Package: runx
runx - An experiment management toolrunxhelps to automate common tasks while doing research:hyperparameter sweepslogging, tensorboard, checkpoint managementexperiment summarizationcode checkpointingunique, per-run, directory creationTable of ContentsQuick-start InstallationIntroduction: a simple examplerunx ArchitectureCreate a project-specific configuration fileRun directory, logfilesExperiment yaml detailsBooleansLists, Inheritancelogx - logging, tensorboarding, checkpointingsumx - summarizing your runsNGC SupportQuick-start InstallationInstall with pip:> pip install runxInstall with source:> git clone https://github.com/NVIDIA/runx
> cd runx
> python setup.py .Introduction exampleSuppose you have an existing project that you call as follows:>pythontrain.py--lr0.01--solversgdTo run a hyperparameter sweep, you'd normally have to code up a one-off script to generate the sweep. But with runx, you would simply define a yaml that defines lists of hyperparams that you'd like to use.Start by creating a yaml file calledsweep.yml:CMD: 'python train.py'

HPARAMS:
  lr: [0.01, 0.02]
  solver: ['sgd', 'adam']Now you can run the sweep with runx:>python-mrunx.runxsweep.yml-i

pythontrain.py--lr0.01--solversgd
pythontrain.py--lr0.01--solveradam
pythontrain.py--lr0.02--solversgd
pythontrain.py--lr0.02--solveradamYou can see that runx automatically computes the cross product of all hyperparameters, which in this case results in 4 runs. It then builds commandlines by concatenating the hyperparameters with the training command.A few useful runx options:-n     don't run, just print the command
-i     interactive mode (as opposed to submitting jobs to a farm)runx is especially useful to launch batch jobs to a farm.Farm support is simple. First create a .runx file that configures the farm:LOGROOT:/home/logsFARM:bigfarmbigfarm:SUBMIT_CMD:'submit_job'RESOURCES:gpu:2cpu:16mem:128LOGROOT: this is where the output of runs should goFARM: you can define multiple farm targets. This selects which one to useSUBMIT_CMD: the script you use to launch jobs to a farmRESOURCES: the arguments to present to SUBMIT_CMDNow when you run runx, it will generate commands that will attempt to launch jobs to a farm using your SUBMIT_CMD. Notice that we left out the-icmdline arg because now we want to submit jobs and not run them interactively.>python-mrunx.runxsweep.yml

submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.01 --solver sgd"submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.01 --solver adam"submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.02 --solver sgd"submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.02 --solver adam"Unique run directoriesWe want the results for each training run to go into a unique output/log directory.  We don't want things like tensorboard files or logfiles to write over each other.runxsolves this problem by automatically generating a unique output directory per run.You have access to this unique directory name within your experiment yaml via the special variable:LOGDIR. Your training
script may use this path and write its output there.CMD: 'python train.py'

HPARAMS:
  lr: [0.01, 0.02]
  solver: ['sgd', 'adam']
  logdir: LOGDIRIn the above experiment yaml, we have passed LOGDIR as an argument to your training script. When we launch the jobs, runx automatically generates unique output directories and passes the paths to your training script:>python-mrunx.runxsweep.yml

submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.01 --solver sgd  --logdir /home/logs/athletic-wallaby_2020.02.06_14.19"submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.01 --solver adam  --logdir /home/logs/industrious-chicken_2020.02.06_14.19"submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.02 --solver sgd  --logdir /home/logs/arrogant-buffalo_2020.02.06_14.19"submit_job--gpu2--cpu16--mem128-c"python train.py --lr 0.02 --solver adam  --logdir /home/logs/vengeful-jaguar_2020.02.06_14.19"Summarization with sumxAfter you've run your experiment, you will likely want to summarize the results.  You might want to know:Which training run was best?How long was an epochWhat about other metrics?You summarize your runs with on the commandline withsumx. All you need to do is tellsumxwhich experiment you want summarized.sumxknows what your LOGROOT (it'll get that from the .runx file) and so it looks within that directory for your experiment directory.In the following example, we asksumxto summarize thesweepexperiment.>python-mrunx.sumxsweep--sortwithacclrsolveraccepochepoch_time
-----------------------------------
run40.02adam99.1105:11
run30.02sgd99.0105:05
run10.01sgd98.2105:15
run20.01adam98.1105:12sumxis part of the runx suite, and is able to summarize the different hyperparmeters used as well as the metrics/results of your runs. Notice that we used the --sortwith feature of sumx, which sorts your results so you can easily locate your best runs.This is the basic idea.
The following sections will go into more details about all the various features.runx Architecturerunx consists of three main modules:runxLaunch sweeps of training runs using a concise yaml format that allows for multiple values for each hyperparameterIn particular, when you call runx:Calculate cross product of all hyperparameters -> runsFor each run, create an output directory, copy your code there, and then launch the training commandlogxLogging of metrics, messages, checkpoints, tensorboardsumxSummarize the results of training runs, showing results and unique hyperparametersThese modules are intended to be used jointly, but if you just want to use runx, that's fine.
However using sumx requires that you've used logx to record metrics.Create a project-specific configuration fileIn order to userunx, you need to create a configuration file in the directory where you'll call therunxCLI.The .runx file defines a number of critical fields:LOGROOT- the root directory where you want your logs placed. This is a path that any farm job can write to.FARM- if defined, jobs should be submitted to this farm, else run interactivelyFor a given farm, these fields are required:SUBMIT_CMD- the farm submission commandRESOURCES- hyperparameters passed to theSUBMIT_CMD. You can list any number of these items, the ones shown below are just examples.CODE_IGNORE_PATTERNS- ignore these files patterns when copying code to output directoryHere's an example of such a file:LOGROOT:/home/logsCODE_IGNORE_PATTERNS:'.git,*.pyc,docs*,test*'FARM:bigfarm# Farm resource needsbigfarm:SUBMIT_CMD:'submit_job'RESOURCES:image:mydocker-image-big:1.0gpu:8cpu:64mem:450smallfarm:SUBMIT_CMD:'submit_small'RESOURCES:image:mydocker-image-small:1.2gpu:4cpu:32mem:256Run directory, logfilesrunx has two level of experiment hierarchy:experimentsandruns. Anexperimentcorresponds to a single yaml file, which may contain manyruns.runxcreates both a parent experiment directory and a unique subdirectory for each run. The name of the experiment directory isLOGROOT/<experiment name>, so in the example of sweep.yml, the experiment name issweep, derived from the yaml filename.For example, this might be the directory structure for the sweep study:/home/logssweep/curious-rattlesnake_2020.02.06_14.19/ambitious-lobster_2020.02.06_14.19/...The individual run directories are named with a combination ofcoolnameand date. The use ofcoolnamemakes it much easier to refer to a given run than referring to a date code.If you include the RUNX.TAG field in your experiment yaml or if you supply the --tag argument to therunxCLI, the names will include that tag.Staging of coderunxactually makes a copy of your code within each run's log directory. This is done for a number of reasons:If you wish to continue modifying your code, while a training run is going on, you may do so without worry whether it will affect the running job(s)In case your job dies and you must restart it, the code and training environment is self-contained within the logdir of a run.This is also useful for documentation purposes: in case you ever want to know exactly the state of the code for a given run.Experiment yaml detailsSpecial variablesCMD- Your base training command. You typically don't include any args here.HPARAMS- All hyperparmeters. This is a datastructure that may either be a simple dict of params or may be a list of dicts. Furthermore, each hyperparameter may be a scalar or list or boolean.PYTHONPATH- This is field optional. For the purpose of altering the default PYTHONPATH which is simply LOGDIR/code. Can be a colon-separated list of paths. May include LOGDIR special variable.HPARAMSA simple example of HPARAMS is:CMD:"pythontrain.py"HPARAMS:logdir:LOGDIRadam:truearch:alexnetlr:[0.01,0.02]epochs:10RUNX.TAG:'alexnet'Here, there will be 2 runs that will be created.BooleansIf you want to specify that a boolean flag should be on or off, this is done usingtrueandfalsekeywords:some_flag: [true, false]This would result having one run with--some_flagand another run without that flagIf instead you want to pass an actual string, you could instad do the following:some_arg: ['True', 'False']This would result in one run with--some_arg Trueand other run with--some_arg FalseIf you'd like an argument to not be passed into your script at all, you can set it toNonesome_arg: NoneLists, InheritanceOftentimes, you might want to define separate lists of hyperparameters in your experiment.
For example:arch = alexnet with lr=[0.01, 0.02]arch = resnet50 with lr=[0.002, 0.005]You can do this with hparams defined as follows:PYTHONPATH:LOGDIR/code:LOGDIR/code/libCMD:"pythontrain.py"HPARAMS:[{logdir:LOGDIR,adam:true,arch:alexnet,lr:[0.01,0.02],epochs:10,RUNX.TAG:'alexnet',},{arch:resnet50,lr:[0.002,0.005],RUNX.TAG:'resnet50',},{RUNX.SKIP:true,arch:resnet50,lr:[0.002,0.005],RUNX.TAG:'resnet50',}]You might observe that hparams is now a list of two dicts.
The nice thing is that runx assumes inheritance from the first item in the list to all remaining dicts, so that you don't have to re-type all the redundant hyperparms.When you pass this yaml to runx, you'll get the following out:submit_job...--namealexnet_2020.02.06_6.32-c"python train.py --logdir ... --lr 0.01 --adam --arch alexnet --epochs 10submit_job ... --name alexnet_2020.02.06_6.40  -c "pythontrain.py--logdir...--lr0.02--adam--archalexnet--epochs10submit_job...--nameresnet50_2020.02.06_6.45-c"python train.py --logdir ... --lr 0.002 --adam --arch resnet50 --epochs 10submit_job ... --name resnet50_2020.02.06_6.50 -c "pythontrain.py--logdir...--lr0.005--adam--archresnet50--epochs10Because of inheritance, adam, arch, and epochs params are set identically in each run.This is also showing the use of the magic variableRUNX.TAG, which allows you to add a tag to a subset of your experiment. This is the same as if you'd used the --tag  option to runx.py, except that here you can specify the tag within the hparams data structure. The value ofRUNX.TAGis not passed to your training script.A very useful feature of RUNX.TAG is that you can reference other hyperparameters, for example:arch:resnet50,RUNX.TAG:'{arch}-lrstudy'This results in the tag becomingresnet50-lrstudy.runxperforms simple string matching and substitution when it finds curly braces.logx - logging, tensorboarding, checkpointingIn order to use sumx, you need to export metrics with logx.
logx helps to write metrics in a canonical way, so that sumx can summarize the results.logx can also make it easy for you to output log information to a file (and stdout)
logx can also manage saving of checkpoints automatically, with the benefit being that logx will keep around only the latest and best checkpoints, saving much disk space.The basic way you use logx is to modify your training code in the following ways:At the top of your training script (or any module that calls logx functions:fromrunx.logximportlogxBefore using logx, you must initialize it as follows:logx.initialize(logdir=args.logdir,coolname=True,tensorboard=True)Make sure that you're only calling logx from rank=0, in the event that you're using distributed data parallel.Then, substitute the following logx calls into your code:FromToWhatprint()logx.msg()stdout messageswriter.add_scalar()logx.add_scalar()tensorboard scalar writeswriter.add_image()logx.add_image()tensorboard image writeslogx.save_model()save latest/best modelsFinally, in order for sumx to be able to read the results of your run, you have to push your metrics to logx. You should definitely push the 'val' metrics, but can push 'train' metrics if you like (sumx doesn't consume them at the moment).# define which metrics to recordmetrics={'loss':test_loss,'accuracy':accuracy}# push the metrics to logfilelogx.metric(phase='val',metrics=metrics,epoch=epoch)Some important points of logx.metric():Thephaseargument describes whether the metric is a train or validation metric.You should set idx == epoch for validation metrics. And for training, idx is typically the iteration count.Here's a final feature of logx: saving of the model. This feature helps save not only the latest but also the best model.save_dict={'epoch':epoch+1,'arch':args.arch,'state_dict':model.state_dict(),'best_acc1':best_acc1,'optimizer':optimizer.state_dict()}logx.save_model(save_dict,metric=accuracy,epoch=epoch,higher_better=True)You do have to tell save_model whether the metric is better when it's higher or lower.sumx - summarizing your runssumx summarizes the results of your runs. It requires that you've logged your metrics with logx.metric().
We chose this behavior instead of reading Tensorboard files directly because that would be much slower.>python-mrunx.sumxsweeplrsolveraccepochepoch_time
run40.02adam99.1105:21
run30.02sgd99.0105:02
run10.01sgd98.2105:40
run20.01adam98.1105:25A few features worth knowing about:use--sortwithto sort the output by a particular field (like accuracy) that you care about mostsumx tells you what epoch your run is current onsumx tells you the average epoch time, which is handy if you are monitoring training speeduse the optional--ignoreflag to limit what fields sumx prints outNGC SupportNGC support is now standard. Your.runxfile should look like the following.LOGROOT:/path/to/logrootFARM:ngcngc:NGC_LOGROOT:/path/to/ngc_logrootWORKSPACE:<your ngc workspace>SUBMIT_CMD:'ngcbatchrun'RESOURCES:image:nvidian/pytorch:19.10-py3instance:dgx1v.16g.1.normace:nv-us-west-2result:/resultNecessary steps:Fill out a path to LOGROOT, which is a client-side staging directory for the log directoryCreate a RW NGC workspace and fill inWORKSPACEwith itMount this workspace on your local machine and fill inNGC_LOGROOTwith this path. When the
job is launched, this is also the path used to mount the workspace on the running instance.Fill out any necessary fields underRESOURCES. Recall that these parameters are passed on
to theSUBMIT_CMD, which must bengc batch run.You should be able to launch jobs to NGC using this configuration. When jobs write their results, you should also be able to see the results in the mounted workspace,
and then you should be able to run runx.sumx in order to summarize the results of those runs.