Package: beaker-gantry
Beaker GantryGantry streamlines running Python experiments inBeakerby managing containers and boilerplate for you⚡️Easy to useNo Docker required!🚫 🐳No writing YAML experiment specs.Easy setup.Simple CLI.🏎FastFire off Beaker experiments from your local computer instantly!No local image build or upload.🪶LightweightPure Python (built on top ofbeaker-py).Minimal dependencies.Who is this for?Gantry is for both new and seasoned Beaker users who need to run Python batch jobs (as opposed to interactive sessions) from a rapidly changing repository.
Without Gantry, this workflow usually looks like this:Add a Dockerfile to your repository.Build the Docker image locally.Push the Docker image to Beaker.Write a YAML Beaker experiment spec that points to the image you just uploaded.Submit the experiment spec.Make changes and repeat from step 2.This requires experience with Docker, experience writing Beaker experiment specs, and a fast and reliable internet connection (a luxury that some of us don't have, especially in the WFH era 🙃).With Gantry, on the other hand, that same workflow simplifies down to this:Write a condaenvironment.ymlfile, or simply a PIPrequirements.txtand/orsetup.pyfile.Commit and push your changes.Submit and track a Beaker experiment with thegantry runcommand.Make changes and repeat from step 2.In this README💾Installing🚀Quick start👓Best practices❓FAQAdditional info👋ExamplesSavings results / metrics from an experiment💻For developersCHANGELOGCONTRIBUTINGInstallingInstalling withpipGantry is availableon PyPI. Just runpipinstallbeaker-gantryInstalling from sourceTo install Gantry from source, first clonethe repository:gitclonehttps://github.com/allenai/beaker-gantry.gitcdbeaker-gantryThen runpipinstall-e.Quick startOne-time setupCreate and clone your repository.If you haven't already done so, create a GitHub repository for your project and clone it locally.Everygantrycommand you run must be invoked from the root directory of your repository.Configure Gantry.If you've already configured theBeaker command-line client, Gantry will
find and use the existing configuration file (usually located at$HOME/.beaker/config.yml).
Otherwise just set the environment variableBEAKER_TOKENto your Beakeruser token.The first time you callgantry run ...you'll also be prompted to provide aGitHub personal access tokenwith thereposcope if your repository is not public. This allows Gantry to clone your private repository when it runs in Beaker. You don't have to do this just yet (Gantry will prompt you for it), but if you need to update this token later you can use thegantry config set-gh-tokencommand.Specify your Python environment.Lastly - and this is the most important part - you'll have to create one of several different files that specify your Python environment. There are three options:A condaenvironment.ymlfile.Asetup.pyfile.A PIPrequirements.txtfile.The first method isthe recommended approach, especially if you're already using conda.
But it's perfectly okay to use a combination of these different approaches as well.
This can be useful when, for example, you need touse a CUDA-enabled version of PyTorch on Beaker but a CPU-only version locally.Submit your first experiment with GantryLet's spin up a Beaker experiment that just prints "Hello, World!" from Python.First make sure you've committedandpushed all changes so far in your repository.
Then (from the root of your repository) run:gantryrun--workspace{WORKSPACE}--cluster{CLUSTER}--python-c'print("Hello, World!")'Just replace{WORKSPACE}with the name of your own Beakerprivateworkspace and{CLUSTER}with the name of the Beaker cluster you want to run on.❗Note: Everything after the--is the command + arguments you want to run on Beaker. It's necessary to include the--if any of your arguments look like options themselves (like-cin this example) so gantry can differentiate them from its own options.Trygantry run --helpto see all of the available options.Best practicesUse your own private Beaker workspaceAny authorized contributors to your workspace will have access to the secrets in your workspace, and Gantry needs to store your GitHub personal access token (PAT) as a secret in the workspace (for non-public repos).
That's also why it's important tolimit the scope and lifetime of your GitHub token.Limit the scope and lifetime of your GitHub tokenYour PAT only needs to have thereposcope and should have a short expiration time (e.g. 30 days).
This limits the harm a bad actor could cause if they were able to read your PAT from your Beaker workspace somehow.Use condaAdding aconda environment filewill generally make your exact Python environment easier to reproduce, especially when you have platform-dependent requirements like PyTorch.
You don't necessarily need to write theenvironment.ymlfile manually either.
If you've already initialized a conda environment locally, you can just run:condaenvexport--from-historySeeExporting an Environment File Across Platformsfor more details.It's also okay touse a combination of conda environment and PIP requirements files.FAQCan I use my own Docker/Beaker image?You sure can! Just set the--beaker-imageor--docker-imageflag.Gantry can use any image that has bash installed. This can be useful when you have dependencies that take a long time to download and build (like PyTorch).In this case it works best if you build your image with a conda environment that already has your big dependencies installed. Then when you callgantry run, use the--venvoption to tell Gantry to use that environment instead of creating a new conda environment in the container. You may also want to add arequirements.txtfile to your repository that lists all of your dependencies (including PyTorch and anything else already installed in your image's conda environment) so Gantry can make sure the environment on the image is up-to-date when it runs.For example, you could use one of ourpre-built PyTorch images, such asai2/pytorch1.11.0-cuda11.3-python3.9, like this:gantryrun\--beaker-image'ai2/pytorch1.11.0-cuda11.3-python3.9'\--venv'base'\--piprequirements.txt\--python-c'print("Hello, World!")'Will Gantry work for GPU experiments?Absolutely! This was the main use-case Gantry was developed for. Just set the--gpusoption forgantry runto the number of GPUs you need.
You should also ensure that the way in which you specify your Python environment (e.g. condaenvironment.yml,setup.py, or PIPrequirements.txtfile) will lead to your dependencies being properly installed to support
the GPU hardware specific to the cluster you're running on.For example, if one of your dependencies isPyTorch, you're probably best off writing a condaenvironment.ymlfile since conda is the preferred way to install PyTorch.
You'll generally want to use the latest supported CUDA version, so in this case yourenvironment.ymlfile could look like this:name:torch-envchannels:-pytorchdependencies:-python=3.9-cudatoolkit=11.3-numpy-pytorch-...Can I use both conda environment and PIP requirements files?Yes you can. Gantry will initialize your environment using your conda environment file (if you have one)
and then will also check for a PIP requirements file.How do I use a CUDA-enabled version of PyTorch on Beaker when I'm using a CPU-only version locally?One way to handle this would be to start with arequirements.txtthat lists thetorchversion you need along with any other dependencies, e.g.# requirements.txt
torch==1.11.0
...Then add a condaenvironment.ymlsomewhere in your repository that specifies exactly how to install PyTorch (and a CUDA toolkit) on Beaker, e.g.:# beaker/environment.ymlname:torch-envchannels:-pytorchdependencies:-python=3.9-cudatoolkit=11.3-pytorch==1.11.0# make sure this matches the version in requirements.txtWhen you callgantry run, use the--condaflag to specify the path to your conda env file (e.g.--conda beaker/environment.yml).
Gantry will use that env file to initialize the environment, and then will install the rest of your dependencies from therequirements.txtfile.How can I save results or metrics from an experiment?By default Gantry uses the/resultsdirectory on the image as the location of the results dataset.
That means that everything your experiment writes to this directory will be persisted as a Beaker dataset when the experiment finalizes.
And you can also create Beaker metrics for your experiment by writing a JSON file calledmetrics.jsonin the/resultsdirectory.Can I access data on NFS?Yes. When you choose an on-premise cluster managed by the Beaker team that supports the NFS drive it will be automatically attached to the experiment's container.How can I just see the Beaker experiment spec that Gantry uses?You can use the--dry-runoption withgantry runto see what Gantry will submit without actually submitting an experiment.
You can also use--save-spec PATHin combination with--dry-runto save the actual experiment spec to a YAML file.How can I update Gantry's GitHub token?Just use the commandgantry config set-gh-token.How can I attach Beaker datasets to an experiment?Just use the--datasetoption forgantry run. For example:gantryrun--dataset'petew/squad-train:/input-data'--ls/input-dataHow can I run distributed batch jobs with Gantry?The three options--replicas(int),--leader-selection(flag), and--host-networking(flag) used together give you the ability to run distributed batch jobs. See theBeaker docsfor more information.Why "Gantry"?A gantry is a structure that's used, among other things, to lift containers off of ships. Analogously Beaker Gantry's purpose is to lift Docker containers (or at least themanagementof Docker containers) away from users.