Package: dora-search
Dora The Explorer, a friendly experiment managerTable of ContentInstallationIntroductionMaking your code compatible with DoraThedoracommanddora run: Running XP locallydora launch: Launching XP remotelydora info: Inspecting an XPdora grid: Managing a grid searchThe Dora APISharing XPsAdvanced configurationContributingInstallation# For bleeding edgepipinstall-Ugit+https://github.com/facebookincubator/submitit@main#egg=submitit
pipinstall-Ugit+https://git@github.com/facebookresearch/dora#egg=dora-search# For stable releasepipinstall-Udora-searchWhat's up?Seethe changelogfor details on releases.2022-06-09: version 0.1.10: adding HiPlot support ! Updated PL support, many small fixes.2022-02-28: version 0.1.92021-12-10: version 0.1.8: see changelog, many of small changes.2021-11-08: version 0.1.7: support for job arrays added.2021-10-20: version 0.1.6 released, bug fixes.2021-09-29: version 0.1.5 released.2021-09-07: added support for agit_saveoption. This will ensure that the project git is clean
and make a clone from which the experiment will run. This does not apply todora runfor easier
debugging (but you can force it with--git_save).2021-06-21: added support for Hydra 1.1. Be very careful if you update to Hydra 1.1, there are some non backward compatible changes in the way group config are parsed, seethe Hydra release notesfor more information.(FB Only) If you are using Dora and want to receive updates on bug fixes and new versions, ping me (@defossez) on Workchat.IntroductionDora is an experiment launching tool which provides the following features:Grid search management:automatic scheduling and canceling of the jobs
to match what is specified in the grid search files. Grid search files
are pure Python, and can contain arbitrary loops, conditions etc.Deduplication:experiments are assigned a signature based on their arguments.
If you ask twice for the same experiment to be ran, it won't be scheduled twice,
but merged to the same run. If your code handles checkpointing properly,
any previous run will be automatically resumed.Monitoring:Dora supports basic monitoring from inside the terminal.
You can customize the metrics to display in the monitoring table,
and easily track progress, and compare runs in a grid search.Some Dora concepts:AGridis a python file with anexplorerfunction, wrapped in adora.Explorer.
The explorer function takes adora.Launcheras argument. Call repeatidly
thedora.Launcherwith a set of
hyper-parameters to schedule different experiments.AnXPis a specific experiment. Each experiment is defined by the arguments
passed to the underlying experimental code, and is assigned a signature
based on those arguments, for easy deduplication.Asignatureis the unique XP identifier, derived from its arguments.
You can use the signature to uniquely identity the XP across runs, and easily
access logs, checkpoints etc.ASheepis the association of a Slurm/Submitit job, and an XP. Given an XP,
it is always possible to retrieve the last Slurm job that was associated with it.Making your code compatible with DoraIn order to derive the XP signature, Dora must know about the configuration schema your project is following, as well as
the parsed arguments for a run.
Dora supports two backends for that :argparse, andhydra. On top of that, Dora provides a smooth integration
with Pytorch Lightning for projects that uses it.In all cases, you must have a specific python package (which we will call heremyproj),
with atrainmodule in it, (i.e.myproj.trainmodule, stored in themyproj/train.pyfile.)Thetrain.pyfile must contain amainfunction that is properly decorated, as explained hereafter.Argparse supportHere is a template for thetrain.pyfile:importargparsefromdoraimportargparse_main,get_xpparser=argparse.ArgumentParser("mycode.train")...@argparse_main(dir="./where_to_store_logs_and_checkpoints",parser=parser,exclude=["list_of_args_to_ignore_in_signature, e.g.","num_workers","can_be_pattern_*","log_*"],use_underscore=True,# flags are --batch_size vs. --batch-sizegit_save=False,# if True, scheduled experiments will run from a separate clone of the repo.)defmain():# No need to reparse args, you can directly access them from the current XP# object.xp=get_xp()xp.cfg# parsed argumentsxp.sig# signature for the current runxp.folder# folder for the current run, please put your checkpoint relative# to this folder, so that it is automatically resumed!xp.link# link object, can send back metrics to Dora# If you load a previous checkpoint, you should always make sure# That the Dora Link is consistent with what is in the checkpoint with# history = checkpoint['history']# xp.link.update_history(history)fortinrange(10):xp.link.push_metrics({"loss":1/(t+1)})...Hydra supportThe template fortrain.py:fromdoraimporthydra_main,get_xp@hydra_main(config_path="./conf",# path where the config is stored, relative to the parent of `mycode`.config_name="config"# a file `config.yaml` should exist there.)defmain(cfg):xp=get_xp()xp.cfg# parsed configurationxp.sig# signature for the current run# Hydra run folder will automatically be set to xp.folder!xp.link# link object, can send back metrics to Dora# If you load a previous checkpoint, you should always make sure# That the Dora Link is consistent with what is in the checkpoint with# history = checkpoint['history']# xp.link.update_history(history)fortinrange(10):xp.link.push_metrics({"loss":1/(t+1)})...You can customizedorabehavior from theconfig.yamlfile, e.g.my_config:plopnum_workers:40logs:interval:10level:infodora:exclude:["num_workers","logs.*"]dir:"./outputs"git_save:true# set git_save option for the project.PyTorch Lightning supportDora supports PyTorch Lightning (PL) out of the box. Dora will automatically
capture logged metrics (make sure to useper_epoch=True), and handles distribution
(you should not passgpus=...ornum_nodes=...to PL).importdora.lightning@dora.argparse_main(...)defmain():xp=dora.get_xp()args=xp.cfg# Replace Pytorch lightning `Trainer(...)` with the following:trainer=dora.lightning.get_trainer(...)# Or when using argparse parsing:trainer=dora.lightning.trainer_from_argparse_args(args)Seeexamples/pl/train.pyfor a full example including
automatic reloading of the last checkpoint, logging etc.Important:Dora deactivates the default PL behavior of dumping a mid-epoch
checkpoint upon preemption, as this lead to non deterministic behavior
(as PL would skip this epoch upon restart). Dora assumes you save checkpoints
from time to time (e.g. every epoch). To get back the old behavior,
passno_unfinished_epochs=Falsetoget_trainer. Seeexamples/pl/train.pyfor an example of how to implement checkpointing in a reliable manner.Distributed training support (non PyTorch Lightning)Dora supports distributed training, and makes a few assumptions for you.  You should initialize distributed training through Dora, by calling in yourmainfunction:importdora.distribdora.distrib.init()Note:This is not required for Pytorch Lightning users, see the PL section hereafter, everything will be setup automatically for you :)Git SaveYou can set thegit_saveoption on your project, see hereafter on how to do it for either argparse or Hydra
based projects.
When this option is set, Dora makes individual clones of your project repository for each experiment that is scheduled.
The job will then run from that clean clone. This allows both to keep track of the exact
code that was used for an experiment, as well as preventing code changes to impact pending, or requeued
jobs.
If you reschedule a failed or cancelled job, the clone will however be updated with the current code.In order to use this option, your code should be able to run from a fresh clone of the repository.
If you need to access to resources that are specified with a path relative to the original
repo, usedora.to_absolute_path(). Note that this is similar tohydra.utils.to_absolute_path(). In fact, you can safely replace the Hydra version with this one,
as even whengit_saveis not set, the Dora one automatically falls back to the Hydra one (if Hydra is used).The repository must be completely cleanbefore scheduling remote jobs, and all files should be either
tracked or git ignored. This is very restricive, but this makes implementing this feature
much simpler and safe. Also this forces good practice :)
Only thedora runcommand can be used on a dirty repository, to allow
for easy debugging. For thedora launchanddora gridcommand, you can also use the--no_git_saveoption to temporarily deactivate this feature.The clone for each experiment is located inside thecode/subfolder inside the XP folder (which you can get with thedora infocommand for instance).ThedoracommandDora will install adoracommand that is the main way to interact with it.
Thedoracommand defines 4 sub-commands, detailed in the following sections:dora run: run training code locally (e.g. for debugging).dora launch: launch remote jobs, useful for one-off experiments.dora info: get information on a specific job/XP, logs etc.dora grid: launch an entire grid search defined in a grid file. Only missing XP will be scheduled.
Will also reports status and latest metrics.In order for Dora to find your code, you must pass your training package
(i.e.mycode) asdora -P mycode [run|launch|grid|info].
This flag can be skipped ifmycodeis in the current working directory and is the only folder with atrain.pyfile in it, in which
case Dora will find it automatically.
You can also exportDORA_PACKAGE=mycodeto avoid having to give the-Pflag explicitely.You can use a different script name thantrain.pywith-M, --main_module,
or setting theDORA_MAIN_MODULEenv variable.Attention:those flags should be specified BEFORE therun | launch |info |gridpart:dora -P mycode run, notdora run -P mycode.ExamplesSee theexamplesfolder for a few examples using argparse, Hydra
and Pytorch Lightning, in order to test the commands described here.
To play with them, first install Dora (pip install .from the top-level of the repo), thencd examples, and usedora -P example_name ...to let Dora know which example to use!dora run: Running XP locallyYou can run an XP locally withdorarun[TRAINING_ARGS...]Warning: for theargparsebackend, you must insert--between the dora args and your own training args, i.e.:dorarun--[TRAINING_ARGS...]dora runsupports two flags:-d: distributed training using all available gpus. The master worker output will be to the shell, and other workers will be redirected to a log file in the XP folder.-f sig: this will inject the hyper-parameters from the XP with the given sig on top of the one provided on the command line. Useful to resume locally a remote job that failed.--git_save: clone the repo inside the XP folder and execute from there. This is mostly for debugging,
and in general is not needed.dora launch: Launching XP remotelyDora supports scheduling experiments on Slurm. If you need to schedule many of them, then a grid file is properly better.dora launch [--dev] [-g NUMBER_OF_GPUS] [TRAINING_ARGS ...]Dora will automatically select the appropriate number of nodes and tasks per nodes based on the number of GPUs required, as well as scale required memory.
For instance, if-g 16, Dora will schedule on 2 nodes with 8 gpus each.This command will launch the command, and immediately tail its log and monitor its progress, just like if it were running in locally.
If you want to kill the command if you kill the local process, you can add the-a,--attachflag.
To avoid tailing the log, just pass--no_tail.If a job already exist for the given XP, Dora will not schedule a second one, but reuse the existing job.If a previous run has failed or was canceled, Dora will not automatically start a new one, to give you a chance to inspect the logs.
If you want to reschedule a run, use the-r, --retryflag.Other flags:-f SIG: injects the arguments from the XP matching this signature, on top of the one provided on the command line.-R, --replace: replace any running job (i.e. cancels, and schedules a new one).-D, --replace_done: also reschedule a job even if a previous one completed successfully.-p, --partition PARTITION: partition to use.-c, --comment COMMENT: comment for the job (e.g. if priority is used).--clear: cancel any previous job, clear the XP folder (i.e. delete checkpoints) and reschedule.dora info: Inspecting an XPYou can get information on an XP with thedora infocommand:dorainfo[TRAINING_ARGS...]dorainfo-fSIGNATURE
dorainfo-jSLURM_IDYou can either specify the XP by listing all of its training arguments, by passing its signature, or even the latest Slurm id associated with it.
The info command supports a number of flags:-l: print the entire log for the main task (this only work for remote jobs, not XP ran locally withdora run)-t: tail the log for the main task.dora grid: Managing a grid searchThe main benefit from Dora is the ability to handle arbitarily complex grid searches.
Eachgridis defined by a grid file, inside agridspackage (i.e.mycode.grids.my_grid).
The grid file defines anexplorerfunction, decorated by anExplorerclass.
TheExplorerclass defines various metadata, in particular on which metrics
to display when calling the grid command.
Theexplorerfunction takes adora.Launcheras an argument, and
should repeatidly call it to schedule experiments.Here is an example of grid search file, for instancemycode.grids.mygrid.fromitertoolsimportproductfromdoraimportExplorer,Launcher@Explorerdefexplorer(launcher:Launcher):launcher(batch_size=128)# Schedule an experiments with the given batch size.# For an argparse based project, this will get converted to the `--batch_size=128` flag# You can pass `use_underscore=False` to `argparse_main` to get instead `--batch-size=128`.sub=launcher.bind(lr=0.01)# bind some parameter value, in a new launchersub.slurm_(gpus=8)# all jobs scheduled with `sub` will use 8 gpus.sub()# Job with lr=0.01 and 8 gpus.sub.bind_(epochs=40)# in-place version of bind()sub.slurm(partition="dev")(batch_size=64)# lr=0.01, 8 gpus, dev, bs=64 and epochs=40.launcher.slurm_(gpus=16)# Now using 16 gpus per job, i.e. 2 full nodes.# Nice thing of native python, you can define arbitrary set of XP!forlr,bsinproduct([0.1,0.01,0.001],[16,32,64]):ifbs>32andlr<0.01:# this is just too extreme, let's skipcontinuelauncher(lr=lr,batch_size=bs)# Job arrays are also supported.# The only limitation is that all jobs in an array must use exactly# the same slurm config.withlauncher.job_array():forseedinrange(1,100):launcher(seed=seed)You can then calldoragridmygridThis will do 3 thing:Any XP defined in theexplorerfunction will be scheduled, if not already running
or completed.Any XP that was previously defined in the grid file, but is no longer referenced
will be cancelled.If you just comment one line in the grid file, the corresponding job will automatically be killed.A table containing job status and metadata as well as the latest metrics will
be printed every 5 minutes.The Launcher APIHere is a more comprehensive description of whatLauncherobject can do.launcher.bind_(...): remember the given parameters (command line option for argparse based
project, or overrides for Hydra based ones) for future scheduling, i.e. all experiments
later scheduled with that launcher will have those parameters set.sub = launcher.bind(...): same as bind, but returns a new "sub" launcher, i.e. the objectlauncheris not changed, only experiments scheduled withsubwill use the given params.subalso inherits from all the params already bound to its parent launcher (i.e. previous call tolauncher.bind_).
Creating a sub-launcher is especially recommended inside loops, to avoid leaking params to the next loop iteration.launcher(...): schedules an experiment with the given params, plus all the ones that have
been aggregated through the various calls tobind_and tobind. This is equivalent tolauncher.bind(...)().launcher.slurm_(key=value, ...)andlauncher.slurm(key=value, ...): same asbind_andbindbut for the slurm config (nb of GPUs etc). For a list of possible options, checkoutSlurmConf.Now let us describe the format for passing parameters overrides or command line flags tolauncher.bind_(),launcher.bind()orlauncher():Simple parameters (i.e. not nested) can be passed as kwargs, for instance if you have a--batch_sizeflag, you can
dolauncher.bind(batch_size=64).Command line flags can be explicitely passed as a list of strings, for instancelauncher.bind(['--lr=1e-4']).A dictionary of overrides can be passed, for instancelauncher.bind({'batch_size': 64}). Note that this
also allows for nested keys in Hydra:launcher.bind({'model.channels': 256}). With Hydra, you can
also define new keys with{'+model.activation': 'relu'}. You must not remove keys though.Finally you can combine all of those (for a Hydra project here):launcher.bind(['optim.lr=1e-4'],{'model.channels':256,'seed':42},{'+model.activation':'relu'},batch_size=64)FlagsThedora gridcommand supports the following flags:-r, --retry: failed or cancelled XP within one grid file will
be rescheduled.-R, --replace: any running XP will be replaced by a new job.-D, --replace_done: any XP in the grid that previously completed will be rescheduled.-C, --cancel: cancel all XPs in a grid.--clear: cancel any previous jobs, clear all XP folders (i.e. delete checkpoints) and reschedule. This will ask confirmation first, because this is quite dangerous.-i, --interval INTERVAL: the table monitoring all jobs will be updated everyINTERVALminutes, until all jobs are finished or failed.-T, --trim IDX: trim all the metrics to the number of epochs of the XP
with the given index inside the grid, i.e. pretend that all XPs have at most
as many epochs as the XP with the given index.-L, --trim_last: trim all XPs to the least advanced XP i.e. if the least
advanced XP has only 3 epochs, show the metrics at epoch 3 for all XPs.-f, --folder IDX: only print the folder of the XP with the given idnex.-l, --log IDX: print the full log of the XP with the given index.-t, --tail IDX: tail the log of the XP with the given index.--no_monitoring: only show the table once and return.--dry_run: only simulate actions.PatternsYou can also pass patterns to thegridcommand, for instancedora grid mygrid bs=64will only show XPs which havebs=64in their name. You can see the name by launching
the grid normally. Names are heavily shorten to avoid running out of space, in particular
nested structure will have all their components but the leaf be shorten. You
can negate a query with!, for instancedora grid mygrid '!bs=64'(use quotes
because!will be interpreted by the shell otherwise).
Multiple patterns are interpreted as logical AND between them.Note that with the latest version (be sure to update), the--clear, or-C, --cancelflags
will only apply to the XP matching the pattern. Similarly, only XP matching those patterns
will be scheduled.Explorer classTheExplorerclass allows to customize which metric to report, and with what precision. It also gives you a chance to reorganize metrics or further post process them (for instance, extracting max, min etc.).
SeeCustomize metrics displayedhereafter for more explanation.By convention, files starting with_inside thegridsfolder are ignored by Dora, and are a good place to put utility code such as your customExplorerclasses.
For an example with detailed comments, go checkoutthe Explorer classes for BrainMagick.HiPlot supportDora supports HiPlot out of the box. Make sure it is installed (pip install hiplot), then
you can runpython-mhiplotdora.hiplot.load--port=XXXXIn the prompt, you can type any number of grid names or XP sig, separated by spaces.
You can customize the metrics displayed by inheritingdora.hiplot.HiPlotExplorerin a class insideyourproject.grids._hiplot. Then, select your explorer with
the commandexplorer=MyExplorerinside the HiPlot prompt (along the grid names and XP
sigs, in any order).The Dora APIDora provides some API, including the possibility to run grid searches
directly from an IPython notebook. See theDora API.DecoratedMainThe most useful class is theDecoratedMain, which is the decorated main function in your project. You can use it
to retrieve anXP objectfrom a list of argv, or a signature:frommyproj.trainimportmainxp=main.get_xp_from_sig('ae43f645')xp2=main.get_xp_from_argv(xp.argv+['batch_size=32'])xp2_name=main.get_name(xp2)# See API in dora.names.NameMixinwithxp2.enter():# You can pretend to be in an XP with this....Advanced customizationIf you want to do some advance customization of the behavior ofDecoratedMain(e.g. custom naming for XP, or weird parsing of flags), feel free
to inheritdora.main.ArgparseMainordora.hydra.HydraMainand use your
custom class instead.Grid APIYou can schedule and manage grids from the Dora API rather than the command-line.
This is useful to manage XPs from a notebook for instance!
See thedora.grid.run_grid.
Flags are passed with as an instance ofdora.grid.RunGridArgs.
Submission rules (e.g. cancel, retry etc.) are passed as adora.conf.SubmitRules.importdoraimportdora.gridfrommyproj.trainimportmain@dora.Explorerdefexplorer(launcher):launcher.slurm_(gpus=2,partition='learnlab')launcher.bind_({'epochs':200,'model.depth':10,'batch_size':128})launcher()forpin[0.005,0.01,0.05,0.1]:sub=launcher.bind({'task.penalty':p})sub()sub(lr=1e-4)args=dora.grid.RunGridArgs(jupyter=True,monitor=False,interval=1)rules=dora.conf.SubmitRules(retry=True)# Should we reschedule failed jobs?# The run_grid function returns a list of sheeps# each sheep as 2 attributues: sheep.xp and sheep.job_id.sheeps=dora.grid.run_grid(main,explorer,grid_name='jupy',rules=rules,args=args)args.monitor=Trueargs.jupyter=True# The jupyter flag will make the grid API use the display API to clear the cell# output and update it regularly. This one will not return until all jobs# are done or failed.# In the following, `grid_name` should be unique. It will be used# to determine which experiments were previously scheduled with that grid# and should potentially be cancelled if no longer needed.dora.grid.run_grid(main,explorer,grid_name='jupy',rules=rules,args=args)# You can retrieve the short names by using `main.get_names()`short_names,ref_name=main.get_names([sheep.xpforsheepinsheeps])Sharing XPsAt the moment, checkpoints and metrics cannot be directly shared (you can always copy
manually an XP folder in someone else XPs folder). However, there are now two ways to share easily an XP hyper-params
using its signature. This is useful if you want someone else to quickly reproduce your XP!Doraimport/exportcommandGiven a list of signatures, you can export its hyper-params to a compact textual format withdora export:doraexportSIG1[OTHER_SIG...]Copy paste the given string and your teammate can import it withdoraimport# now paste to stdinThe command will show you the list of imported XPs. Once an XP is imported, you can simply run it or query hyper params
withdora run -f SIG,dora info -f SIGetc. From a grid file, you can programmatically retrieve the hyper-params from that XP, e.g.frommyproject.trainimportmainxp=main.get_xp_from_sig(SIG)launcher.bind_(xp.argv)Secondary shared XPs repositoryYou can now configure a secondary shared XPs repository where only mappings from SIG -> hyper params are stored. With Hydra you can adddora:dir:outputsshared:/shared_folder/shared_xpsThen other teammates can reference any SIG from an XP launched by other team members within the Dora commands.Advanced configurationSetting SLURM default parametersSlurm configuration is detailed indora/conf.py.
It is a bit different from the usual Slurm config, as it tries to make it as easy as possible to change the number of GPUs without requiring to manually compute the number of nodes, tasks per nodes etc.Slurm config flagsgpus (int): number of GPUs to schedule. Number of nodes
and tasks per nodes will be automatically inferred.mem_per_gpu (float): amount of memory in GB to schedule per gpus.time (int): maximum duration for the job in minutes.cpus_per_gpu (int): number of cpus per gpu, this will set
thecpus_per_taskautomatically, based on the
number of gpus andone_task_per_node, unlesscpus_per_taskis explicitely provided.cpus_per_task (int or None): number of cpus per task.partition (str): partition name.comment (str): comment for the job.setup (List[str]): list of shell commands to execute
before the actual command. Use it formodule load.max_num_timeout (int): maximum number of requeue.one_task_per_node (bool): if True, schedules a single task
per node, otherwise, will schedule one task per gpu (default is False).Default Slurm configYou can pass an instance ofSlurmConfigtoargparse_mainthat will be used as the default
config for alldora launchcommands and grid files.
Grid files can override any field defined onSlurmConfigwith thelauncher.slurm(return new launcher) andlauncher.slurm_(in-place) methods.For Hydra, the default slurm configuration is taken from theslurmentry in the yaml file, for instance, you can
have their:my_param:whateverbatch_size:42dora:dir:outputsslurm:partition:devlabmem_per_gpu:30# this is in GBCustomize metrics displayed in ExplorerMetrics are formatted with thetreetable,
which is not heavily documented, but it should be easy enough to get how it works
with this example:fromdoraimportExplorerimporttreetableasttclassMyExplorer(Explorer):test_metrics=['sisnr','pesq']defget_grid_metrics(self):"""Return the structure of the metrics that should be displayed in the tracking table."""# This will return a list of `tt.group`, each group will# be in separate parts of the table.return[tt.group("train",[tt.leaf("epoch"),tt.leaf("loss",".3f"),# The second argument of tt.leaf is a formatting string.],align=">"),# Align can be left (<) or right (>) and will apply on all# leaves inside the group.tt.group("valid",[tt.leaf("best",".3f"),tt.leaf("loss",".3f"),],align=">"),tt.group("test",[tt.leaf(name,".3f")fornameinself.test_metrics],align=">")]# In practice you can even have deeply nested groups etc. but honestly# you probably don't need that.defprocess_history(self,history):"""This process the history obtained from the Dora Linkinto the right format for the `get_grid_metrics()` layout.This should return a dictionary, with one key per group, eachbeing a sub-dict with one key per metric.It is fine for a key to be absent, things won't crash, and it willjust not show anything there in the table."""train={'epoch':len(history),}valid={}test={}best=float('inf')formetricsinhistory:train.update(metrics['train'])valid.update(metrics['valid'])# Let say you forgot to compute best valid loss, you can# fill in it here on the fly.best=min(valid['loss'],best)valid['best']=best# The reason for having this for loop is also if some metrics# are not defined with every epoch. Let say you compute test metrics# only every 10 epochs, then this will automatically fill the# test metrics of the last epoch which evaluated them.if'test'inmetrics:test.update(metrics['test'])return{"train":train,"valid":valid,"test":test}LicenseDora is released under the MIT license as found in theLICENSEfile.ContributingBefore submitting any change, please runmaketo run unit tests and code linting.