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ML-029 — Containerization Documentation

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
78/100
Issue type
Documentation
Clarity
Clearly specified
Activity status
Active
Tech stack
docker, fastapi, python
Domain
api, devops, documentation, machine-learning

Research direction

Start with the project README and the documented Docker workflow, including the .env configuration and FastAPI /health entry point. Verify the build, docker run command, port mapping, runtime settings, logs, health inspection, and cleanup steps from a clean environment. Done means an unfamiliar developer can start the service, verify /health, inspect it, troubleshoot common issues, and stop the container without source changes.

Written by the indexing model from the issue text.

Description

ML-029 — Containerization Documentation

Goal

Document the containerized development and execution workflow so that developers can build and run the ML Platform serving API without needing implementation-specific knowledge.

Background

Containerization is only useful if developers and operators can reproduce the build and runtime workflow.

The repository documentation should describe the complete path from cloning the project to running the serving API in Docker.

Requirements

Documentation should cover:

  • Docker prerequisites.
  • Building the image.
  • Running the container.
  • Port mapping.
  • Environment configuration.
  • MLflow connectivity.
  • Model configuration.
  • Health checks.
  • Container inspection.
  • Container cleanup.
  • Common troubleshooting scenarios.

Tasks

  • Add a containerization section to the project README.
  • Document Docker prerequisites.
  • Document the image build command.
  • Document container startup.
  • Document port mapping.
  • Document runtime environment variables.
  • Document .env usage.
  • Document MLflow tracking/registry configuration.
  • Document model name and alias configuration.
  • Document /health.
  • Document how to inspect container health.
  • Document how to view application logs.
  • Document how to stop/remove the container.
  • Add common troubleshooting guidance.

Acceptance Criteria

A developer unfamiliar with the container implementation can:

  1. Clone the repository.
  2. Build the Docker image.
  3. Configure the serving environment.
  4. Start the container.
  5. Access the FastAPI service.
  6. Verify /health.
  7. Inspect logs and health state.
  8. Stop and remove the container.

No source-code modifications should be required to perform the standard container workflow.

Example Workflow

docker build -t ml-platform .

docker run --rm \
  --env-file .env \
  -p 8000:8000 \
  ml-platform

Verify:

curl http://localhost:8000/health

Definition of Done

  • README updated.
  • Build instructions verified from a clean environment.
  • Runtime configuration documented.
  • MLflow connectivity documented.
  • Health-check workflow documented.
  • Troubleshooting section added.
  • Documentation reflects the actual implementation.
Dominant language
Python
Stars
1
Forks
0
Avg merge
7m
Merged PRs (30d)
3

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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