beaker-gantry

library3.7.0pypypi✓ verified 83d ago

Beaker Gantry (beaker-gantry) is a command-line interface (CLI) tool and Python library that streamlines running Python experiments in Beaker. It manages containers and boilerplate, eliminating the need for manual Dockerfile creation or complex Beaker YAML experiment specifications. Gantry automatically handles environment setup, repository cloning, and workload management at runtime, making it ideal for Python-based batch jobs from rapidly changing Git repositories. It is actively maintained with frequent updates.

devops workflow

pip install beaker-gantry

github ↗package ↗

INSTALL

IMPORT

SIG · BEAKER-GANTRY

beaker-gantry

devopspythonv3.7.0

Install

6.5s avg

Import

—

Disk

65MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v3.7.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 0.000s · 63.8MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 6.5s · import 0.000s · 62MB

65MB installed

● package 65MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

GantryError

✓ from gantry.exceptions import GantryError

✗ from gantry import GantryError

While primarily a CLI tool, `beaker-gantry` interacts with Beaker. This quickstart demonstrates how to verify your Beaker token (a prerequisite) and shows the most basic CLI command to run an experiment. It also hints at the Python API for logging metrics from within a running experiment. Ensure you have committed and pushed your changes to a Git repository before running `gantry run`.

import os # Ensure BEAKER_TOKEN is set in your environment BEAKER_TOKEN = os.environ.get('BEAKER_TOKEN', 'YOUR_BEAKER_TOKEN') if not BEAKER_TOKEN or BEAKER_TOKEN == 'YOUR_BEAKER_TOKEN': print("Warning: Please set the BEAKER_TOKEN environment variable.") print("You can obtain it from your Beaker profile page.") else: print("Beaker token is set. To run an experiment, ensure you are in a Git repository") print("and execute: gantry run --show-logs -- python -c 'print(\"Hello, Beaker Gantry!\")'") print("For programmatic metric logging from within an experiment:") print(" from gantry.api import write_metrics") print(" write_metrics({'accuracy': 0.95, 'loss': 0.05})")

gantry --version

Debug

Known issues

gotchaAll `gantry` CLI commands must be invoked from the root directory of your Git repository. Running from a subdirectory will likely result in a `GitError` or `DirtyRepoError`.

fix

Change directory to the root of your Git repository before executing `gantry` commands. Alternatively, use the `--remote` option for non-repository contexts (Gantry 3.5.3+).

affects: All versions

breakingGantry relies on Beaker authentication. You must have the `BEAKER_TOKEN` environment variable set or the Beaker command-line client configured locally (e.g., `$HOME/.beaker/config.yml`).

fix

Set the `BEAKER_TOKEN` environment variable (e.g., `export BEAKER_TOKEN='your_token'`) or ensure `beaker-py` can find a valid configuration file.

affects: All versions

gotchaWhen providing a command and its arguments to `gantry run`, use `--` to clearly separate `gantry`'s own options from the command's arguments. Without it, Gantry might misinterpret your command's options as its own.

fix

Always use `--` before your command and its arguments, e.g., `gantry run --show-logs -- python my_script.py --arg value`.

affects: All versions

gotchaFor private Git repositories, Gantry requires a GitHub Personal Access Token (PAT) with `repo` scope to clone your repository on Beaker. Gantry will prompt you for this token the first time it's needed.

fix

Generate a GitHub PAT with `repo` scope and provide it when prompted by Gantry, or set it using `gantry config set-gh-token`.

affects: All versions

gotchaTo save results or metrics from an experiment, your experiment should write files to the `/results` directory within the Beaker container. This directory is automatically configured to be persisted as a Beaker dataset.

fix

Modify your experiment code to output relevant data to the path specified by the `RESULTS_DIR` environment variable (which defaults to `/results` in the container), or use `from gantry.api import write_metrics` for programmatic metric logging.

affects: All versions

Upgrade

Version history

3.7.0latest on PyPI · released Apr 8, 2026

Audit

Dependencies

beaker-pyrequiredGantry is built on top of the Beaker Python client.

Agent activity

27 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/allenai/beaker-gantry ↗

packagehttps://pypi.org/project/beaker-gantry/ ↗