beaker-py 2.5.4

A Python Beaker client

Beaker-py

A lightweight pure-Python client for Beaker.

Quick Links

• 📝 Docs and examples:
  • API docs
  • Integration test examples
  • v1 → v2 migration guide
• 🌍 Ecosystem:
  • beaker-gantry
  • beaker-run-action

Installing

Installing with pip

beaker-py is available on PyPI. Just run

pip install beaker-py

Installing from source

To install beaker-py from source, first clone the repository:

git clone https://github.com/allenai/beaker.git

Then create or activate a Python virtual environment, and run:

cd beaker/bindings/python
make dev-install

Quick start

If you've already configured the Beaker command-line client, beaker-py will find and use the existing configuration file (usually located at $HOME/.beaker/config.yml) or BEAKER_TOKEN environment variable.

Then you can instantiate the Beaker client with the .from_env() class method:

from beaker import Beaker

with Beaker.from_env() as beaker:
    ...

With the Python client, you can:

• Query Clusters with beaker.cluster.* methods. For example:

  beaker.cluster.get("ai2/jupiter-cirrascale-2")
  

• Manage Datasets with beaker.dataset.* methods. For example:

  beaker.dataset.create(dataset_name, source_dir)
  

• Manage Experiments with beaker.experiment.* and beaker.workload.* methods. For example:

  beaker.experiment.create(spec=spec, name=name)
  

• Manage Groups with beaker.group.* methods. For example:

  beaker.group.create(name)
  

• Manage Images with beaker.image.* methods. For example:

  beaker.image.update(image, name=name)
  

• Manage Secrets with beaker.secret.* methods. For example:

  beaker.secret.write(name, value)
  

• Manage Workspaces with beaker.workspace.* methods. For example:

  beaker.workspace.create("ai2/new_workspace")
  

• Track Jobs with beaker.job.* methods. For example:

  beaker.job.logs(job, follow=True)
  

• Create and process Queues with beaker.queue.* methods. For example:

  beaker.queue.create("my-work-queue", batch_size=4)
  

If you're coming from v1 of beaker-py, consider reading the migration guide.

Example workflow

Launch and follow an experiment like beaker-gantry does:

import time
from beaker import Beaker, BeakerExperimentSpec, BeakerJobPriority


with Beaker.from_env() as beaker:
    # Build experiment spec...
    spec = BeakerExperimentSpec.new(
        description="beaker-py test run",
        beaker_image="petew/hello-world",
        priority=BeakerJobPriority.low,
        preemptible=True,
    )

    # Create experiment workload...
    workload = beaker.experiment.create(spec=spec)

    # Wait for job to be created...
    while (job := beaker.workload.get_latest_job(workload)) is None:
        print("waiting for job to start...")
        time.sleep(1.0)

    # Follow logs...
    print("Job logs:")
    for job_log in beaker.job.logs(job, follow=True):
        print(job_log.message.decode())

See the integration tests for more examples.

Development

After installing from source, you can run checks and tests locally with:

make checks

Releases

At the moment releases need to be published manually by following these steps:

1. Ensure you've authenticated with PyPI through a ~/.pypirc file and have write permissions to the beaker-py project.
2. Ensure the target release version defined in src/beaker/version.py is correct, or change the version on the fly by adding the Make argument BEAKER_PY_VERSION=X.X.X to the command in the next step.
3. Ensure the CHANGELOG.md has a section at the top for the new release (## vX.X.X - %Y-%m-%d).
4. Run make publish for a stable release or make publish-nightly for a nightly pre-release.