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gslocal - Run Gradescope autograders locally in Docker

PyPI Python License

gslocal runs your Gradescope autograder locally in Docker against any submission. No uploads, and no waiting on Gradescope.

The difference from hitting the "Run Tests" button is that gslocal mirrors Gradescope's exact container workflow: it builds your autograder zip, spins up the same Docker base image Gradescope uses, mounts the submission, runs the grader, and prints a formatted score summary. That catches the bugs your IDE can't, like missing dependencies, setup.sh mistakes, path assumptions, and anything else that only surfaces inside Gradescope's container. Build and image caching mean repeat runs are fast.


Requirements

Installation

pip install gslocal

Quick Start

In your autograder project directory, run:

gslocal init

This walks you through creating a gslocal.toml config file. Run gslocal init --placeholders intead to write a config with sentinel values and no prompts, then edit it yourself.

Then test a submission:

gslocal run path/to/submission.zip

How It Works

gslocal expects your autograder project to produce a Gradescope-compatible zip file via a build command. It is agnostic about your build tool - Maven, Make, a shell script, anything that outputs a zip works.

On each run, gslocal:

  1. Prepares the submission (zip, directory, or GitHub clone)
  2. Runs your build command if source files changed since the last build
  3. Generates a Dockerfile and builds a Docker image if the zip is fresh
  4. Runs the container with the submission mounted, waits for results
  5. Prints a formatted score summary

Build caching: gslocal hashes the files listed in your watch config. If nothing changed and the output zip exists, the build is skipped entirely. The Docker image is similarly only rebuilt when the zip changes.


Configuration

gslocal is configured via a gslocal.toml file in your autograder project root. Run gslocal init to generate one interactively.

[build]
cmd = "mvn clean package"       # command to produce the autograder zip
zip = "target/*.zip"            # glob pattern locating the output zip
watch = ["src/**/*", "pom.xml"] # glob patterns to watch for rebuild triggering

[docker]
setup = "src/main/resources/setup.sh"  # path to setup.sh inside the project (required)
# metadata = "src/main/resources/mock_submission_metadata.json"  # optional

[run]
timeout = 60     # autograder timeout in seconds
verbose = false  # show full build/docker output instead of spinners

Field reference:

Field Required Description
build.cmd yes Shell command to build the autograder zip
build.zip yes Glob pattern to locate the output zip
build.watch yes Glob patterns whose content is hashed to detect source changes
docker.setup yes Path to setup.sh relative to project root
docker.metadata no Path to mock submission metadata JSON; a bundled default is used if omitted
run.timeout no Container timeout in seconds (default: 60)
run.verbose no Show full build and Docker output (default: false)

gslocal locates gslocal.toml by walking up the directory tree from your current working directory, so you can run it from any subdirectory of your project.


Submission Types

gslocal accepts submissions in three formats:

# Zip file
gslocal run test-zips/solution.zip

# Local directory
gslocal run /path/to/student/project/

# GitHub URL (SSH or HTTPS)
gslocal run git@github.com:student/repo.git
gslocal run https://github.com/student/repo

GitHub submissions are cloned with --depth 1.


Commands

gslocal run <submission>

Run the autograder against a submission.

Flag Description
-r, --rebuild Force rebuild of build command and Docker image
-n, --no-build Skip build, use existing zip
-i, --interactive Drop into a shell inside the container
-c, --clean Delete temp files after the run
-t, --timeout SECS Override timeout (also: GSLOCAL_TIMEOUT env var)
-v, --verbose Show full build and Docker output

Timeout precedence: --timeout flag > GSLOCAL_TIMEOUT env var > gslocal.toml value > 60s default.

After each run, the following are available for inspection:

.gslocal/
  temp/
    submission/   # prepared submission files
    results/      # results.json
    autograder/   # extracted autograder zip contents

gslocal init

Generate a gslocal.toml in the current directory. Prompts for each field with example hints. Skipping a required field writes a placeholder value that gslocal will catch and refuse to run with.

Flag Description
--placeholders Write config with all sentinel values, no prompts

gslocal clean

Remove local gslocal state for the current project.

Flag Description
(none) Delete .gslocal/temp/ only
--image Also remove the Docker image for this project
--all Delete .gslocal/ entirely and remove Docker image (full reset)

Project State

gslocal stores all state in a .gslocal/ directory at the project root. Add it to your .gitignore (gslocal init offers to do this automatically):

.gslocal/

Adding gslocal to an Existing Autograder

  1. Install gslocal: pip install gslocal
  2. Run gslocal init in your autograder project root
  3. Fill in your build command, zip output path, and the files to watch for changes
  4. Add .gslocal/ to .gitignore
  5. Run gslocal run <submission> to test

Contributing

Contributions are welcome. See CONTRIBUTING.md for setup instructions, project layout, and pull request guidelines.


Releasing

gslocal is released through the GitHub Actions workflow at .github/workflows/publish.yml.

Before cutting a release:

  1. Update version in pyproject.toml
  2. Add a matching section to CHANGELOG.md, for example ## [0.1.1] - 2026-05-02
  3. Merge the release-ready changes to main

Then, in GitHub:

  1. Open Actions
  2. Select Release and Publish
  3. Click Run workflow
  4. Enter the version number, such as 0.1.1

The workflow will:

  • verify the run is on main
  • verify the requested version matches pyproject.toml
  • verify CHANGELOG.md contains a section for that version
  • fail if that version is already published on PyPI
  • build the package and run twine check
  • create the v<version> tag if needed
  • create the GitHub Release from the matching changelog section if needed
  • publish the package to PyPI

If the GitHub tag or release is created but PyPI publishing fails, fix the issue and rerun the workflow for the same version. If PyPI already has that version, publish a new patch version instead.


License

MIT

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Run your Gradescope autograder locally in Docker mirroring Gradescope's exact container workflow, no uploads.

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