alias for
-/network/scratch/<u>/<username> with:
-echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
-Then, you can follow the general procedure explained above.",117,"[-0.005913616623729467, -0.01070469431579113, 0.006292304489761591, -0.03210731968283653, -0.022586025297641754, 0.016188906505703926, -0.01790652610361576, -0.01916431076824665, -0.01077231764793396, -0.01309854257851839, 0.025304464623332024, -0.016026610508561134, 0.0023414406459778547, -0.003061623778194189, -0.026629870757460594, -0.035867150872945786, 0.011218627914786339, -0.01119834091514349, 0.013294649310410023, -0.011306537315249443, -0.006741996388882399, 0.016702840104699135, -0.00917641818523407, -0.02453356422483921, -0.019015541300177574, -0.002420897362753749, -0.0028638269286602736, -0.010278670117259026, -0.00011738901230273768, 0.022748321294784546, -0.015012268908321857, -0.011407971382141113, 0.0035299118608236313, -0.010346293449401855, -0.04814745485782623, 0.013571902178227901, 0.00034487643279135227, -0.021449962630867958, 0.001465724897570908, 0.00028908762033097446, 0.023870859295129776, 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-113,"Digital Research Alliance of Canada
-Using singularity on Digital Research Alliance of Canada is similar except that
-you need to add Yoshua’s account name and load singularity. Here is an example
-of a sbatch script using singularity on compute Canada cluster:
-
-Warning
-You should use singularity/2.6 or singularity/3.4. There is a bug
-in singularity/3.2 which makes gpu unusable.
-
- 1#!/bin/bash
- 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
- 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
- 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
- 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
- 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
- 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
- 8
- 9# 1. You have to load singularity
-10module load singularity
-11# 2. Then you copy the container to the local disk
-12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
-13# 3. Copy your dataset on the compute node
-14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
-15# 4. Executing your code with singularity
-16singularity exec --nv \
-17        -H $HOME:/home \
-18        -B $SLURM_TMPDIR:/dataset/ \
-19        -B $SLURM_TMPDIR:/tmp_log/ \
-20        -B $SCRATCH:/final_log/ \
-21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
-22        python ""<YOUR_CODE>""
-23# 5. Copy whatever you want to save on $SCRATCH
-24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",432,"[-0.009171411395072937, -0.01838463917374611, -0.0131926154717803, -0.024573251605033875, -0.002383451908826828, 0.009422301314771175, -0.021130487322807312, -0.0148582449182868, -0.01099036168307066, -0.032113879919052124, 0.037967972457408905, 0.006003929302096367, -0.012865065596997738, -0.0037563759833574295, 0.016196323558688164, -0.001030564191751182, 0.018161624670028687, -0.029995255172252655, 0.007275800686329603, -0.014231020584702492, -0.020893534645438194, 0.004575252067297697, -0.0005218156729824841, 0.008641756139695644, -0.022552194073796272, 0.015736358240246773, 0.007415183819830418, -0.00033386622089892626, 0.0031291518826037645, 0.00604574428871274, -0.000522686867043376, -0.006833259481936693, 0.005868030712008476, -0.0001606173173058778, -0.024461744353175163, -0.012035735882818699, -0.001932198996655643, -0.0006041388842277229, -0.013826808892190456, -0.021506821736693382, 0.024712633341550827, 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-114,"Sharing Data with ACLs
-Regular permissions bits are extremely blunt tools: They control access through
-only three sets of bits owning user, owning group and all others. Therefore,
-access is either too narrow (0700 allows access only by oneself) or too wide
-(770 gives all permissions to everyone in the same group, and 777 to
-literally everyone).
-ACLs (Access Control Lists) are an expansion of the permissions bits that allow
-more fine-grained, granular control of accesses to a file. They can be used to
-permit specific users access to files and folders even if conservative default
-permissions would have denied them such access.
-As an illustrative example, to use ACLs to allow $USER (oneself) to
-share with $USER2 (another person) a “playground” folder hierarchy in
-Mila’s scratch filesystem at a location
-
-/network/scratch/${USER:0:1}/$USER/X/Y/Z/...
-
-in a safe and secure fashion that allows both users to read, write, execute,
-search and delete each others’ files:
-
-
-1. Grant oneself permissions to access any future files/folders created
-by the other (or oneself)
-(-d renders this permission a “default” / inheritable one)
-
-setfacl -Rdm user:${USER}:rwx  /network/scratch/${USER:0:1}/$USER/X/Y/Z/
-
-
-
-
-Note
-The importance of doing this seemingly-redundant step first is that files
-and folders are always owned by only one person, almost always their
-creator (the UID will be the creator’s, the GID typically as well). If that
-user is not yourself, you will not have access to those files unless the
-other person specifically gives them to you – or these files inherited a
-default ACL allowing you full access.
-This is the inherited, default ACL serving that purpose.
-
-
-2. Grant the other permission to access any future files/folders created
-by the other (or oneself)
-(-d renders this permission a “default” / inheritable one)
-
-setfacl -Rdm user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/
-
-
-
-
-3. Grant the other permission to access any existing files/folders created
-by oneself.
-Such files and folders were created before the new default ACLs were added
-above and thus did not inherit them from their parent folder at the moment of
-their creation.
-
-setfacl -Rm  user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/
-
-
-
-Note
-The purpose of granting permissions first for future files and then for
-existing files is to prevent a race condition whereby after the first
-setfacl command the other person could create files to which the
-second setfacl command does not apply.
-
-
-
-4. Grant another permission to search through one’s hierarchy down to the
-shared location in question.
-
-
-Non-recursive (!!!!)
-May also grant :rx in unlikely event others listing your folders on the
-path is not troublesome or desirable.
-
-setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/Y/
-setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/
-setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/
-
-
-
-Note
-In order to access a file, all folders from the root (/) down to the
-parent folder in question must be searchable (+x) by the concerned user.
-This is already the case for all users for folders such as /,
-/network and /network/scratch, but users must explicitly grant access
-to some or all users either through base permissions or by adding ACLs, for
-at least /network/scratch/${USER:0:1}/$USER, $HOME and subfolders.
-To bluntly allow all users to search through a folder (think twice!),
-the following command can be used:
-chmod a+x /network/scratch/${USER:0:1}/$USER/
-
-
-
-
-Note
-For more information on setfacl and path resolution/access checking,
-consider the following documentation viewing commands:
-
-man setfacl
-man path_resolution
-
-
-
-Viewing and Verifying ACLs
-getfacl /path/to/folder/or/file
-           1:  # file: somedir/
-           2:  # owner: lisa
-           3:  # group: staff
-           4:  # flags: -s-
-           5:  user::rwx
-           6:  user:joe:rwx               #effective:r-x
-           7:  group::rwx                 #effective:r-x
-           8:  group:cool:r-x
-           9:  mask::r-x
-          10:  other::r-x
-          11:  default:user::rwx
-          12:  default:user:joe:rwx       #effective:r-x
-          13:  default:group::r-x
-          14:  default:mask::r-x
-          15:  default:other::---
-
-
-
-Note
-
-man getfacl",1102,"[0.00020359196059871465, 0.01278620958328247, 0.006951528135687113, -0.06909561902284622, -0.03024287149310112, 0.021321630105376244, -0.034249983727931976, -0.005530086345970631, -0.02573486976325512, -0.013740605674684048, 0.027251074090600014, -0.022364020347595215, 0.010918028652668, 0.013693224638700485, -0.0019680203404277563, 0.02064475230872631, 0.019453449174761772, -0.008758789859712124, 0.009686112403869629, 0.008833246305584908, -0.004030803218483925, 0.010945103131234646, -0.04099167883396149, -0.0014722078340128064, -0.027887338772416115, 0.011872424744069576, 0.014620546251535416, -0.00546916713938117, 0.017436355352401733, -0.01715206727385521, 0.01903378590941429, 0.0032913151662796736, -0.014255032874643803, 0.0057906839065253735, -0.017639419063925743, 0.002873343415558338, -0.006535248830914497, -0.025843169540166855, -0.0008554034866392612, 0.02064475230872631, 0.020387539640069008, -0.0017209601355716586, -0.010972178541123867, 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-115,"Viewing and Verifying ACLs
-getfacl /path/to/folder/or/file
-           1:  # file: somedir/
-           2:  # owner: lisa
-           3:  # group: staff
-           4:  # flags: -s-
-           5:  user::rwx
-           6:  user:joe:rwx               #effective:r-x
-           7:  group::rwx                 #effective:r-x
-           8:  group:cool:r-x
-           9:  mask::r-x
-          10:  other::r-x
-          11:  default:user::rwx
-          12:  default:user:joe:rwx       #effective:r-x
-          13:  default:group::r-x
-          14:  default:mask::r-x
-          15:  default:other::---
-
-
-
-Note
-
-man getfacl",206,"[0.00094050751067698, 0.0094239367172122, -0.006409511901438236, -0.04477398842573166, -0.011419836431741714, 0.01580258458852768, -0.047160837799310684, -0.01753099262714386, -0.02731158398091793, -0.010171541944146156, 0.025569459423422813, -0.029712149873375893, -0.011316955089569092, 0.01438967976719141, -0.013559769839048386, 0.017215488478541374, 0.0188478734344244, -0.005106347147375345, 0.010281282477080822, -0.0019084501545876265, -0.00588824599981308, 0.03525402769446373, -0.03388227894902229, -0.010507620871067047, -0.01006180141121149, 0.010301858186721802, 0.0003973794518969953, -0.018532371148467064, 0.0165844839066267, -0.010109812952578068, 0.02359413541853428, -0.0045610759407281876, -0.015212730504572392, -0.008491145446896553, -0.01858724094927311, 0.0029149733018130064, -0.01011667214334011, -0.04337480291724205, -0.0070165120996534824, 0.021948033943772316, 0.014705182984471321, 0.0019667495507746935, -0.018422631546854973, 0.010185259394347668, 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-116,"Contributing datasets
-If a dataset could help the research of others at Mila, this form can be filled to request its addition
-to /network/datasets.
-
-Publicly share a Mila dataset
-Mila offers two ways to publicly share a Mila dataset:
-
-Academic Torrent
-Google Drive
-
-Note that these options are not mutually exclusive and both can be used.
-
-Academic Torrent
-Mila hosts/seeds some datasets created by the Mila community through Academic
-Torrent. The first step is to create an
-account and a torrent file.
-Then drop the dataset in /network/scratch/.transit_datasets and send the
-Academic Torrent URL to Mila’s helpdesk. If
-the dataset does not reside on the Mila cluster, only the Academic Torrent URL
-would be needed to proceed with the initial download. Then you can delete /
-stop sharing your copy.
-
-Note
-
-Avoid mentioning dataset in the name of the dataset
-Avoid capital letters, special charaters (including spaces) in files and
-directories names. Spaces can be replaced by hyphens (-).
-Multiple archives can be provided to spread the data (e.g. dataset splits,
-raw data, extra data, …)
-
-
-
-Generate a .torrent file to be uploaded to Academic Torrent
-The command line / Python utility torrentool can be used to create a
-DATASET_NAME.torrent file:
-# Install torrentool
-python3 -m pip install torrentool click
-# Change Directory to the location of the dataset to be hosted by Mila
-cd /network/scratch/.transit_datasets
-torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
-
-
-The resulting DATASET_NAME.torrent can then be used to register a new dataset
-on Academic Torrent.
-
-Warning
-
-The creation of a DATASET_NAME.torrent file requires the computation of
-checksums for the dataset content which can quickly become CPU-heavy. This
-process should not be executed on a login node
-
-
-
-
-Download a dataset from Academic Torrent
-Academic Torrent provides a Python API to easily download a dataset
-from it’s registered list:
-# Install the Python API with:
-# python3 -m pip install academictorrents
-import academictorrents as at
-mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
-
-
-
-Note
-Current needs have been evaluated to be for a download speed of about 10
-MB/s. This speed can be higher if more users also seeds the dataset.
-
-
-
-
-Google Drive
-Only a member of the staff team can upload to Mila’s Google Drive
-which requires to first drop the dataset in
-/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:
-
-directory containing the archived dataset (zip is favored) in
-/network/scratch/.transit_datasets
-the name of the dataset
-a licence in .txt format. One of the the creative common licenses can be used. It is
-recommended to at least have the Attribution option. The No Derivatives
-option is discouraged unless the dataset should not be modified by others.
-MD5 checksum of the archive
-the arXiv and GitHub URLs (those can be sent later if the article is still in
-the submission process)
-instructions to know if the dataset needs to be unziped, untared or
-else before uploading to Google Drive
-
-
-Note
-
-Avoid mentioning dataset in the name of the dataset
-Avoid capital letters, special charaters (including spaces) in files and
-directories names. Spaces can be replaced by hyphens (-).
-Multiple archives can be provided to spread the data (e.g. dataset splits,
-raw data, extra data, …)
-
-
-
-Download a dataset from Mila’s Google Drive with  gdown
-The utility gdown is a simple utility to
-download data from Google Drive from the command line shell or in a Python
-script and requires no setup.
-
-Warning
-A limitation however is that it uses a shared client id which can cause a
-quota block when too many users uses it in the same day. It is described in
-a GitHub issue.
-
-
-
-Download a dataset from Mila’s Google Drive with rclone
-Rclone is a command line program to manage files on
-cloud storage. In the context of a Google Drive remote, it allows to specify a
-client id to avoid sharing with other users which avoid quota limits. Rclone
-describes the creation of a client id in its documentaton. Once this is done, a
-remote for Mila’s Google Drive can be configured from the command line:
-rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
-    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
-    scope 'drive.readonly' \
-    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
-    config_is_local false \
-    config_refresh_token false
-
-
-The remote can then be used to download a dataset:
-rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
-
-
-Rclone is available from the conda channel conda-forge.
-
-
-
-Digital Object Identifier (DOI)
-It is recommended to get a DOI to reference the dataset. A DOI is a permanent
-id/URL which prevents losing references of online scientific data.
-https://figshare.com can be used to create a DOI:
-
-Go in My Data
-Create an item by clicking Create new item
-Check Metadata record only at the top
-Fill the metadata fields
-
-Then reference the dataset using https://doi.org like this:
-https://doi.org/10.6084/m9.figshare.2066037",1216,"[-0.007763088680803776, 0.0014167112531140447, 0.02133975923061371, -0.04080687463283539, -0.01970469020307064, 0.026776017621159554, -0.016728023067116737, -0.02147950977087021, -0.014813454821705818, -0.04061122611165047, 0.020892560482025146, 0.009488996118307114, 0.016336724162101746, 0.0001875701273093, -0.02203850820660591, 0.006194399669766426, 0.023449977859854698, -0.017901919782161713, 0.028984060510993004, -0.002943475265055895, 0.01297574769705534, -0.008035600185394287, -0.016238899901509285, -0.009160584770143032, -0.0306331068277359, 0.025560196489095688, 0.018167443573474884, -0.01097034104168415, 0.003930456470698118, 0.003098946763202548, 0.002587113995105028, -0.017049448564648628, -0.009342258796095848, -0.009943181648850441, -0.011801850982010365, -0.004908703733235598, 0.03217035159468651, -0.008175349794328213, 0.004957615863531828, -0.002538201631978154, 0.0195928905159235, 0.027670415118336678, 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-117,"Publicly share a Mila dataset
-Mila offers two ways to publicly share a Mila dataset:
-
-Academic Torrent
-Google Drive
-
-Note that these options are not mutually exclusive and both can be used.
-
-Academic Torrent
-Mila hosts/seeds some datasets created by the Mila community through Academic
-Torrent. The first step is to create an
-account and a torrent file.
-Then drop the dataset in /network/scratch/.transit_datasets and send the
-Academic Torrent URL to Mila’s helpdesk. If
-the dataset does not reside on the Mila cluster, only the Academic Torrent URL
-would be needed to proceed with the initial download. Then you can delete /
-stop sharing your copy.
-
-Note
-
-Avoid mentioning dataset in the name of the dataset
-Avoid capital letters, special charaters (including spaces) in files and
-directories names. Spaces can be replaced by hyphens (-).
-Multiple archives can be provided to spread the data (e.g. dataset splits,
-raw data, extra data, …)
-
-
-
-Generate a .torrent file to be uploaded to Academic Torrent
-The command line / Python utility torrentool can be used to create a
-DATASET_NAME.torrent file:
-# Install torrentool
-python3 -m pip install torrentool click
-# Change Directory to the location of the dataset to be hosted by Mila
-cd /network/scratch/.transit_datasets
-torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
-
-
-The resulting DATASET_NAME.torrent can then be used to register a new dataset
-on Academic Torrent.
-
-Warning
-
-The creation of a DATASET_NAME.torrent file requires the computation of
-checksums for the dataset content which can quickly become CPU-heavy. This
-process should not be executed on a login node
-
-
-
-
-Download a dataset from Academic Torrent
-Academic Torrent provides a Python API to easily download a dataset
-from it’s registered list:
-# Install the Python API with:
-# python3 -m pip install academictorrents
-import academictorrents as at
-mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
-
-
-
-Note
-Current needs have been evaluated to be for a download speed of about 10
-MB/s. This speed can be higher if more users also seeds the dataset.
-
-
-
-
-Google Drive
-Only a member of the staff team can upload to Mila’s Google Drive
-which requires to first drop the dataset in
-/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:
-
-directory containing the archived dataset (zip is favored) in
-/network/scratch/.transit_datasets
-the name of the dataset
-a licence in .txt format. One of the the creative common licenses can be used. It is
-recommended to at least have the Attribution option. The No Derivatives
-option is discouraged unless the dataset should not be modified by others.
-MD5 checksum of the archive
-the arXiv and GitHub URLs (those can be sent later if the article is still in
-the submission process)
-instructions to know if the dataset needs to be unziped, untared or
-else before uploading to Google Drive
-
-
-Note
-
-Avoid mentioning dataset in the name of the dataset
-Avoid capital letters, special charaters (including spaces) in files and
-directories names. Spaces can be replaced by hyphens (-).
-Multiple archives can be provided to spread the data (e.g. dataset splits,
-raw data, extra data, …)
-
-
-
-Download a dataset from Mila’s Google Drive with  gdown
-The utility gdown is a simple utility to
-download data from Google Drive from the command line shell or in a Python
-script and requires no setup.
-
-Warning
-A limitation however is that it uses a shared client id which can cause a
-quota block when too many users uses it in the same day. It is described in
-a GitHub issue.
-
-
-
-Download a dataset from Mila’s Google Drive with rclone
-Rclone is a command line program to manage files on
-cloud storage. In the context of a Google Drive remote, it allows to specify a
-client id to avoid sharing with other users which avoid quota limits. Rclone
-describes the creation of a client id in its documentaton. Once this is done, a
-remote for Mila’s Google Drive can be configured from the command line:
-rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
-    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
-    scope 'drive.readonly' \
-    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
-    config_is_local false \
-    config_refresh_token false
-
-
-The remote can then be used to download a dataset:
-rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
-
-
-Rclone is available from the conda channel conda-forge.
-
-
-
-Digital Object Identifier (DOI)
-It is recommended to get a DOI to reference the dataset. A DOI is a permanent
-id/URL which prevents losing references of online scientific data.
-https://figshare.com can be used to create a DOI:
-
-Go in My Data
-Create an item by clicking Create new item
-Check Metadata record only at the top
-Fill the metadata fields
-
-Then reference the dataset using https://doi.org like this:
-https://doi.org/10.6084/m9.figshare.2066037",1181,"[-0.010033246129751205, 0.0051741586066782475, 0.019506366923451424, -0.031983159482479095, -0.0144932447001338, 0.015543480403721333, -0.024827558547258377, -0.02757217362523079, -0.016019586473703384, -0.035988058894872665, 0.022363007068634033, 0.011650608852505684, 0.01262382697314024, 0.0010992460884153843, -0.01739189401268959, 0.009473120793700218, 0.019940465688705444, -0.018316101282835007, 0.035371918231248856, -0.003878868417814374, 0.020024484023451805, 0.0025818280410021544, -0.02409939654171467, -0.013709069229662418, -0.033579517155885696, 0.015641503036022186, 0.02219497039914131, -0.0049956184811890125, 0.006392431445419788, 0.0022282488644123077, 0.00012263683311175555, -0.026241876184940338, -0.00421494385227561, -0.009438113309442997, -0.014248190447688103, 0.0020619616843760014, 0.027992267161607742, -0.0035883034579455853, 0.00015709767467342317, 0.003369504353031516, 0.012546809390187263, 0.012091707438230515, 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-118,"Academic Torrent
-Mila hosts/seeds some datasets created by the Mila community through Academic
-Torrent. The first step is to create an
-account and a torrent file.
-Then drop the dataset in /network/scratch/.transit_datasets and send the
-Academic Torrent URL to Mila’s helpdesk. If
-the dataset does not reside on the Mila cluster, only the Academic Torrent URL
-would be needed to proceed with the initial download. Then you can delete /
-stop sharing your copy.
-
-Note
-
-Avoid mentioning dataset in the name of the dataset
-Avoid capital letters, special charaters (including spaces) in files and
-directories names. Spaces can be replaced by hyphens (-).
-Multiple archives can be provided to spread the data (e.g. dataset splits,
-raw data, extra data, …)
-
-
-
-Generate a .torrent file to be uploaded to Academic Torrent
-The command line / Python utility torrentool can be used to create a
-DATASET_NAME.torrent file:
-# Install torrentool
-python3 -m pip install torrentool click
-# Change Directory to the location of the dataset to be hosted by Mila
-cd /network/scratch/.transit_datasets
-torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
-
-
-The resulting DATASET_NAME.torrent can then be used to register a new dataset
-on Academic Torrent.
-
-Warning
-
-The creation of a DATASET_NAME.torrent file requires the computation of
-checksums for the dataset content which can quickly become CPU-heavy. This
-process should not be executed on a login node
-
-
-
-
-Download a dataset from Academic Torrent
-Academic Torrent provides a Python API to easily download a dataset
-from it’s registered list:
-# Install the Python API with:
-# python3 -m pip install academictorrents
-import academictorrents as at
-mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
-
-
-
-Note
-Current needs have been evaluated to be for a download speed of about 10
-MB/s. This speed can be higher if more users also seeds the dataset.",471,"[-0.004834022838622332, -0.011273466981947422, 0.021793950349092484, -0.03131281957030296, -0.007721650414168835, 0.026695456355810165, -0.025729363784193993, -0.02838612161576748, -0.011934105306863785, -0.03489305078983307, 0.024337051436305046, 0.007970277220010757, 0.009440729394555092, 0.010612829588353634, -0.004741675686091185, 0.00535969203338027, 0.019336091354489326, -0.014541138894855976, 0.0278746597468853, -0.004805608652532101, 0.006030985154211521, 0.007735857740044594, -0.022007059305906296, -0.014235682785511017, -0.023200469091534615, 0.003079425310716033, 0.017801707610487938, -0.019321884959936142, -0.007245706859976053, 0.005579904653131962, -0.0010682089487090707, -0.024493331089615822, -0.008566983044147491, -0.020799441263079643, -0.00948335137218237, 0.0021683843806385994, 0.033784884959459305, 0.0005625190096907318, 0.017730670049786568, 0.01954920031130314, 0.018071645870804787, 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-119,"Generate a .torrent file to be uploaded to Academic Torrent
-The command line / Python utility torrentool can be used to create a
-DATASET_NAME.torrent file:
-# Install torrentool
-python3 -m pip install torrentool click
-# Change Directory to the location of the dataset to be hosted by Mila
-cd /network/scratch/.transit_datasets
-torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
-
-
-The resulting DATASET_NAME.torrent can then be used to register a new dataset
-on Academic Torrent.
-
-Warning
-
-The creation of a DATASET_NAME.torrent file requires the computation of
-checksums for the dataset content which can quickly become CPU-heavy. This
-process should not be executed on a login node",158,"[0.0010754105169326067, -0.017816422507166862, 0.014752636663615704, -0.03240933641791344, 0.015841659158468246, 0.023639066144824028, -0.02764667198061943, -0.0361555740237236, -0.007514261174947023, -0.024553846567869186, 0.017192048951983452, 0.014781677164137363, -0.0057500433176755905, 0.027036819607019424, 0.0049114953726530075, -0.0008104149019345641, 0.012494727969169617, -0.011565428227186203, 0.029998963698744774, 0.007870009168982506, 0.008153154514729977, 0.005357994697988033, -0.027995159849524498, -0.014389628544449806, -0.014781677164137363, -0.0017270095413550735, 0.014542091637849808, -0.026949698105454445, 0.0012968452647328377, 0.020604321733117104, 0.017380813136696815, -0.010345721617341042, -0.006926188711076975, -0.02340674214065075, -0.012959377840161324, 0.002553759841248393, 0.031102506443858147, 0.0029349178075790405, 0.013002938590943813, 0.01958790048956871, 0.026717372238636017, 0.00951080396771431, 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-120,"Download a dataset from Academic Torrent
-Academic Torrent provides a Python API to easily download a dataset
-from it’s registered list:
-# Install the Python API with:
-# python3 -m pip install academictorrents
-import academictorrents as at
-mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
-
-
-
-Note
-Current needs have been evaluated to be for a download speed of about 10
-MB/s. This speed can be higher if more users also seeds the dataset.",143,"[-0.018178056925535202, -0.024415483698248863, 0.03505665063858032, -0.027822967618703842, -0.0023751312401145697, 0.03566306456923485, -0.005627400707453489, -0.016517629846930504, -0.004400129429996014, -0.03586520627140999, 0.015218166634440422, 0.019665220752358437, 0.0017930797766894102, 0.0036168412771075964, -0.001692010322585702, 0.011298117227852345, 0.01073501631617546, -0.020343830808997154, 0.010583411902189255, -0.003938097506761551, 0.017918163910508156, 0.008684750646352768, -0.024632060900330544, -0.022321902215480804, -0.013449451886117458, -0.0012985615758225322, 0.0050859577022492886, -0.02256735786795616, -0.015131535939872265, 0.003815370611846447, 0.020906930789351463, -0.036558251827955246, 0.0005175295518711209, -0.03670263662934303, -0.0015097244177013636, -0.003844247432425618, 0.013333943672478199, -0.002994181588292122, 0.03563418984413147, 0.01985292136669159, 0.004021118860691786, 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-121,"Google Drive
-Only a member of the staff team can upload to Mila’s Google Drive
-which requires to first drop the dataset in
-/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:
-
-directory containing the archived dataset (zip is favored) in
-/network/scratch/.transit_datasets
-the name of the dataset
-a licence in .txt format. One of the the creative common licenses can be used. It is
-recommended to at least have the Attribution option. The No Derivatives
-option is discouraged unless the dataset should not be modified by others.
-MD5 checksum of the archive
-the arXiv and GitHub URLs (those can be sent later if the article is still in
-the submission process)
-instructions to know if the dataset needs to be unziped, untared or
-else before uploading to Google Drive
-
-
-Note
-
-Avoid mentioning dataset in the name of the dataset
-Avoid capital letters, special charaters (including spaces) in files and
-directories names. Spaces can be replaced by hyphens (-).
-Multiple archives can be provided to spread the data (e.g. dataset splits,
-raw data, extra data, …)
-
-
-
-Download a dataset from Mila’s Google Drive with  gdown
-The utility gdown is a simple utility to
-download data from Google Drive from the command line shell or in a Python
-script and requires no setup.
-
-Warning
-A limitation however is that it uses a shared client id which can cause a
-quota block when too many users uses it in the same day. It is described in
-a GitHub issue.
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone",529,"[-0.012373371981084347, -0.004206245299428701, -0.01334080845117569, -0.021998662501573563, -0.014665775932371616, 0.013305756263434887, -0.007655366323888302, -0.013971745036542416, 0.007220721337944269, -0.005545233376324177, 0.035809170454740524, -0.000393678288673982, 0.01724560558795929, 0.0031126216053962708, -0.01850747875869274, 0.011847591027617455, 0.004921306855976582, -0.005867712199687958, 0.015184545889496803, -0.013474006205797195, 0.004020469728857279, 0.007346908561885357, 0.006635352037847042, -0.04304391145706177, 0.005366467870771885, -0.003666443983092904, 0.00875600054860115, -0.02159206010401249, -0.009015385992825031, 0.017091376706957817, 0.026345117017626762, -0.007739491295069456, -0.020792873576283455, -0.029219385236501694, -0.022867953404784203, 0.0016938900807872415, 0.00980055145919323, -0.0057835872285068035, 0.029387634247541428, -0.013488026335835457, 0.00863682385534048, 0.018213042989373207, -0.006617825943976641, 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+"Example: Recipe with OpenAI gym, MuJoCo and Miniworld",https://docs.mila.quebec/Userguide.html#example-recipe-with-openai-gym-mujoco-and-miniworld," https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
 
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
 
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
 
-Download a dataset from Mila’s Google Drive with rclone
-Rclone is a command line program to manage files on
-cloud storage. In the context of a Google Drive remote, it allows to specify a
-client id to avoid sharing with other users which avoid quota limits. Rclone
-describes the creation of a client id in its documentaton. Once this is done, a
-remote for Mila’s Google Drive can be configured from the command line:
-rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
-    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
-    scope 'drive.readonly' \
-    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
-    config_is_local false \
-    config_refresh_token false
+        # Then install miniworld
+        cd /usr/local/
+        git clone https://github.com/maximecb/gym-miniworld.git
+        cd gym-miniworld
+        pip install -e .
 
+# Export global environment variables
+%environment
+        export SHELL=/bin/bash
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
 
-The remote can then be used to download a dataset:
-rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
+%runscript
+        exec /bin/bash ""$@""
 
 
-Rclone is available from the conda channel conda-forge.",558,"[-0.005975086707621813, 0.007927065715193748, 0.0063360449858009815, -0.018545547500252724, -0.013975745998322964, 0.025862839072942734, -0.020802414044737816, -0.04300660640001297, -0.00026524305576458573, -0.03484824672341347, 0.03866108879446983, -0.0023690080270171165, -0.004983327351510525, -0.0028964276425540447, -0.041913218796253204, -0.009370898827910423, 0.016414843499660492, 0.007604655809700489, 0.04353928565979004, -0.021461250260472298, 0.015784041956067085, 0.008564875461161137, -0.02219017595052719, -0.006265955977141857, -0.025946946814656258, 0.007695771753787994, 0.008887284435331821, -0.006378098390996456, 0.010422233492136002, 0.004709980450570583, -0.002204298973083496, -0.014648600481450558, -0.0029787824023514986, -0.012721152976155281, -0.009462014771997929, -0.015447614714503288, 0.022442495450377464, -0.0059085022658109665, 0.0018538539297878742, -0.005354799330234528, 0.013197758235037327, 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-122,"Download a dataset from Mila’s Google Drive with  gdown
-The utility gdown is a simple utility to
-download data from Google Drive from the command line shell or in a Python
-script and requires no setup.
+Keep in mind that those environment variables are sourced at runtime and not at
+build time. This is why, you should also define them in the %post section
+since they are required to install MuJoCo.
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+Using containers on clusters,https://docs.mila.quebec/Userguide.html#using-containers-on-clusters,"Using containers on clusters
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+How to use containers on clusters,https://docs.mila.quebec/Userguide.html#how-to-use-containers-on-clusters,"How to use containers on clusters
+On every cluster with Slurm, datasets and intermediate results should go in
+$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
+In order to use the container you built, you need to copy it on the cluster you
+want to use.
 
 Warning
-A limitation however is that it uses a shared client id which can cause a
-quota block when too many users uses it in the same day. It is described in
-a GitHub issue.",87,"[-0.02421621046960354, 0.0049215639010071754, 0.010090087540447712, -0.011896424926817417, -0.014648267067968845, 0.026629364117980003, -0.009426822885870934, -0.034263961017131805, -0.0008784726378507912, -0.045948706567287445, 0.014944619499146938, -0.007606373634189367, -0.007860389538109303, 0.0022843817714601755, -0.023750513792037964, 0.010040695779025555, 0.006505636963993311, -0.025782642886042595, 0.04007811099290848, -0.025796756148338318, 0.024921810254454613, 0.018938317894935608, -0.007662821561098099, -0.026403572410345078, -0.01858551800251007, -0.004064259119331837, 0.015876011922955513, -0.016652172431349754, 0.011367224156856537, 0.007175957318395376, 0.0021873617079108953, -0.038610462099313736, -0.001581427175551653, -0.000868329661898315, -0.014309578575193882, -0.017865804955363274, 0.00975845567882061, -0.0067349569872021675, -0.001980973407626152, -0.01337113045156002, 0.012199833057820797, 0.009631447494029999, 0.0023390657734125853, 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-123,"Download a dataset from Mila’s Google Drive with rclone
-Rclone is a command line program to manage files on
-cloud storage. In the context of a Google Drive remote, it allows to specify a
-client id to avoid sharing with other users which avoid quota limits. Rclone
-describes the creation of a client id in its documentaton. Once this is done, a
-remote for Mila’s Google Drive can be configured from the command line:
-rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
-    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
-    scope 'drive.readonly' \
-    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
-    config_is_local false \
-    config_refresh_token false
-
+You should always store your container in $SCRATCH !
 
-The remote can then be used to download a dataset:
-rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
+Then reserve a node with srun/sbatch, copy the container and your dataset on the
+node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
+<YOUR_CODE> within the container <YOUR_CONTAINER> with:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
+Remember that /dataset, /tmp_log and /final_log were created in the
+previous section. Now each time, we’ll use singularity, we are explicitly
+telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
+/dataset inside the container with the option -B such that each dataset
+downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
+This will allow us to have code and scripts that are invariant to the cluster
+environment. The option -H specify what will be the container’s home. For
+example, if you have your code in $HOME/Project12345/Version35/ you can
+specify -H $HOME/Project12345/Version35:/home, thus the container will only
+have access to the code inside Version35.
+If you want to run multiple commands inside the container you can use:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
+   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
+   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'
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+Example: Interactive case (srun/salloc),https://docs.mila.quebec/Userguide.html#example-interactive-case-srun-salloc,"Example: Interactive case (srun/salloc)
+Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
+<YOUR_DATASET> to $SLURM_TMPDIR
+0. Get an interactive session
+srun --gres=gpu:1
+1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+Then use singularity shell to get a shell inside the container
+3. Get a shell in your environment
+singularity shell --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>
+4. Execute your code
+python <YOUR_CODE>
+or use singularity exec to execute <YOUR_CODE>.
+3. Execute your code
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python <YOUR_CODE>
+You can create also the following alias to make your life easier.
+alias my_env='singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>'
+This will allow you to run any code with:
+my_env python <YOUR_CODE>
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+Example: sbatch case,https://docs.mila.quebec/Userguide.html#example-sbatch-case,"Example: sbatch case
+You can also create a sbatch script:
+:linenos:
 
+#!/bin/bash
+#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+#SBATCH --gres=gpu:1              # Ask for 1 GPU
+#SBATCH --mem=10G                 # Ask for 10 GB of RAM
+#SBATCH --time=0:10:00            # The job will run for 10 minutes
 
-Rclone is available from the conda channel conda-forge.",226,"[-0.005835805553942919, -0.004188932944089174, 0.0022567620035260916, -0.01655072718858719, -0.01281280443072319, 0.02630895934998989, -0.008917711675167084, -0.04701445251703262, -0.012723969295620918, -0.019120121374726295, 0.02960270456969738, -0.00662849098443985, -0.010284410789608955, -0.0014956813538447022, -0.03053205832839012, 0.0034099144395440817, 0.01364649087190628, -0.023151883855462074, 0.029766706749796867, -0.009990571066737175, 0.02051415480673313, 0.004024928901344538, -0.019215790554881096, -0.02848201058804989, -0.02013147808611393, -0.009464391507208347, 0.007038500625640154, -0.020404817536473274, 0.019530130550265312, 0.0016528518171980977, 0.001648580888286233, -0.02136150747537613, -0.009040715172886848, -0.003512416733428836, -0.013441486284136772, -0.009293554350733757, 0.012997308745980263, -0.00751001201570034, -0.007892687804996967, -0.005712802521884441, 0.0121226217597723, 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-124,"Digital Object Identifier (DOI)
-It is recommended to get a DOI to reference the dataset. A DOI is a permanent
-id/URL which prevents losing references of online scientific data.
-https://figshare.com can be used to create a DOI:
+# 1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+# 2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+# 3. Executing your code with singularity
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python ""<YOUR_CODE>""
+# 4. Copy whatever you want to save on $SCRATCH
+rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
 
-Go in My Data
-Create an item by clicking Create new item
-Check Metadata record only at the top
-Fill the metadata fields
 
-Then reference the dataset using https://doi.org like this:
-https://doi.org/10.6084/m9.figshare.2066037",108,"[-0.032070040702819824, -0.0024736954364925623, 0.018199097365140915, -0.02928020991384983, -0.021692907437682152, 0.01550052035599947, -0.029984185472130775, -0.002413401147350669, -0.009099548682570457, -0.008930073119699955, 0.00581758376210928, 0.0082000233232975, 0.0018088285578414798, 0.02029799111187458, -0.03629390150308609, 0.018603231757879257, 0.013662359677255154, -0.006785551551729441, 0.037206463515758514, -0.001465803012251854, -0.03358228877186775, 0.0166216678917408, -0.006462895777076483, -0.013108303770422935, -0.01027936115860939, 0.007144058123230934, 0.016178423538804054, -0.017716743052005768, 0.016373973339796066, -0.022344736382365227, -0.004993018694221973, -0.020167624577879906, -0.01456188503652811, -0.03193967789411545, -0.006000096444040537, 0.016400046646595, -0.024495776742696762, -0.018564121797680855, -0.0015904655447229743, -0.02622964419424534, 0.008578084409236908, 0.016282716765999794, 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-125,"Data Transmission using Globus Connect Personal
-Mila doesn’t own a Globus license but if the source or destination provides a
-Globus account, like Digital Research Alliance of Canada for example, it’s
-possible to setup Globus Connect Personal to create a personal endpoint on the
-Mila cluster by following the Globus guide to Install, Configure, and
-Uninstall Globus Connect Personal for Linux.
-This endpoint can then be used to transfer data to and from the Mila cluster.",102,"[-0.010427705943584442, -0.011922230012714863, 0.012506453320384026, -0.04263471066951752, -0.012662699446082115, 0.03407515957951546, -0.030026357620954514, 0.008253851905465126, 0.016548464074730873, -0.024061845615506172, 0.00566900335252285, 0.006460422184318304, 0.0038348142988979816, -0.010359772481024265, -0.027132414281368256, -0.000693340552970767, 0.011738811619579792, -0.012513247318565845, 0.00509157357737422, -0.006059617735445499, -0.011446699500083923, 0.02233634889125824, -0.006093584466725588, -0.01779842935502529, -0.014890899881720543, -0.0167250894010067, 0.023925980553030968, -0.005696176551282406, 0.0058490256778895855, 0.005882992409169674, -0.0059475284069776535, -0.019211433827877045, -0.012438520789146423, -0.0078666340559721, -0.010054074227809906, -0.014388196170330048, 0.03725442290306091, -0.0009018097189255059, -0.03211869299411774, 0.00674234377220273, 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-126,"JupyterHub
-JupyterHub is a platform connected to SLURM to start a JupyterLab
-session as a batch job then connects it when the allocation has been granted.
-It does not require any ssh tunnel or port redirection, the hub acts as a proxy
-server that will redirect you to a session as soon as it is available.
-It is currently available for Mila clusters and some Digital Research Alliance
-of Canada (Alliance) clusters.
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+Issue with PyBullet and OpenGL libraries,https://docs.mila.quebec/Userguide.html#issue-with-pybullet-and-opengl-libraries,"Issue with PyBullet and OpenGL libraries
+If you are running certain gym environments that require pyglet, you may
+encounter a problem when running your singularity instance with the Nvidia
+drivers using the --nv flag. This happens because the --nv flag also
+provides the OpenGL libraries:
+libGL.so.1 => /.singularity.d/libs/libGL.so.1
+libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
 
 
+If you don’t experience those problems with pyglet, you probably don’t need
+to address this. Otherwise, you can resolve those problems by apt-get install
+-y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
+sure that your LD_LIBRARY_PATH points to those libraries before the ones in
+/.singularity.d/libs.
+%environment
+        # ...
+        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH
 
 
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+Mila cluster,https://docs.mila.quebec/Userguide.html#mila-cluster,"Mila cluster
+On the Mila cluster $SCRATCH is not yet defined, you should add the
+experiment results you want to keep in /network/scratch/<u>/<username>/. In
+order to use the sbatch script above and to match other cluster environment’s
+names, you can define $SCRATCH as an alias for
+/network/scratch/<u>/<username> with:
+echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
+Then, you can follow the general procedure explained above.
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+Digital Research Alliance of Canada,https://docs.mila.quebec/Userguide.html#digital-research-alliance-of-canada,"Digital Research Alliance of Canada
+Using singularity on Digital Research Alliance of Canada is similar except that
+you need to add Yoshua’s account name and load singularity. Here is an example
+of a sbatch script using singularity on compute Canada cluster:
 
+Warning
+You should use singularity/2.6 or singularity/3.4. There is a bug
+in singularity/3.2 which makes gpu unusable.
 
+ 1#!/bin/bash
+ 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
+ 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
+ 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
+ 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
+ 8
+ 9# 1. You have to load singularity
+10module load singularity
+11# 2. Then you copy the container to the local disk
+12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+13# 3. Copy your dataset on the compute node
+14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+15# 4. Executing your code with singularity
+16singularity exec --nv \
+17        -H $HOME:/home \
+18        -B $SLURM_TMPDIR:/dataset/ \
+19        -B $SLURM_TMPDIR:/tmp_log/ \
+20        -B $SCRATCH:/final_log/ \
+21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+22        python ""<YOUR_CODE>""
+23# 5. Copy whatever you want to save on $SCRATCH
+24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
 
-Cluster
-Address
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+Sharing Data with ACLs,https://docs.mila.quebec/Userguide.html#sharing-data-with-acls,"Sharing Data with ACLs
+Regular permissions bits are extremely blunt tools: They control access through
+only three sets of bits owning user, owning group and all others. Therefore,
+access is either too narrow (0700 allows access only by oneself) or too wide
+(770 gives all permissions to everyone in the same group, and 777 to
+literally everyone).
+ACLs (Access Control Lists) are an expansion of the permissions bits that allow
+more fine-grained, granular control of accesses to a file. They can be used to
+permit specific users access to files and folders even if conservative default
+permissions would have denied them such access.
+As an illustrative example, to use ACLs to allow $USER (oneself) to
+share with $USER2 (another person) a “playground” folder hierarchy in
+Mila’s scratch filesystem at a location
 
+/network/scratch/${USER:0:1}/$USER/X/Y/Z/...
 
-Mila Local
-https://jupyterhub.server.mila.quebec
-Google Oauth
+in a safe and secure fashion that allows both users to read, write, execute,
+search and delete each others’ files:
 
-Alliance
-https://docs.alliancecan.ca/wiki/JupyterHub
-DRAC login
 
+1. Grant oneself permissions to access any future files/folders created
+by the other (or oneself)
+(-d renders this permission a “default” / inheritable one)
 
+setfacl -Rdm user:${USER}:rwx  /network/scratch/${USER:0:1}/$USER/X/Y/Z/
 
 
-Warning
-Do not forget to close the JupyterLab session! Closing the window leaves
-running the session and the SLURM job it is linked to.
-To close it, use the hub menu and then Control Panel > Stop my server
 
 
 Note
-For Mila Clusters:
-mila.quebec account credentials should be used to login and start a
-JupyterLab session.
-
-
-Access Mila Storage in JupyterLab
-Unfortunately, JupyterLab does not allow the navigation to parent directories of
-$HOME. This makes some file systems like /network/datasets or
-$SLURM_TMPDIR unavailable through their absolute path in the interface. It
-is however possible to create symbolic links to those resources. To do so, you
-can use the ln -s command:
-ln -s /network/datasets $HOME
+The importance of doing this seemingly-redundant step first is that files
+and folders are always owned by only one person, almost always their
+creator (the UID will be the creator’s, the GID typically as well). If that
+user is not yourself, you will not have access to those files unless the
+other person specifically gives them to you – or these files inherited a
+default ACL allowing you full access.
+This is the inherited, default ACL serving that purpose.
 
 
-Note that $SLURM_TMPDIR is a directory that is dynamically created for each
-job so you would need to recreate the symbolic link every time you start a
-JupyterHub session:
-ln -sf $SLURM_TMPDIR $HOME",361,"[-0.024874243885278702, -0.013851704075932503, 0.008200377225875854, -0.012388101778924465, -0.026652976870536804, 0.014972166158258915, -0.00878161657601595, -0.012584182433784008, 0.011841876432299614, -0.028851881623268127, 0.019215913489460945, -0.008641558699309826, -0.01924392580986023, 0.0061870478093624115, -0.030280468985438347, -0.00877461303025484, 0.01750721037387848, -0.019229918718338013, 0.004460837226361036, -0.02630283124744892, -0.01956605724990368, 0.009264815598726273, 0.010567352175712585, -0.025014301761984825, -0.03537856787443161, 0.007479079999029636, 0.008445478044450283, -0.017493203282356262, -0.005658330395817757, 0.016932973638176918, -0.007682163733989, 0.0077872066758573055, -0.014495969749987125, 0.0014478462981060147, -0.009117755107581615, 0.013550580479204655, 0.021919026970863342, -0.023067498579621315, -0.0032108223531395197, -0.001179985934868455, 0.02721320651471615, -0.0026260814629495144, -0.002659345045685768, 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-127,"Access Mila Storage in JupyterLab
-Unfortunately, JupyterLab does not allow the navigation to parent directories of
-$HOME. This makes some file systems like /network/datasets or
-$SLURM_TMPDIR unavailable through their absolute path in the interface. It
-is however possible to create symbolic links to those resources. To do so, you
-can use the ln -s command:
-ln -s /network/datasets $HOME
+2. Grant the other permission to access any future files/folders created
+by the other (or oneself)
+(-d renders this permission a “default” / inheritable one)
 
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+Sharing Data with ACLs,https://docs.mila.quebec/Userguide.html#sharing-data-with-acls," -Rdm user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/
 
-Note that $SLURM_TMPDIR is a directory that is dynamically created for each
-job so you would need to recreate the symbolic link every time you start a
-JupyterHub session:
-ln -sf $SLURM_TMPDIR $HOME",143,"[-0.004889263771474361, 0.0011752031277865171, 0.011223319917917252, -0.02861667424440384, -0.028058301657438278, 0.010092612355947495, -0.00849426630884409, -0.024428868666291237, -0.0003367694152984768, -0.026439016684889793, 0.0298450980335474, -0.0034636645577847958, -0.008459368720650673, 0.010476495139300823, -0.01857990026473999, -0.010657966136932373, 0.013924455270171165, -0.011202380992472172, -0.0033083667512983084, -0.021078623831272125, -0.0035386960953474045, 0.011956186033785343, -0.005276635754853487, -0.03844406083226204, -0.0031216605566442013, -0.009303908795118332, 0.01304501574486494, -0.03453544154763222, 0.008787413127720356, -0.001903706812299788, 0.00029271020321175456, -0.010671925731003284, -0.010518372990190983, -0.015048183500766754, -0.020590046420693398, 0.0008388698915950954, 0.008619900792837143, -0.024107804521918297, 0.008906067349016666, -0.008242998272180557, -0.01040669810026884, 0.0025371124502271414, 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-128,"Advanced SLURM usage and Multiple GPU jobs
 
-Handling preemption
-On the Mila cluster, jobs can preempt one-another depending on their priority
-(unkillable>high>low) (See the Slurm documentation)
-The default preemption mechanism is to kill and re-queue the job automatically
-without any notice. To allow a different preemption mechanism, every partition
-have been duplicated (i.e. have the same characteristics as their counterparts)
-allowing a 120sec grace period before killing your job but don’t requeue
-it automatically: those partitions are referred by the suffix: -grace
-(main-grace, long-grace, main-cpu-grace, long-cpu-grace).
-When using a partition with a grace period, a series of signals consisting of
-first SIGCONT and SIGTERM then SIGKILL will be sent to the SLURM
-job.  It’s good practice to catch those signals using the Linux trap command
-to properly terminate a job and save what’s necessary to restart the job.  On
-each cluster, you’ll be allowed a grace period before SLURM actually kills
-your job (SIGKILL).
-The easiest way to handle preemption is by trapping the SIGTERM signal
- 1#SBATCH --ntasks=1
- 2#SBATCH ....
- 3
- 4exit_script() {
- 5    echo ""Preemption signal, saving myself""
- 6    trap - SIGTERM # clear the trap
- 7    # Optional: sends SIGTERM to child/sub processes
- 8    kill -- -$$
- 9}
-10
-11trap exit_script SIGTERM
-12
-13# The main script part
-14python3 my_script
 
 
+3. Grant the other permission to access any existing files/folders created
+by oneself.
+Such files and folders were created before the new default ACLs were added
+above and thus did not inherit them from their parent folder at the moment of
+their creation.
 
-Note
+setfacl -Rm  user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/
 
-Requeuing:
-The Slurm scheduler on the cluster does not allow a grace period before
-preempting a job while requeuing it automatically, therefore your job will
-be cancelled at the end of the grace period.
-To automatically requeue it, you can just add the sbatch command inside
-your exit_script function.
 
 
+Note
+The purpose of granting permissions first for future files and then for
+existing files is to prevent a race condition whereby after the first
+setfacl command the other person could create files to which the
+second setfacl command does not apply.
 
 
-Packing jobs
 
-Sharing a GPU between processes
-srun, when used in a batch job is responsible for starting tasks on the
-allocated resources (see srun) SLURM batch script
-1#SBATCH --ntasks-per-node=2
-2#SBATCH --output=myjob_output_wrapper.out
-3#SBATCH --ntasks=2
-4#SBATCH --gres=gpu:1
-5#SBATCH --cpus-per-task=4
-6#SBATCH --mem=18G
-7srun -l --output=myjob_output_%t.out python script args
+4. Grant another permission to search through one’s hierarchy down to the
+shared location in question.
 
 
-This will run Python 2 times, each process with 4 CPUs with the same arguments
---output=myjob_output_%t.out will create 2 output files appending the task
-id (%t) to the filename and 1 global log file for things happening outside
-the srun command.
-Knowing that, if you want to have 2 different arguments to the Python program,
-you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
-0  python script firstarg
-1  python script secondarg
+Non-recursive (!!!!)
+May also grant :rx in unlikely event others listing your folders on the
+path is not troublesome or desirable.
 
+setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/Y/
+setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/
+setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/
 
-Or by specifying a range of tasks
-0-1  python script %t
 
 
-%t being the taskid that your Python script will parse.  Note the -l on the
-srun command: this will prepend each line with the taskid (0:, 1:)
+Note
+In order to access a file, all folders from the root (/) down to the
+parent folder in question must be searchable (+x) by the concerned user.
+This is already the case for all users for folders such as /,
+/network and /network/scratch, but users must explicitly grant access
+to some or all users either through base permissions or by adding ACLs, for
+at least /network/scratch/${USER:0:1}/$USER, $HOME and subfolders.
+To bluntly allow all users to search through a folder (think twice!),
+the following command can be used:
+chmod a+x /network/scratch/${USER:0:1}/$USER/
 
 
-Sharing a node with multiple GPU 1process/GPU
-On Digital Research Alliance of Canada, several nodes, especially nodes with
-largeGPU (P100) are reserved for jobs requesting the whole node, therefore
-packing multiple processes in a single job can leverage faster GPU.
-If you want different tasks to access different GPUs in a single allocation you
-need to create an allocation requesting a whole node and using srun with a
-subset of those resources (1 GPU).
-Keep in mind that every resource not specified on the srun command while
-inherit the global allocation specification so you need to split each resource
-in a subset (except –cpu-per-task which is a per-task requirement)
-Each srun represents a job step (%s).
-Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
-Requesting 1 task per GPU
- 1#!/bin/bash
- 2#SBATCH --nodes=1-1
- 3#SBATCH --ntasks-per-node=4
- 4#SBATCH --output=myjob_output_wrapper.out
- 5#SBATCH --gres=gpu:4
- 6#SBATCH --cpus-per-task=6
- 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
- 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
- 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
-10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
-11wait
 
 
-This will create 4 output files:
+Note
+For more information on setfacl and path resolution/access checking,
+consider the following documentation viewing commands:
 
-JOBID-step-0.out
-JOBID-step-1.out
-JOBID-step-2.out
-JOBID-step-3.out
+man setfacl
+man path_resolution
 
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+Viewing and Verifying ACLs,https://docs.mila.quebec/Userguide.html#viewing-and-verifying-acls,"Viewing and Verifying ACLs
+getfacl /path/to/folder/or/file
+           1:  # file: somedir/
+           2:  # owner: lisa
+           3:  # group: staff
+           4:  # flags: -s-
+           5:  user::rwx
+           6:  user:joe:rwx               #effective:r-x
+           7:  group::rwx                 #effective:r-x
+           8:  group:cool:r-x
+           9:  mask::r-x
+          10:  other::r-x
+          11:  default:user::rwx
+          12:  default:user:joe:rwx       #effective:r-x
+          13:  default:group::r-x
+          14:  default:mask::r-x
+          15:  default:other::---
 
 
-Sharing a node with multiple GPU & multiple processes/GPU
-Combining both previous sections, we can create a script requesting a whole node
-with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
-processes
-Example still with a 24 cores/4 GPUs/128G RAM
-Requesting 2 tasks per GPU
- 1#!/bin/bash
- 2#SBATCH --nodes=1-1
- 3#SBATCH --ntasks-per-node=8
- 4#SBATCH --output=myjob_output_wrapper.out
- 5#SBATCH --gres=gpu:4
- 6#SBATCH --cpus-per-task=3
- 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
- 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
- 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
-10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
-11wait
 
+Note
 
---exclusive is important to specify subsequent step/srun to bind to different cpus.
-This will produce 8 output files, 2 for each step:
+man getfacl
 
-JOBID-step-0-task-0.out
-JOBID-step-0-task-1.out
-JOBID-step-1-task-0.out
-JOBID-step-1-task-1.out
-JOBID-step-2-task-0.out
-JOBID-step-2-task-1.out
-JOBID-step-3-task-0.out
-JOBID-step-3-task-1.out
 
-Running nvidia-smi in silly.conf, while parsing the output, we can see 4
-GPUs allocated and 2 tasks per GPU
-cat JOBID-step-* | grep Tesla
-0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |",1858,"[-0.011224907822906971, -0.0169999897480011, 0.0005294426809996367, -0.02269556373357773, -0.01840219832956791, 0.003677187254652381, -0.029749980196356773, 0.011998291127383709, -0.043598610907793045, -0.03437582775950432, 0.04169045016169548, 0.027928553521633148, 0.005088432226330042, 0.012894549407064915, 0.0006586411618627608, -0.006584604736417532, 0.021813761442899704, -0.005771467462182045, 0.0058654299937188625, -0.012677713297307491, -0.009894976392388344, 0.009627544321119785, -0.00450658705085516, -0.008160283789038658, -0.028723619878292084, 0.01420279685407877, 0.015091827139258385, -0.016306111589074135, -0.0023996583186089993, 0.010538258589804173, -0.016956621780991554, 0.006049741059541702, -0.028839267790317535, 0.008210878819227219, -0.04030269384384155, 0.0035380504559725523, 0.007423039525747299, -0.003122447058558464, -0.009446847252547741, -0.008210878819227219, 0.018431110307574272, 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-129,"Handling preemption
-On the Mila cluster, jobs can preempt one-another depending on their priority
-(unkillable>high>low) (See the Slurm documentation)
-The default preemption mechanism is to kill and re-queue the job automatically
-without any notice. To allow a different preemption mechanism, every partition
-have been duplicated (i.e. have the same characteristics as their counterparts)
-allowing a 120sec grace period before killing your job but don’t requeue
-it automatically: those partitions are referred by the suffix: -grace
-(main-grace, long-grace, main-cpu-grace, long-cpu-grace).
-When using a partition with a grace period, a series of signals consisting of
-first SIGCONT and SIGTERM then SIGKILL will be sent to the SLURM
-job.  It’s good practice to catch those signals using the Linux trap command
-to properly terminate a job and save what’s necessary to restart the job.  On
-each cluster, you’ll be allowed a grace period before SLURM actually kills
-your job (SIGKILL).
-The easiest way to handle preemption is by trapping the SIGTERM signal
- 1#SBATCH --ntasks=1
- 2#SBATCH ....
- 3
- 4exit_script() {
- 5    echo ""Preemption signal, saving myself""
- 6    trap - SIGTERM # clear the trap
- 7    # Optional: sends SIGTERM to child/sub processes
- 8    kill -- -$$
- 9}
-10
-11trap exit_script SIGTERM
-12
-13# The main script part
-14python3 my_script
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+Contributing datasets,https://docs.mila.quebec/Userguide.html#contributing-datasets,"Contributing datasets
+If a dataset could help the research of others at Mila, this form can be filled to request its addition
+to /network/datasets.
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+Publicly share a Mila dataset,https://docs.mila.quebec/Userguide.html#publicly-share-a-mila-dataset,"Publicly share a Mila dataset
+Mila offers two ways to publicly share a Mila dataset:
 
+Academic Torrent
+Google Drive
 
+Note that these options are not mutually exclusive and both can be used.
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+Academic Torrent,https://docs.mila.quebec/Userguide.html#id10,"Academic Torrent
+Mila hosts/seeds some datasets created by the Mila community through Academic
+Torrent. The first step is to create an
+account and a torrent file.
+Then drop the dataset in /network/scratch/.transit_datasets and send the
+Academic Torrent URL to Mila’s helpdesk. If
+the dataset does not reside on the Mila cluster, only the Academic Torrent URL
+would be needed to proceed with the initial download. Then you can delete /
+stop sharing your copy.
 
 Note
 
-Requeuing:
-The Slurm scheduler on the cluster does not allow a grace period before
-preempting a job while requeuing it automatically, therefore your job will
-be cancelled at the end of the grace period.
-To automatically requeue it, you can just add the sbatch command inside
-your exit_script function.",412,"[-0.013301797211170197, -0.02218841016292572, 0.003374663181602955, -0.018138814717531204, -0.017984142526984215, 0.00542055256664753, -0.022975830361247063, 0.02637861669063568, -0.03065318986773491, -0.017337331548333168, 0.03717753663659096, 0.023580458015203476, 0.00036185351200401783, 0.01837785355746746, 0.00015357353549916297, -0.0125917112454772, 0.02356639690697193, 0.0006788872997276485, 0.004355424549430609, -0.020107368007302284, 0.013365072198212147, 0.024635041132569313, 0.002494086977094412, -0.022539937868714333, -0.023791374638676643, 0.01600152812898159, 0.010243508964776993, -0.01984020695090294, 0.0020388588309288025, 0.024761591106653214, -0.033071696758270264, 0.01179725956171751, -0.025478705763816833, 0.012247215025126934, -0.04403935372829437, 3.9739075873512775e-05, 0.0030688343103975058, -0.00998337846249342, 0.0050690253265202045, -0.01242297887802124, 0.018068509176373482, 0.010116958990693092, 0.020515140146017075, 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-130,"Packing jobs
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
 
-Sharing a GPU between processes
-srun, when used in a batch job is responsible for starting tasks on the
-allocated resources (see srun) SLURM batch script
-1#SBATCH --ntasks-per-node=2
-2#SBATCH --output=myjob_output_wrapper.out
-3#SBATCH --ntasks=2
-4#SBATCH --gres=gpu:1
-5#SBATCH --cpus-per-task=4
-6#SBATCH --mem=18G
-7srun -l --output=myjob_output_%t.out python script args
 
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+Generate a .torrent file to be uploaded to Academic Torrent,https://docs.mila.quebec/Userguide.html#generate-a-torrent-file-to-be-uploaded-to-academic-torrent,"Generate a .torrent file to be uploaded to Academic Torrent
+The command line / Python utility torrentool can be used to create a
+DATASET_NAME.torrent file:
+# Install torrentool
+python3 -m pip install torrentool click
+# Change Directory to the location of the dataset to be hosted by Mila
+cd /network/scratch/.transit_datasets
+torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
 
-This will run Python 2 times, each process with 4 CPUs with the same arguments
---output=myjob_output_%t.out will create 2 output files appending the task
-id (%t) to the filename and 1 global log file for things happening outside
-the srun command.
-Knowing that, if you want to have 2 different arguments to the Python program,
-you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
-0  python script firstarg
-1  python script secondarg
 
+The resulting DATASET_NAME.torrent can then be used to register a new dataset
+on Academic Torrent.
 
-Or by specifying a range of tasks
-0-1  python script %t
+Warning
 
+The creation of a DATASET_NAME.torrent file requires the computation of
+checksums for the dataset content which can quickly become CPU-heavy. This
+process should not be executed on a login node
 
-%t being the taskid that your Python script will parse.  Note the -l on the
-srun command: this will prepend each line with the taskid (0:, 1:)
 
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+Download a dataset from Academic Torrent,https://docs.mila.quebec/Userguide.html#download-a-dataset-from-academic-torrent,"Download a dataset from Academic Torrent
+Academic Torrent provides a Python API to easily download a dataset
+from it’s registered list:
+# Install the Python API with:
+# python3 -m pip install academictorrents
+import academictorrents as at
+mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
 
-Sharing a node with multiple GPU 1process/GPU
-On Digital Research Alliance of Canada, several nodes, especially nodes with
-largeGPU (P100) are reserved for jobs requesting the whole node, therefore
-packing multiple processes in a single job can leverage faster GPU.
-If you want different tasks to access different GPUs in a single allocation you
-need to create an allocation requesting a whole node and using srun with a
-subset of those resources (1 GPU).
-Keep in mind that every resource not specified on the srun command while
-inherit the global allocation specification so you need to split each resource
-in a subset (except –cpu-per-task which is a per-task requirement)
-Each srun represents a job step (%s).
-Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
-Requesting 1 task per GPU
- 1#!/bin/bash
- 2#SBATCH --nodes=1-1
- 3#SBATCH --ntasks-per-node=4
- 4#SBATCH --output=myjob_output_wrapper.out
- 5#SBATCH --gres=gpu:4
- 6#SBATCH --cpus-per-task=6
- 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
- 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
- 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
-10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
-11wait
 
 
-This will create 4 output files:
+Note
+Current needs have been evaluated to be for a download speed of about 10
+MB/s. This speed can be higher if more users also seeds the dataset.
 
-JOBID-step-0.out
-JOBID-step-1.out
-JOBID-step-2.out
-JOBID-step-3.out
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+Google Drive,https://docs.mila.quebec/Userguide.html#id12,"Google Drive
+Only a member of the staff team can upload to Mila’s Google Drive
+which requires to first drop the dataset in
+/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:
 
+directory containing the archived dataset (zip is favored) in
+/network/scratch/.transit_datasets
+the name of the dataset
+a licence in .txt format. One of the the creative common licenses can be used. It is
+recommended to at least have the Attribution option. The No Derivatives
+option is discouraged unless the dataset should not be modified by others.
+MD5 checksum of the archive
+the arXiv and GitHub URLs (those can be sent later if the article is still in
+the submission process)
+instructions to know if the dataset needs to be unziped, untared or
+else before uploading to Google Drive
 
 
-Sharing a node with multiple GPU & multiple processes/GPU
-Combining both previous sections, we can create a script requesting a whole node
-with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
-processes
-Example still with a 24 cores/4 GPUs/128G RAM
-Requesting 2 tasks per GPU
- 1#!/bin/bash
- 2#SBATCH --nodes=1-1
- 3#SBATCH --ntasks-per-node=8
- 4#SBATCH --output=myjob_output_wrapper.out
- 5#SBATCH --gres=gpu:4
- 6#SBATCH --cpus-per-task=3
- 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
- 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
- 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
-10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
-11wait
+Note
 
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
 
---exclusive is important to specify subsequent step/srun to bind to different cpus.
-This will produce 8 output files, 2 for each step:
 
-JOBID-step-0-task-0.out
-JOBID-step-0-task-1.out
-JOBID-step-1-task-0.out
-JOBID-step-1-task-1.out
-JOBID-step-2-task-0.out
-JOBID-step-2-task-1.out
-JOBID-step-3-task-0.out
-JOBID-step-3-task-1.out
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+Download a dataset from Mila’s Google Drive with  gdown,https://docs.mila.quebec/Userguide.html#download-a-dataset-from-mila-s-google-drive-with-gdown,"Download a dataset from Mila’s Google Drive with  gdown
+The utility gdown is a simple utility to
+download data from Google Drive from the command line shell or in a Python
+script and requires no setup.
 
-Running nvidia-smi in silly.conf, while parsing the output, we can see 4
-GPUs allocated and 2 tasks per GPU
-cat JOBID-step-* | grep Tesla
-0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |",1434,"[-0.0033083255402743816, -0.004895759746432304, 0.006124967709183693, -0.016759373247623444, -0.019498750567436218, -0.00866064801812172, -0.02670542150735855, 0.0028289344627410173, -0.03118676319718361, -0.035738345235586166, 0.021310955286026, 0.026241835206747055, 0.013675817288458347, 0.0009903904283419251, -0.00019831953977700323, 0.012425537221133709, 0.011076919734477997, -0.00852719135582447, 0.008428854867815971, 0.00283420248888433, -0.028587866574525833, -0.020130915567278862, -0.012734594754874706, 0.004562117625027895, -0.02479488216340542, 0.012474705465137959, 0.019611135125160217, -0.006957317236810923, 0.0024057356640696526, -0.0080355079844594, -0.009995217435061932, -0.0010694109369069338, -0.015424804762005806, 0.0003652503655757755, -0.024457726627588272, -0.0011774057056754827, 0.002342519350349903, -0.0012467681663110852, 0.002698989585042, -0.023811515420675278, 0.024050332605838776, 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-131,"Sharing a GPU between processes
-srun, when used in a batch job is responsible for starting tasks on the
-allocated resources (see srun) SLURM batch script
-1#SBATCH --ntasks-per-node=2
-2#SBATCH --output=myjob_output_wrapper.out
-3#SBATCH --ntasks=2
-4#SBATCH --gres=gpu:1
-5#SBATCH --cpus-per-task=4
-6#SBATCH --mem=18G
-7srun -l --output=myjob_output_%t.out python script args
+Warning
+A limitation however is that it uses a shared client id which can cause a
+quota block when too many users uses it in the same day. It is described in
+a GitHub issue.
+
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+Download a dataset from Mila’s Google Drive with rclone,https://docs.mila.quebec/Userguide.html#download-a-dataset-from-mila-s-google-drive-with-rclone,"Download a dataset from Mila’s Google Drive with rclone
+Rclone is a command line program to manage files on
+cloud storage. In the context of a Google Drive remote, it allows to specify a
+client id to avoid sharing with other users which avoid quota limits. Rclone
+describes the creation of a client id in its documentaton. Once this is done, a
+remote for Mila’s Google Drive can be configured from the command line:
+rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
+    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
+    scope 'drive.readonly' \
+    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
+    config_is_local false \
+    config_refresh_token false
 
 
-This will run Python 2 times, each process with 4 CPUs with the same arguments
---output=myjob_output_%t.out will create 2 output files appending the task
-id (%t) to the filename and 1 global log file for things happening outside
-the srun command.
-Knowing that, if you want to have 2 different arguments to the Python program,
-you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
-0  python script firstarg
-1  python script secondarg
+The remote can then be used to download a dataset:
+rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
 
 
-Or by specifying a range of tasks
-0-1  python script %t
+Rclone is available from the conda channel conda-forge.
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+Digital Object Identifier (DOI),https://docs.mila.quebec/Userguide.html#digital-object-identifier-doi,"Digital Object Identifier (DOI)
+It is recommended to get a DOI to reference the dataset. A DOI is a permanent
+id/URL which prevents losing references of online scientific data.
+https://figshare.com can be used to create a DOI:
 
+Go in My Data
+Create an item by clicking Create new item
+Check Metadata record only at the top
+Fill the metadata fields
 
-%t being the taskid that your Python script will parse.  Note the -l on the
-srun command: this will prepend each line with the taskid (0:, 1:)",279,"[-0.008530627936124802, -0.003139256965368986, 0.008558735251426697, -0.016653696075081825, -0.002961828140541911, -0.011664614081382751, -0.018241770565509796, 0.007258763071149588, -0.030496641993522644, -0.032604705542325974, 0.018241770565509796, 0.022584378719329834, -0.003980725072324276, -0.00281075038947165, -0.0019042834173887968, 0.00856576208025217, 0.011826232075691223, 0.0012217981275171041, 0.017581243067979813, 0.006854717619717121, -0.0335884690284729, -0.013927267864346504, -0.005779605358839035, -0.00978843867778778, -0.033447928726673126, 0.010863550007343292, 0.017637459561228752, -0.008987374603748322, 0.0016987472772598267, -0.00502421660348773, -0.015669934451580048, 0.002036037389189005, -0.008586842566728592, -0.0028862892650067806, -0.028107503429055214, 0.0008915349026210606, -0.00043434876715764403, -0.003699650289490819, 0.013470521196722984, -0.020349832251667976, 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-132,"Sharing a node with multiple GPU 1process/GPU
-On Digital Research Alliance of Canada, several nodes, especially nodes with
-largeGPU (P100) are reserved for jobs requesting the whole node, therefore
-packing multiple processes in a single job can leverage faster GPU.
-If you want different tasks to access different GPUs in a single allocation you
-need to create an allocation requesting a whole node and using srun with a
-subset of those resources (1 GPU).
-Keep in mind that every resource not specified on the srun command while
-inherit the global allocation specification so you need to split each resource
-in a subset (except –cpu-per-task which is a per-task requirement)
-Each srun represents a job step (%s).
-Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
-Requesting 1 task per GPU
- 1#!/bin/bash
- 2#SBATCH --nodes=1-1
- 3#SBATCH --ntasks-per-node=4
- 4#SBATCH --output=myjob_output_wrapper.out
- 5#SBATCH --gres=gpu:4
- 6#SBATCH --cpus-per-task=6
- 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
- 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
- 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
-10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
-11wait
+Then reference the dataset using https://doi.org like this:
+https://doi.org/10.6084/m9.figshare.2066037
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+Data Transmission using Globus Connect Personal,https://docs.mila.quebec/Userguide.html#data-transmission-using-globus-connect-personal,"Data Transmission using Globus Connect Personal
+Mila doesn’t own a Globus license but if the source or destination provides a
+Globus account, like Digital Research Alliance of Canada for example, it’s
+possible to setup Globus Connect Personal to create a personal endpoint on the
+Mila cluster by following the Globus guide to Install, Configure, and
+Uninstall Globus Connect Personal for Linux.
+This endpoint can then be used to transfer data to and from the Mila cluster.
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+JupyterHub,https://docs.mila.quebec/Userguide.html#jupyterhub,"JupyterHub
+JupyterHub is a platform connected to SLURM to start a JupyterLab
+session as a batch job then connects it when the allocation has been granted.
+It does not require any ssh tunnel or port redirection, the hub acts as a proxy
+server that will redirect you to a session as soon as it is available.
+It is currently available for Mila clusters and some Digital Research Alliance
+of Canada (Alliance) clusters.
 
 
-This will create 4 output files:
 
-JOBID-step-0.out
-JOBID-step-1.out
-JOBID-step-2.out
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-133,"Sharing a node with multiple GPU & multiple processes/GPU
-Combining both previous sections, we can create a script requesting a whole node
-with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
-processes
-Example still with a 24 cores/4 GPUs/128G RAM
-Requesting 2 tasks per GPU
- 1#!/bin/bash
- 2#SBATCH --nodes=1-1
- 3#SBATCH --ntasks-per-node=8
- 4#SBATCH --output=myjob_output_wrapper.out
- 5#SBATCH --gres=gpu:4
- 6#SBATCH --cpus-per-task=3
- 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
- 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
- 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
-10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
-11wait
 
 
---exclusive is important to specify subsequent step/srun to bind to different cpus.
-This will produce 8 output files, 2 for each step:
 
-JOBID-step-0-task-0.out
-JOBID-step-0-task-1.out
-JOBID-step-1-task-0.out
-JOBID-step-1-task-1.out
-JOBID-step-2-task-0.out
-JOBID-step-2-task-1.out
-JOBID-step-3-task-0.out
-JOBID-step-3-task-1.out
 
-Running nvidia-smi in silly.conf, while parsing the output, we can see 4
-GPUs allocated and 2 tasks per GPU
-cat JOBID-step-* | grep Tesla
-0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
-0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
-1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |",717,"[-0.008710494264960289, 0.008550986647605896, 0.012178047560155392, -0.031568605452775955, -0.0015942075988277793, -0.0004698534612543881, -0.030153842642903328, 0.0007810663664713502, -0.044384680688381195, -0.03858693316578865, 0.025285398587584496, 0.027837518602609634, 0.006161842029541731, -0.002040655119344592, 0.01049281656742096, 0.026755640283226967, 0.009889462031424046, -0.009813175536692142, 0.005891372915357351, 0.003779633203521371, -0.024314483627676964, -0.009078054688870907, -0.01244158111512661, 0.006040477659553289, -0.017157454043626785, 0.01296864915639162, 0.02002858743071556, -0.009209821932017803, 0.006546740420162678, -0.006678507663309574, -0.00582202197983861, -0.007198640611022711, -0.01307267602533102, -0.008342932909727097, -0.016602644696831703, 0.007781189400702715, 0.003301110817119479, 0.005107705947011709, -0.008481635712087154, -0.018072888255119324, 0.0065918187610805035, 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-134,"Multiple Nodes
+Cluster
+Address
+Login type
 
-Data Parallel
 
-Request 3 nodes with at least 4 GPUs each.
- 1#!/bin/bash
- 2
- 3# Number of Nodes
- 4#SBATCH --nodes=3
- 5
- 6# Number of tasks. 3 (1 per node)
- 7#SBATCH --ntasks=3
- 8
- 9# Number of GPU per node
-10#SBATCH --gres=gpu:4
-11#SBATCH --gpus-per-node=4
-12
-13# 16 CPUs per node
-14#SBATCH --cpus-per-gpu=4
-15
-16# 16Go per nodes (4Go per GPU)
-17#SBATCH --mem=16G
-18
-19# we need all nodes to be ready at the same time
-20#SBATCH --wait-all-nodes=1
-21
-22# Total resources:
-23#   CPU: 16 * 3 = 48
-24#   RAM: 16 * 3 = 48 Go
-25#   GPU:  4 * 3 = 12
-26
-27# Setup our rendez-vous point
-28RDV_ADDR=$(hostname)
-29WORLD_SIZE=$SLURM_JOB_NUM_NODES
-30# -----
-31
-32srun -l torchrun \
-33   --nproc_per_node=$SLURM_GPUS_PER_NODE\
-34   --nnodes=$WORLD_SIZE\
-35   --rdzv_id=$SLURM_JOB_ID\
-36   --rdzv_backend=c10d\
-37   --rdzv_endpoint=$RDV_ADDR\
-38   training_script.py
 
+Mila Local
+https://jupyterhub.server.mila.quebec
+Google Oauth
 
-You can find below a pytorch script outline on what a multi-node trainer could look like.
-import os
-import torch.distributed as dist
+Alliance
+https://docs.alliancecan.ca/wiki/JupyterHub
+DRAC login
 
-class Trainer:
-   def __init__(self):
-      self.local_rank = None
-      self.chk_path = ...
-      self.model = ...
 
-   @property
-   def device_id(self):
-      return self.local_rank
 
-   def load_checkpoint(self, path):
-      self.chk_path = path
-      # ...
 
-   def should_checkpoint(self):
-      # Note: only one worker saves its weights
-      return self.global_rank == 0 and self.local_rank == 0
+Warning
+Do not forget to close the JupyterLab session! Closing the window leaves
+running the session and the SLURM job it is linked to.
+To close it, use the hub menu and then Control Panel > Stop my server
 
-   def save_checkpoint(self):
-      if self.chk_path is None:
-            return
 
-      # Save your states here
-      # Note: you should save the weights of self.model not ddp_model
-      # ...
+Note
+For Mila Clusters:
+mila.quebec account credentials should be used to login and start a
+JupyterLab session.
 
-   def initialize(self):
-      self.global_rank = int(os.environ.get(""RANK"", -1))
-      self.local_rank = int(os.environ.get(""LOCAL_RANK"", -1))
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+Access Mila Storage in JupyterLab,https://docs.mila.quebec/Userguide.html#access-mila-storage-in-jupyterlab,"Access Mila Storage in JupyterLab
+Unfortunately, JupyterLab does not allow the navigation to parent directories of
+$HOME. This makes some file systems like /network/datasets or
+$SLURM_TMPDIR unavailable through their absolute path in the interface. It
+is however possible to create symbolic links to those resources. To do so, you
+can use the ln -s command:
+ln -s /network/datasets $HOME
 
-      assert self.global_rank >= 0, 'Global rank should be set (Only Rank 0 can save checkpoints)'
-      assert self.local_rank >= 0, 'Local rank should be set'
 
-      dist.init_process_group(backend=""gloo|nccl"")
+Note that $SLURM_TMPDIR is a directory that is dynamically created for each
+job so you would need to recreate the symbolic link every time you start a
+JupyterHub session:
+ln -sf $SLURM_TMPDIR $HOME
 
-   def sync_weights(self, resuming=False):
-      if resuming:
-            # in the case of resuming all workers need to load the same checkpoint
-            self.load_checkpoint()
 
-            # Wait for everybody to finish loading the checkpoint
-            dist.barrier()
-            return
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+Advanced SLURM usage and Multiple GPU jobs,https://docs.mila.quebec/Userguide.html#advanced-slurm-usage-and-multiple-gpu-jobs,"Advanced SLURM usage and Multiple GPU jobs
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+Handling preemption,https://docs.mila.quebec/Userguide.html#handling-preemption,"Handling preemption
+On the Mila cluster, jobs can preempt one-another depending on their priority
+(unkillable>high>low) (See the Slurm documentation)
+The default preemption mechanism is to kill and re-queue the job automatically
+without any notice. To allow a different preemption mechanism, every partition
+have been duplicated (i.e. have the same characteristics as their counterparts)
+allowing a 120sec grace period before killing your job but don’t requeue
+it automatically: those partitions are referred by the suffix: -grace
+(main-grace, long-grace, main-cpu-grace, long-cpu-grace).
+When using a partition with a grace period, a series of signals consisting of
+first SIGCONT and SIGTERM then SIGKILL will be sent to the SLURM
+job.  It’s good practice to catch those signals using the Linux trap command
+to properly terminate a job and save what’s necessary to restart the job.  On
+each cluster, you’ll be allowed a grace period before SLURM actually kills
+your job (SIGKILL).
+The easiest way to handle preemption is by trapping the SIGTERM signal
+ 1#SBATCH --ntasks=1
+ 2#SBATCH ....
+ 3
+ 4exit_script() {
+ 5    echo ""Preemption signal, saving myself""
+ 6    trap - SIGTERM # clear the trap
+ 7    # Optional: sends SIGTERM to child/sub processes
+ 8    kill -- -$$
+ 9}
+10
+11trap exit_script SIGTERM
+12
+13# The main script part
+14python3 my_script
 
-      # Make sure all workers have the same initial weights
-      # This makes the leader save his weights
-      if self.should_checkpoint():
-            self.save_checkpoint()
 
-      # All workers wait for the leader to finish
-      dist.barrier()
 
-      # All followers load the leader's weights
-      if not self.should_checkpoint():
-            self.load_checkpoint()
+Note
 
-      # Leader waits for the follower to load the weights
-      dist.barrier()
+Requeuing:
+The Slurm scheduler on the cluster does not allow a grace period before
+preempting a job while requeuing it automatically, therefore your job will
+be cancelled at the end of the grace period.
+To automatically requeue it, you can just add the sbatch command inside
+your exit_script function.
+
+
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+Sharing a GPU between processes,https://docs.mila.quebec/Userguide.html#sharing-a-gpu-between-processes,"Sharing a GPU between processes
+srun, when used in a batch job is responsible for starting tasks on the
+allocated resources (see srun) SLURM batch script
+1#SBATCH --ntasks-per-node=2
+2#SBATCH --output=myjob_output_wrapper.out
+3#SBATCH --ntasks=2
+4#SBATCH --gres=gpu:1
+5#SBATCH --cpus-per-task=4
+6#SBATCH --mem=18G
+7srun -l --output=myjob_output_%t.out python script args
+
+
+This will run Python 2 times, each process with 4 CPUs with the same arguments
+--output=myjob_output_%t.out will create 2 output files appending the task
+id (%t) to the filename and 1 global log file for things happening outside
+the srun command.
+Knowing that, if you want to have 2 different arguments to the Python program,
+you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
+0  python script firstarg
+1  python script secondarg
 
-   def dataloader(self, dataset, batch_size):
-      train_sampler = ElasticDistributedSampler(dataset)
-      train_loader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            num_workers=4,
-            pin_memory=True,
-            sampler=train_sampler,
-      )
-      return train_loader
 
-   def train_step(self):
-      # Your batch processing step here
-      # ...
-      pass
+Or by specifying a range of tasks
+0-1  python script %t
 
-   def train(self, dataset, batch_size):
-      self.sync_weights()
 
-      ddp_model = torch.nn.parallel.DistributedDataParallel(
-            self.model,
-            device_ids=[self.device_id],
-            output_device=self.device_id
-      )
+%t being the taskid that your Python script will parse.  Note the -l on the
+srun command: this will prepend each line with the taskid (0:, 1:)
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+Sharing a node with multiple GPU 1process/GPU,https://docs.mila.quebec/Userguide.html#sharing-a-node-with-multiple-gpu-1process-gpu,"Sharing a node with multiple GPU 1process/GPU
+On Digital Research Alliance of Canada, several nodes, especially nodes with
+largeGPU (P100) are reserved for jobs requesting the whole node, therefore
+packing multiple processes in a single job can leverage faster GPU.
+If you want different tasks to access different GPUs in a single allocation you
+need to create an allocation requesting a whole node and using srun with a
+subset of those resources (1 GPU).
+Keep in mind that every resource not specified on the srun command while
+inherit the global allocation specification so you need to split each resource
+in a subset (except –cpu-per-task which is a per-task requirement)
+Each srun represents a job step (%s).
+Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
+Requesting 1 task per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=4
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=6
+ 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
+ 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
+ 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
+10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
+11wait
 
-      loader = self.dataloader(dataset, batch_size)
 
-      for epoch in range(100):
-            for batch in iter(loader):
-               self.train_step(batch)
+This will create 4 output files:
 
-               if self.should_checkpoint():
-                  self.save_checkpoint()
+JOBID-step-0.out
+JOBID-step-1.out
+JOBID-step-2.out
+JOBID-step-3.out
 
-def main():
-   trainer = Trainer()
-   trainer.load_checkpoint(path)
-   tainer.initialize()
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+Sharing a node with multiple GPU & multiple processes/GPU,https://docs.mila.quebec/Userguide.html#sharing-a-node-with-multiple-gpu-multiple-processes-gpu,"Sharing a node with multiple GPU & multiple processes/GPU
+Combining both previous sections, we can create a script requesting a whole node
+with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
+processes
+Example still with a 24 cores/4 GPUs/128G RAM
+Requesting 2 tasks per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=8
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=3
+ 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+11wait
 
-   trainer.train(dataset, batch_size)
 
+--exclusive is important to specify subsequent step/srun to bind to different cpus.
+This will produce 8 output files, 2 for each step:
 
+JOBID-step-0-task-0.out
+JOBID-step-0-task-1.out
+JOBID-step-1-task-0.out
+JOBID-step-1-task-1.out
+JOBID-step-2-task-0.out
+JOBID-step-2-task-1.out
+JOBID-step-3-task-0.out
+JOBID-step-3-task-1.out
 
-Note
-To bypass Python GIL (Global interpreter lock) pytorch spawn one process for each GPU.
-In the example above this means at least 12 processes are spawn, at least 4 on each node.",980,"[-0.033587753772735596, -0.0028996746987104416, -0.0028501804918050766, -0.022255301475524902, -0.00193027185741812, 0.018213847652077675, -0.016370616853237152, 0.012977708131074905, -0.027989795431494713, -0.02946438081562519, -0.002955995500087738, 0.04038722813129425, -0.013790094293653965, -0.008178481832146645, 0.012097053229808807, 0.04281756281852722, 0.0060178060084581375, -0.0030839976388961077, 0.0012126066721975803, 0.01284799911081791, -0.02550484798848629, -0.003247840330004692, 0.007591378875076771, -0.003539685159921646, -0.000521395297255367, 0.011086690239608288, 0.02960091643035412, -0.0050279232673347, 0.020179960876703262, -0.013872016221284866, 0.009632585570216179, -0.02495870552957058, 0.01124370563775301, -0.02848132513463497, -0.006778992246836424, 0.012663676403462887, 0.01092284731566906, -0.002029260154813528, 0.0054170493967831135, -0.008137521333992481, 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-135,"Data Parallel
+Running nvidia-smi in silly.conf, while parsing the output, we can see 4
+GPUs allocated and 2 tasks per GPU
+cat JOBID-step-* | grep Tesla
+0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
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+Data Parallel,https://docs.mila.quebec/Userguide.html#data-parallel,"Data Parallel
 
 Request 3 nodes with at least 4 GPUs each.
  1#!/bin/bash
@@ -7199,7 +3458,8 @@ class Trainer:
 
       dist.init_process_group(backend=""gloo|nccl"")
 
-   def sync_weights(self, resuming=False):
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+Data Parallel,https://docs.mila.quebec/Userguide.html#data-parallel,"nc_weights(self, resuming=False):
       if resuming:
             # in the case of resuming all workers need to load the same checkpoint
             self.load_checkpoint()
@@ -7268,64 +3528,55 @@ def main():
 
 Note
 To bypass Python GIL (Global interpreter lock) pytorch spawn one process for each GPU.
-In the example above this means at least 12 processes are spawn, at least 4 on each node.",975,"[-0.031103160232305527, -0.0004558548389468342, 0.0021918911952525377, -0.019945694133639336, 0.004848676733672619, 0.019876820966601372, -0.011639579199254513, 0.009697353467345238, -0.03231532871723175, -0.03314180672168732, -0.003991204779595137, 0.04162699356675148, -0.008147705346345901, -0.009256565012037754, 0.011908184736967087, 0.04253612086176872, 0.007920423522591591, -0.006105613894760609, 0.0036812752950936556, 0.017590228468179703, -0.026764145120978355, -0.0008725380175746977, 0.012658903375267982, -0.0025965215172618628, -0.010716677643358707, 0.009903973899781704, 0.02799008972942829, -0.009049945510923862, 0.01643315702676773, -0.01309969276189804, 0.004710930399596691, -0.031736794859170914, 0.01102660782635212, -0.024932116270065308, 0.0006878715939819813, 0.009373649023473263, 0.010269002057611942, 0.0039464375004172325, 0.0031285674776881933, -0.013299425132572651, 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-136,"Frequently asked questions (FAQs)
-
-Connection/SSH issues
-
-I’m getting connection refused while trying to connect to a login node
+In the example above this means at least 12 processes are spawn, at least 4 on each node.
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+Frequently asked questions (FAQs),https://docs.mila.quebec/Userguide.html#frequently-asked-questions-faqs,"Frequently asked questions (FAQs)
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+Connection/SSH issues,https://docs.mila.quebec/Userguide.html#connection-ssh-issues,"Connection/SSH issues
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+I’m getting connection refused while trying to connect to a login node,https://docs.mila.quebec/Userguide.html#i-m-getting-connection-refused-while-trying-to-connect-to-a-login-node,"I’m getting connection refused while trying to connect to a login node
 Login nodes are protected against brute force attacks and might ban your IP if
 it detects too many connections/failures. You will be automatically unbanned
 after 1 hour. For any further problem, please submit a support ticket.
-
-
-
-Shell issues
-
-How do I change my shell ?
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+How do I change my shell ?,https://docs.mila.quebec/Userguide.html#how-do-i-change-my-shell,"How do I change my shell ?
 By default you will be assigned /bin/bash as a shell. If you would like to
 change for another one, please submit a support ticket.
-
-
-
-SLURM issues
-
-How can I get an interactive shell on the cluster ?
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+SLURM issues,https://docs.mila.quebec/Userguide.html#slurm-issues,"SLURM issues
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+How can I get an interactive shell on the cluster ?,https://docs.mila.quebec/Userguide.html#how-can-i-get-an-interactive-shell-on-the-cluster,"How can I get an interactive shell on the cluster ?
 Use salloc [--slurm_options] without any executable at the end of the
 command, this will launch your default shell on an interactive session. Remember
 that an interactive session is bound to the login node where you start it so you
 could risk losing your job if the login node becomes unreachable.
-
-
-How can I reset my cluster password ?
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+How can I reset my cluster password ?,https://docs.mila.quebec/Userguide.html#how-can-i-reset-my-cluster-password,"How can I reset my cluster password ?
 To reset your password, please submit a support ticket.
 Warning: your cluster password is the same as your Google Workspace account. So,
 after reset, you must use the new password for all your Google services.
-
-
-srun: error: –mem and –mem-per-cpu are mutually exclusive
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+srun: error: –mem and –mem-per-cpu are mutually exclusive,https://docs.mila.quebec/Userguide.html#srun-error-mem-and-mem-per-cpu-are-mutually-exclusive,"srun: error: –mem and –mem-per-cpu are mutually exclusive
 You can safely ignore this, salloc has a default memory flag in case you
 don’t provide one.
-
-
-How can I see where and if my jobs are running ?
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+How can I see where and if my jobs are running ?,https://docs.mila.quebec/Userguide.html#how-can-i-see-where-and-if-my-jobs-are-running,"How can I see where and if my jobs are running ?
 Use squeue -u YOUR_USERNAME to see all your job status and locations.
 To get more info on a running job, try scontrol show job #JOBID
-
-
-Unable to allocate resources: Invalid account or account/partition combination specified
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+Unable to allocate resources: Invalid account or account/partition combination specified,https://docs.mila.quebec/Userguide.html#unable-to-allocate-resources-invalid-account-or-account-partition-combination-specified,"Unable to allocate resources: Invalid account or account/partition combination specified
 Chances are your account is not setup properly. You should submit a support ticket.
-
-
-How do I cancel a job?
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+How do I cancel a job?,https://docs.mila.quebec/Userguide.html#how-do-i-cancel-a-job,"How do I cancel a job?
 
 To cancel a specific job, use scancel #JOBID
 To cancel all your jobs (running and pending), use scancel -u YOUR_USERNAME
 To cancel all your pending jobs only, use scancel -t PD
 
-
-
-How can I access a node on which one of my jobs is running ?
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+How can I access a node on which one of my jobs is running ?,https://docs.mila.quebec/Userguide.html#how-can-i-access-a-node-on-which-one-of-my-jobs-is-running,"How can I access a node on which one of my jobs is running ?
 You can ssh into a node on which you have a job running, your ssh connection
 will be adopted by your job, i.e.  if your job finishes your ssh connection will
 be automatically terminated. In order to connect to a node, you need to have
@@ -7335,36 +3586,30 @@ ssh-keygen (3xENTER)
 cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
 chmod 600 ~/.ssh/authorized_keys
 chmod 700 ~/.ssh
-
-
-I’m getting Permission denied (publickey) while trying to connect to a node
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+I’m getting Permission denied (publickey) while trying to connect to a node,https://docs.mila.quebec/Userguide.html#i-m-getting-permission-denied-publickey-while-trying-to-connect-to-a-node,"I’m getting Permission denied (publickey) while trying to connect to a node
 See previous question
-
-
-Where do I put my data during a job ?
+",23,"[-0.0100051648914814, -0.00542580196633935, 0.004013969097286463, -0.04192514717578888, -0.051500480622053146, 0.024467356503009796, -0.015712011605501175, 0.0032220864668488503, -0.02951953560113907, -0.029916303232312202, 0.01059370394796133, -0.0028732612263411283, -0.0014887064462527633, -0.005706845782697201, 0.008239547722041607, 0.008675992488861084, 0.014442353509366512, -0.012425449676811695, 0.01010435726493597, -0.0015788058517500758, -0.0032485376577824354, 0.0007836167351342738, -0.005875471979379654, -0.010997084900736809, -0.0029195507522672415, 0.002780682174488902, 0.014058811590075493, -0.021147731691598892, 0.006401189602911472, -0.023713497444987297, 0.011327724903821945, 0.010335804894566536, -0.012055132538080215, -0.0015267300186678767, -0.03166208416223526, -0.020089684054255486, -0.014958151616156101, -0.03190014511346817, 0.012630446814000607, -0.0017871089512482285, -0.0023508500307798386, -0.006903762463480234, -0.003732925048097968, 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+Where do I put my data during a job ?,https://docs.mila.quebec/Userguide.html#where-do-i-put-my-data-during-a-job,"Where do I put my data during a job ?
 Your /home as well as the datasets are on shared file-systems, it is
 recommended to copy them to the $SLURM_TMPDIR to better process them and
 leverage higher-speed local drives. If you run a low priority job subject to
 preemption, it’s better to save any output you want to keep on the shared file
 systems, because the $SLURM_TMPDIR is deleted at the end of each job.
-
-
-slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
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+slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup,https://docs.mila.quebec/Userguide.html#slurmstepd-error-detected-1-oom-kill-event-s-in-step-batch-cgroup,"slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
 You exceeded the amount of memory allocated to your job, either you did not
 request enough memory or you have a memory leak in your process. Try increasing
 the amount of memory requested with --mem= or --mem-per-cpu=.
-
-
-fork: retry: Resource temporarily unavailable
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+fork: retry: Resource temporarily unavailable,https://docs.mila.quebec/Userguide.html#fork-retry-resource-temporarily-unavailable,"fork: retry: Resource temporarily unavailable
 You exceeded the limit of 2000 tasks/PIDs in your job, it probably means there
 is an issue with a sub-process spawning too many processes in your script. For
 any help with your software, please submit a support ticket.
-
-
-
-PyTorch issues
-
-I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263
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+PyTorch issues,https://docs.mila.quebec/Userguide.html#pytorch-issues,"PyTorch issues
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+"I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263",https://docs.mila.quebec/Userguide.html#i-randomly-get-internal-assert-failed-at-aten-src-aten-mapallocator-cpp-263,"I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263
 You are using PyTorch 1.10.x and hitting #67864,
 for which the solution is PR #72232
 merged in PyTorch 1.11.x. For an immediate fix, consider the following compilable Gist:
@@ -7379,175 +3624,123 @@ be injected using the setup key in Hydra YAML config file:
 hydra:
   launcher:
     setup:
-      - export LD_PRELOAD=/absolute/path/to/hack.so",1071,"[-0.003647199599072337, -0.0016801803139969707, -0.017801392823457718, -0.02517228201031685, -0.04747965186834335, -0.010743418708443642, -0.01958153210580349, -0.001377695705741644, -0.033461056649684906, -0.022933201864361763, 0.03220939636230469, -0.008977186866104603, 0.002395539078861475, 0.008546059019863605, 0.009109307080507278, -0.0035707091446965933, 0.018149076029658318, -0.026368314400315285, 0.010541762225329876, -0.01862192526459694, -0.014282836578786373, -0.0013090282445773482, 0.001969626871868968, -0.005027502775192261, -0.006567740812897682, 0.00556641211733222, 0.006355653516948223, -0.020249085500836372, -0.009728183038532734, -0.0006032307865098119, 0.0016236817464232445, 0.008212283253669739, -0.029010707512497902, 0.006386945024132729, -0.035463713109493256, 0.006672045681625605, 0.004937105346471071, -0.024866322055459023, -0.0012707830173894763, -0.02799547277390957, 0.014477538876235485, 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-137,"Connection/SSH issues
+      - export LD_PRELOAD=/absolute/path/to/hack.so
 
-I’m getting connection refused while trying to connect to a login node
-Login nodes are protected against brute force attacks and might ban your IP if
-it detects too many connections/failures. You will be automatically unbanned
-after 1 hour. For any further problem, please submit a support ticket.",70,"[-0.0026325094513595104, -0.03412771224975586, 0.006893478333950043, -0.022112414240837097, -0.038337063044309616, -0.0021030104253441095, -0.022711846977472305, 0.0007538701174780726, 0.01045011356472969, -0.015065747313201427, 0.004295935854315758, 0.005618018563836813, 0.00934449303895235, -0.013707032427191734, -0.0054215374402701855, 0.013880202546715736, 0.020607171580195427, -0.01570514217019081, 0.004778812173753977, -0.023644298315048218, -0.025016333907842636, -0.00813230685889721, -0.015944914892315865, 0.002670806599780917, -0.013960126787424088, -0.012421581894159317, 0.0025059625040739775, -0.01457288023084402, 0.005534763913601637, -0.005714593920856714, 0.013207505457103252, -0.018449213355779648, -0.01642446219921112, 0.02375086396932602, -0.021752754226326942, 0.026415010914206505, 0.011249357834458351, -0.026415010914206505, -0.004322577267885208, -0.011069527827203274, 0.014799332246184349, 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-138,"I’m getting connection refused while trying to connect to a login node
-Login nodes are protected against brute force attacks and might ban your IP if
-it detects too many connections/failures. You will be automatically unbanned
-after 1 hour. For any further problem, please submit a support ticket.",63,"[-0.011293814517557621, -0.027075940743088722, 0.0021611619740724564, -0.028576470911502838, -0.03784523159265518, 0.009501145221292973, -0.0260667335242033, 0.0009087835205718875, 0.01027796883136034, -0.017767339944839478, -0.000852347700856626, 0.0043289633467793465, 0.014168724417686462, -0.015111534856259823, -0.004003627225756645, 0.013471574522554874, 0.02177760750055313, -0.004826927091926336, 0.005019472911953926, -0.026664290577173233, -0.017674388363957405, 0.004923200234770775, -0.018537525087594986, 0.0014183663297444582, -0.01979903317987919, -0.017461923882365227, -0.0017810497665777802, -0.01219014823436737, 0.015018581412732601, -0.007203873246908188, 0.007914301007986069, -0.021140214055776596, -0.013239191845059395, 0.018816383555531502, -0.029293537139892578, 0.024672435596585274, 0.008863751776516438, -0.028603028506040573, 0.002851671539247036, -0.008936786092817783, 0.01581532321870327, 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-139,"Shell issues
 
-How do I change my shell ?
-By default you will be assigned /bin/bash as a shell. If you would like to
-change for another one, please submit a support ticket.",45,"[0.01981346681714058, -0.011463318020105362, -0.003413272090256214, -0.013376069255173206, -0.030261049047112465, -0.004419115372002125, -0.03688312694430351, -0.007248668931424618, -0.0026926924474537373, -0.004854431375861168, 0.028968291357159615, -0.000315562792820856, 0.017373060807585716, -0.0038980557583272457, 0.00041676548426039517, 0.0027883299626410007, 0.009939712472259998, 0.013771810568869114, -0.005345810670405626, -0.023414716124534607, -0.0075454749166965485, 0.021277712658047676, 0.024219391867518425, -0.00517432251945138, -0.02032793127000332, -0.009168015792965889, -0.0013389260275289416, -0.010203540325164795, 0.0022326428443193436, -0.014523720368742943, 0.007387178484350443, 0.010065030306577682, -0.010375028476119041, -0.019760701805353165, -0.01863943412899971, 0.0019341874867677689, 0.00650005741044879, -0.02667299099266529, -0.002834499813616276, -0.009761628694832325, 0.011799697764217854, 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-140,"How do I change my shell ?
-By default you will be assigned /bin/bash as a shell. If you would like to
-change for another one, please submit a support ticket.",40,"[0.013889284804463387, -0.016441982239484787, -0.004061405081301928, -0.014072556048631668, -0.028825830668210983, 0.00022131548030301929, -0.02955891191959381, -0.012802752666175365, -0.007402818650007248, -0.006496284622699022, 0.031836703419685364, 0.0021665189415216446, 0.0185626819729805, -0.004722487647086382, 0.0004888576804660261, 0.002834147308021784, 0.012783116661012173, 0.014255826361477375, -0.0018376139923930168, -0.019243402406573296, -0.004225039388984442, 0.02466297149658203, 0.024165524169802666, -0.01672997698187828, -0.025828048586845398, -0.010197693482041359, 0.00042340392246842384, -0.00952352024614811, 0.009425339289009571, -0.008986799977719784, 0.003848680295050144, 0.010845686309039593, -0.00917007029056549, -0.023864436894655228, -0.0244797021150589, 0.0074093639850616455, 0.007167185191065073, -0.02667894773185253, -0.000737581925932318, -0.01737142540514469, 0.016468163579702377, 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+Mila technical documentation,https://docs.mila.quebec/index.html#mila-technical-documentation,"Mila technical documentation
+Welcome to Mila’s technical documentation. If this is your first time here, we
+recommend you start by checking out the short quick start guide.
 
-How can I get an interactive shell on the cluster ?
-Use salloc [--slurm_options] without any executable at the end of the
-command, this will launch your default shell on an interactive session. Remember
-that an interactive session is bound to the login node where you start it so you
-could risk losing your job if the login node becomes unreachable.
+Introduction
 
+Purpose of this documentation
+Intended audience
 
-How can I reset my cluster password ?
-To reset your password, please submit a support ticket.
-Warning: your cluster password is the same as your Google Workspace account. So,
-after reset, you must use the new password for all your Google services.
 
+Contributing
 
-srun: error: –mem and –mem-per-cpu are mutually exclusive
-You can safely ignore this, salloc has a default memory flag in case you
-don’t provide one.
 
 
-How can I see where and if my jobs are running ?
-Use squeue -u YOUR_USERNAME to see all your job status and locations.
-To get more info on a running job, try scontrol show job #JOBID
+How-tos and Guides
 
+User’s guide
+Quick Start
+Logging in to the cluster
+Running your code
+Portability concerns and solutions
+Singularity
+Sharing Data with ACLs
+Contributing datasets
+Data Transmission using Globus Connect Personal
+JupyterHub
+Advanced SLURM usage and Multiple GPU jobs
+Multiple Nodes
+Frequently asked questions (FAQs)
 
-Unable to allocate resources: Invalid account or account/partition combination specified
-Chances are your account is not setup properly. You should submit a support ticket.
 
+AI tooling and methodology handbook
 
-How do I cancel a job?
 
-To cancel a specific job, use scancel #JOBID
-To cancel all your jobs (running and pending), use scancel -u YOUR_USERNAME
-To cancel all your pending jobs only, use scancel -t PD
 
+Systems and services
 
+Computing infrastructure and policies
+Roles and authorizations
+Overview of available computing resources at Mila
+Node profile description
+Data sharing policies
+Monitoring
+Storage
+Data Transmission
 
-How can I access a node on which one of my jobs is running ?
-You can ssh into a node on which you have a job running, your ssh connection
-will be adopted by your job, i.e.  if your job finishes your ssh connection will
-be automatically terminated. In order to connect to a node, you need to have
-password-less ssh either with a key present in your home or with an
-ssh-agent. You can generate a key on the login node like this:
-ssh-keygen (3xENTER)
-cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
-chmod 600 ~/.ssh/authorized_keys
-chmod 700 ~/.ssh
 
+Computational resources outside of Mila
+Digital Research Alliance of Canada Clusters
 
-I’m getting Permission denied (publickey) while trying to connect to a node
-See previous question
 
 
-Where do I put my data during a job ?
-Your /home as well as the datasets are on shared file-systems, it is
-recommended to copy them to the $SLURM_TMPDIR to better process them and
-leverage higher-speed local drives. If you run a low priority job subject to
-preemption, it’s better to save any output you want to keep on the shared file
-systems, because the $SLURM_TMPDIR is deleted at the end of each job.
 
 
-slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
-You exceeded the amount of memory allocated to your job, either you did not
-request enough memory or you have a memory leak in your process. Try increasing
-the amount of memory requested with --mem= or --mem-per-cpu=.
+General theory
 
+What is a computer cluster?
+Parts of a computing cluster
+The login nodes
+The compute nodes
+The storage nodes
+Different nodes for different uses
 
-fork: retry: Resource temporarily unavailable
-You exceeded the limit of 2000 tasks/PIDs in your job, it probably means there
-is an issue with a sub-process spawning too many processes in your script. For
-any help with your software, please submit a support ticket.",715,"[-0.0015256910119205713, -0.0025252816267311573, -0.00854869931936264, -0.017759768292307854, -0.05089201405644417, 0.01349576748907566, -0.015620867721736431, -0.02241015061736107, -0.04377155005931854, -0.013074886985123158, 0.031186537817120552, 0.010121826082468033, -0.014558318071067333, 0.009438757784664631, 0.005236854776740074, -0.021513191983103752, 0.024756040424108505, -0.02344510145485401, 0.013958046212792397, 0.0073343561962246895, -0.006485695950686932, -0.0032790713012218475, 0.00319282547570765, -0.001376485452055931, -0.006447747815400362, 0.002066453220322728, 0.017511378973722458, -0.016255637630820274, 0.0029892849270254374, -0.004229501821100712, -0.01349576748907566, 0.0052782525308430195, -0.01902930811047554, 0.001923284842632711, -0.04252960532903671, 0.019112104550004005, 0.005616336595267057, -0.016752414405345917, -0.004860822111368179, -0.027557307854294777, 0.00983893871307373, 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-142,"How can I get an interactive shell on the cluster ?
-Use salloc [--slurm_options] without any executable at the end of the
-command, this will launch your default shell on an interactive session. Remember
-that an interactive session is bound to the login node where you start it so you
-could risk losing your job if the login node becomes unreachable.",75,"[-0.0221310593187809, -0.005491217598319054, 0.01285900454968214, -0.01570502109825611, -0.04428981989622116, 0.006460662931203842, -0.033515121787786484, -0.005664332769811153, -0.03321043774485588, 0.00032091248431243, 0.02107851952314377, 0.018114784732460976, -0.0037600642535835505, 0.024665467441082, -0.01653597317636013, -0.025260984897613525, 0.02465161867439747, -0.017574666067957878, 0.022504989057779312, 0.006741109769791365, -0.004445600789040327, 4.820179310627282e-05, 0.02033066190779209, -0.016549821943044662, -0.0019336979603394866, -0.01380767673254013, 0.00815719272941351, -0.014043113216757774, -0.00030360097298398614, -0.008344157598912716, -0.008995071053504944, -0.0022678105160593987, 0.006910762749612331, -0.020635344088077545, -0.03174242004752159, 0.017076093703508377, 0.005782051011919975, -0.01506795547902584, -0.015317241661250591, -0.012699738144874573, 0.024208443239331245, 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-143,"How can I reset my cluster password ?
-To reset your password, please submit a support ticket.
-Warning: your cluster password is the same as your Google Workspace account. So,
-after reset, you must use the new password for all your Google services.",53,"[0.014951542019844055, -0.02654089778661728, -0.012098778039216995, -0.02247825637459755, -0.019714640453457832, 0.03204265609383583, -0.018581176176667213, -0.013168564066290855, 0.0008365667890757322, -0.029138950631022453, 0.025063572451472282, -0.010322167538106441, 0.0068899355828762054, 0.0033207961823791265, 0.0025439271703362465, -0.00860286783427, 0.03573596850037575, -0.014123731292784214, 0.028705941513180733, -0.009971939958631992, -0.008144387975335121, 0.0042600431479513645, 0.006660695653408766, -0.007711378857493401, -0.024172084406018257, -0.01556284911930561, 0.019803788512945175, -0.0035054616164416075, 0.007717746775597334, 0.018810415640473366, -0.0034226805437356234, 0.012665510177612305, 0.0031297628302127123, -0.0037824600003659725, -0.023357009515166283, 0.009035876952111721, 0.010175708681344986, -0.05196106806397438, -0.007055497728288174, -0.01877220906317234, 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-144,"srun: error: –mem and –mem-per-cpu are mutually exclusive
-You can safely ignore this, salloc has a default memory flag in case you
-don’t provide one.",41,"[-0.015243717469274998, -0.003035913221538067, 0.01972389966249466, -0.021416105329990387, -0.03858782723546028, 0.011900918558239937, -0.020070664584636688, -0.029350051656365395, -0.03942006081342697, -0.016034338623285294, 0.04505149647593498, 0.032623499631881714, -0.006536489352583885, -0.010409836657345295, -0.005822157021611929, -0.02706141397356987, 0.005000327713787556, -0.00916842371225357, 0.037117552012205124, 0.0057874806225299835, 0.014099398627877235, 0.005919250659644604, 0.004677837714552879, -0.006571165751665831, 0.00012667699775192887, 0.004116081167012453, 0.005093954037874937, -0.02763010561466217, -0.00945276953279972, -0.005274270661175251, 0.005114759784191847, -0.01799008436501026, -0.011991077102720737, -0.018794573843479156, -0.023413462564349174, 0.006886720657348633, -0.01292040292173624, 0.0020320334006100893, 0.018489422276616096, -0.022581230849027634, 0.012913468293845654, 0.010118555277585983, 0.0009666028781794012, 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-145,"How can I see where and if my jobs are running ?
-Use squeue -u YOUR_USERNAME to see all your job status and locations.
-To get more info on a running job, try scontrol show job #JOBID",48,"[-0.022199230268597603, -0.023132652044296265, 0.007954386062920094, -0.02094113826751709, -0.044127900153398514, -0.0023876684717833996, -0.038175638765096664, -0.006452792324125767, -0.018370842561125755, -0.008705182000994682, -0.0013248854083940387, -0.008860752917826176, -0.011295768432319164, 0.023362625390291214, 0.004224076401442289, -0.046833474189043045, 0.01818145252764225, -0.0048125386238098145, -0.0017992062494158745, -0.023565543815493584, -0.009997094050049782, 0.0042376043274998665, 0.007609425112605095, -0.03035653382539749, -0.00521499291062355, 0.025838226079940796, -0.009043378755450249, -0.007379451300948858, 0.007244172506034374, -0.001849935739301145, -0.021847505122423172, 0.011295768432319164, -0.020832914859056473, -0.023200292140245438, -0.025662362575531006, 0.01600346714258194, 0.019710101187229156, -0.014718319289386272, 0.005624210461974144, -0.01302733551710844, 0.01704511232674122, 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-146,"Unable to allocate resources: Invalid account or account/partition combination specified
-Chances are your account is not setup properly. You should submit a support ticket.",33,"[0.01642603427171707, -0.026543715968728065, 0.0032234068494290113, -0.03039356879889965, -0.037471894174814224, 0.02078920044004917, -0.041740499436855316, -0.013251596130430698, -0.00545395677909255, -0.03871465101838112, 0.03039356879889965, 0.022369666025042534, -0.0052344477735459805, -0.006798028014600277, 0.009692171588540077, 0.007659179158508778, 0.00911807082593441, -0.01872243732213974, 0.01719600521028042, -0.02435537986457348, -0.010529682971537113, 0.01104975026100874, -0.009577350690960884, -0.004423952661454678, -0.006061828229576349, -0.016358492895960808, 0.011205095797777176, -0.0023960263933986425, 0.0020144181326031685, 0.008496690541505814, 0.0008624175097793341, -0.0002906385052483529, -0.013832450844347477, -0.02603040263056755, -0.02411223016679287, 0.004184181336313486, 0.00842914916574955, -0.025409022346138954, 0.019532933831214905, -0.0045016249641776085, 0.012434346601366997, 0.02034342847764492, 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-147,"How do I cancel a job?
 
-To cancel a specific job, use scancel #JOBID
-To cancel all your jobs (running and pending), use scancel -u YOUR_USERNAME
-To cancel all your pending jobs only, use scancel -t PD",55,"[-0.003947847057133913, -0.022938642650842667, 0.019985342398285866, 0.0025183958932757378, -0.03676116466522217, 0.0070191435515880585, -0.02320834994316101, -0.021010233089327812, -0.004504119046032429, -0.0088059576228261, 0.029182376340031624, -0.001229698653332889, -0.010134268552064896, 0.012811117805540562, 0.0025352525990456343, -0.02319486439228058, 0.0024037701077759266, -0.006088652182370424, 0.009095893241465092, -0.0068236058577895164, -0.013984345830976963, 0.030315149575471878, -0.012871801853179932, 0.0015718904323875904, -0.011482806876301765, 0.02074052393436432, 0.008596934378147125, -0.009217261336743832, 0.005987511482089758, 0.015278942883014679, -0.02640438638627529, -0.002919586142525077, -0.019378500059247017, -0.002685277722775936, -0.029209347441792488, -0.01664096675813198, -0.01781419664621353, -0.009338630363345146, 0.003968074917793274, -0.0034657444339245558, 0.02732139267027378, 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-148,"How can I access a node on which one of my jobs is running ?
-You can ssh into a node on which you have a job running, your ssh connection
-will be adopted by your job, i.e.  if your job finishes your ssh connection will
-be automatically terminated. In order to connect to a node, you need to have
-password-less ssh either with a key present in your home or with an
-ssh-agent. You can generate a key on the login node like this:
-ssh-keygen (3xENTER)
-cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
-chmod 600 ~/.ssh/authorized_keys
-chmod 700 ~/.ssh",138,"[-0.009912973269820213, -0.02489018253982067, 0.006198975257575512, -0.021428722888231277, -0.03833195939660072, 0.01231040433049202, -0.04552425071597099, -0.016458768397569656, -0.02462080866098404, -0.010593143291771412, 0.02429755963385105, -0.0038015444297343493, -0.024351434782147408, -0.005761242005974054, -0.008269790560007095, -0.0025034970603883266, 0.021145880222320557, -0.01891007460653782, 0.00501709571108222, -0.019825946539640427, -0.016445299610495567, 0.0015716304769739509, 0.0028755704406648874, -0.024661215022206306, -0.003288049716502428, 0.011603296734392643, 0.022816000506281853, -0.015556364320218563, -0.003730833763256669, -0.010148676112294197, 0.011993889696896076, -0.00013552892778534442, -0.027880236506462097, -0.004639972001314163, -0.023044968023896217, 0.017226485535502434, 0.011313719674944878, -0.029227107763290405, -1.6786567584858858e-06, -0.007401057984679937, 0.02782636135816574, -0.00039943153387866914, 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-149,"I’m getting Permission denied (publickey) while trying to connect to a node
-See previous question",22,"[-0.01213471032679081, -0.005885334685444832, 0.0013929636916145682, -0.0373479425907135, -0.05932525172829628, 0.022341350093483925, -0.02521323226392269, 0.004429169464856386, -0.030067116022109985, -0.02877274714410305, 0.009256087243556976, -0.001906160730868578, 0.0020763836801052094, -0.0030758120119571686, 0.004567370284348726, 0.00886507984250784, 0.017622295767068863, -0.011393144726753235, 0.011534716002643108, 0.004304451402276754, -0.0029831163119524717, 0.006431396584957838, -0.007226894143968821, -0.009195414371788502, -0.005538146942853928, 0.0007428296958096325, 0.00697071710601449, -0.024175040423870087, 0.004385349340736866, -0.021235743537545204, 0.013739189133048058, 0.010422367602586746, -0.010820116847753525, -0.0046786051243543625, -0.03308730944991112, -0.018660487607121468, -0.016651518642902374, -0.034138984978199005, 0.009984170086681843, -0.002300538821145892, -0.001547175575979054, -0.005750504322350025, -0.002201101742684841, 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-150,"Where do I put my data during a job ?
-Your /home as well as the datasets are on shared file-systems, it is
-recommended to copy them to the $SLURM_TMPDIR to better process them and
-leverage higher-speed local drives. If you run a low priority job subject to
-preemption, it’s better to save any output you want to keep on the shared file
-systems, because the $SLURM_TMPDIR is deleted at the end of each job.",104,"[-0.02241329476237297, -0.011722519062459469, 0.002294274279847741, -0.030870884656906128, -0.014457999728620052, 0.024123817682266235, -0.02427314966917038, -0.01657579094171524, -0.0064450097270309925, -0.019766055047512054, 0.02454466186463833, 0.0050127855502069, -0.01267280988395214, 0.020417682826519012, 0.002161912154406309, -0.0164128839969635, 0.02605155110359192, -0.011145556345582008, 0.012048332951962948, -0.02449035830795765, -0.007188273128122091, -0.006183679681271315, -0.0013380438322201371, -0.029160359874367714, -0.004507094621658325, 0.0013651949120685458, 0.01964387483894825, -0.0016782819293439388, 0.019141577184200287, -0.004004797898232937, -0.017770443111658096, -0.010127387940883636, -0.006509494036436081, -0.015449018217623234, -0.0387447252869606, 0.002572573721408844, 0.0058646537363529205, -0.0043577635660767555, 0.019928961992263794, -0.01999683864414692, 0.008688375353813171, 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-151,"slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
-You exceeded the amount of memory allocated to your job, either you did not
-request enough memory or you have a memory leak in your process. Try increasing
-the amount of memory requested with --mem= or --mem-per-cpu=.",75,"[0.007729715667665005, -0.01992078311741352, 0.0009432723745703697, -0.019666656851768494, -0.026556339114904404, 0.02785521373152733, -0.014421743340790272, -0.01671595126390457, -0.037752073258161545, -0.012685204856097698, 0.018127771094441414, 0.025511592626571655, -0.014139379374682903, -0.008463862352073193, -0.011894585564732552, 0.014767639338970184, 0.011118084192276001, -0.008612103760242462, 0.011936940252780914, 0.027756385505199432, -0.010447469539940357, 0.005407271441072226, -0.00971332285553217, 0.010299229063093662, 0.00550962844863534, 0.004997843876481056, 0.010101573541760445, -0.021530259400606155, 0.020471394062042236, 0.01079336553812027, -0.012240481562912464, -0.01056747417896986, -0.026739874854683876, 0.00135799462441355, -0.038655638694763184, 0.0038542691618204117, -0.00012640202476177365, 0.003114828374236822, 0.026273975148797035, -0.016786541789770126, 0.01901721954345703, 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-152,"fork: retry: Resource temporarily unavailable
-You exceeded the limit of 2000 tasks/PIDs in your job, it probably means there
-is an issue with a sub-process spawning too many processes in your script. For
-any help with your software, please submit a support ticket.",59,"[0.00011817013000836596, -0.0350295752286911, -0.02866813726723194, -0.021667778491973877, -0.02411234937608242, 0.011861720122396946, -0.031196044757962227, 0.00831292662769556, -0.012570089660584927, -0.02565409429371357, 0.026723593473434448, 0.025584645569324493, 0.004854416009038687, -0.0156535804271698, -0.0032796822488307953, 0.026918048039078712, 0.014077111147344112, -0.02950151450932026, -0.009139358066022396, -0.015528574585914612, -0.007951796986162663, 0.016320282593369484, -0.005896136164665222, -0.0033335043117403984, -0.021709447726607323, 0.005059287417680025, 0.020014915615320206, -0.009938010014593601, -0.00165893929079175, 0.0011241895845159888, 0.008382374420762062, -0.002932442119345069, -0.009965789504349232, 0.0015226475661620498, -0.036668550223112106, -0.007903183810412884, -0.014667419716715813, -0.03586295247077942, 0.013882656581699848, -0.0031807187478989363, 0.03577961400151253, 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-153,"PyTorch issues
+UNIX
+The workload manager
+Processing data
+Data parallelism
+Model parallelism
+Communication concerns
+Filesystem concerns
 
-I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263
-You are using PyTorch 1.10.x and hitting #67864,
-for which the solution is PR #72232
-merged in PyTorch 1.11.x. For an immediate fix, consider the following compilable Gist:
-hack.cpp.
-Compile the patch to hack.so and then export LD_PRELOAD=/absolute/path/to/hack.so
-before executing the Python process that import torch a broken PyTorch 1.10.
-For Hydra users who are using the submitit launcher plug-in, the env_set key cannot
-be used to set LD_PRELOAD in the environment as it does so too late at runtime. The
-dynamic loader reads LD_PRELOAD only once and very early during the startup of any
-process, before the variable can be set from inside the process. The hack must therefore
-be injected using the setup key in Hydra YAML config file:
-hydra:
-  launcher:
-    setup:
-      - export LD_PRELOAD=/absolute/path/to/hack.so",230,"[-0.015566213056445122, -0.010916379280388355, 0.0037735190708190203, -0.032133929431438446, -0.021360622718930244, 0.0286000557243824, -0.0230631772428751, 0.004091853741556406, -0.03342157602310181, -0.02736963890492916, 0.05436728894710541, -0.009120828472077847, 0.011960803531110287, 0.0017678311560302973, 0.017068468034267426, 0.0101724062114954, -0.0015889913775026798, -0.005994709208607674, 0.010372706688940525, -0.006470422726124525, -0.007575652562081814, 0.0009094002307392657, -0.0115816630423069, -0.004513915628194809, 0.003580372082069516, -0.00867015216499567, 0.011724735610187054, -0.03339296206831932, 0.0025574087630957365, 0.00250017992220819, 0.011553049087524414, -0.013985269702970982, -0.015609134919941425, -0.02918664924800396, -0.013813584111630917, -0.005490380804985762, -0.008112171664834023, -0.023077484220266342, 0.02128908596932888, -0.004771444946527481, 0.019100086763501167, -0.005268619395792484, 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-154,"I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263
-You are using PyTorch 1.10.x and hitting #67864,
-for which the solution is PR #72232
-merged in PyTorch 1.11.x. For an immediate fix, consider the following compilable Gist:
-hack.cpp.
-Compile the patch to hack.so and then export LD_PRELOAD=/absolute/path/to/hack.so
-before executing the Python process that import torch a broken PyTorch 1.10.
-For Hydra users who are using the submitit launcher plug-in, the env_set key cannot
-be used to set LD_PRELOAD in the environment as it does so too late at runtime. The
-dynamic loader reads LD_PRELOAD only once and very early during the startup of any
-process, before the variable can be set from inside the process. The hack must therefore
-be injected using the setup key in Hydra YAML config file:
-hydra:
-  launcher:
-    setup:
-      - export LD_PRELOAD=/absolute/path/to/hack.so",223,"[-0.020267872139811516, -0.008940447121858597, 0.00890471413731575, -0.02798624336719513, -0.02419852465391159, 0.026599794626235962, -0.02421281673014164, 0.004441637080162764, -0.0366479717195034, -0.027857603505253792, 0.05537216737866402, -0.012084968388080597, 0.008411595597863197, -0.002256551757454872, 0.013242724351584911, 0.007589731831103563, -0.0026853501331061125, 0.0027014301158487797, 0.015236636623740196, -0.006546322256326675, -0.009269192814826965, 0.003444680478423834, -0.009119112975895405, -0.011341718025505543, 0.0005976377869956195, -0.01205638237297535, 0.01615140773355961, -0.03470408543944359, 0.003827025881037116, 0.00252991053275764, 0.004920461680740118, -0.014507679268717766, -0.011127319186925888, -0.02911541238427162, -0.01336421724408865, -0.006510589271783829, -0.008618848398327827, -0.02212599851191044, 0.027700377628207207, -0.004791822284460068, 0.01898147724568844, -0.0034464672207832336, 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+
+Software on the cluster
+Cluster software modules
+Containers
+Python Virtual environments
+
+
+
+
+
+Extras
+
+Mila Datasets
+Audio and video resources at Mila
+Visual Studio Code
+Connecting to the cluster
+Activating an environment
+Troubleshooting
+
+
+Who, what, where is IDT
+IDT’s mission
+The IDT team
+
+
+
+
+
+Support
+To reach the Mila infrastructure support, please submit
+a support ticket.
+
+Contribution
+If you find any errors in the documentation, missing or unclear
+sections, or would simply like to contribute, please open an
+issue or make a pull request on the github page.
+
+
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+Audio and video resources at Mila,https://docs.mila.quebec/Audio_video.html#audio-and-video-resources-at-mila,"Audio and video resources at Mila
+See the intranet section on
+audio and video
+for complete information on audio and video systems made available at Mila.
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