# mlflow
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/17065/merge
# mlflow-skinny
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/17065/merge#subdirectory=libs/skinny

%sh curl -LsSf https://raw.githubusercontent.com/mlflow/mlflow/HEAD/dev/install-skinny.sh | sh -s pull/17065/merge

Related Issues/PRs

Add a full Docker Compose setup including PostgreSQL, MinIO, and MLflow services for end-to-end experiment tracking.

What changes are proposed in this pull request?

This PR introduces a comprehensive docker-compose.yml that spins up a PostgreSQL container for the MLflow backend store, a MinIO container for S3-compatible artifact storage (with an automated bucket-creation helper), and the MLflow server itself—configured to connect to both services and serve artifacts via its built-in proxy. With these three services orchestrated on a shared mlflow-network and durable named volumes (pgdata, minio-data), you can now bootstrap a complete MLflow tracking stack locally or in production with a single docker compose up -d.

How is this PR tested?

• Existing unit/integration tests
• New unit/integration tests
• Manual tests

Does this PR require documentation update?

• No. You can skip the rest of this section.
• Yes. I've updated:
  • Examples
  • API references
  • Instructions

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

This PR introduces a comprehensive docker-compose.yml that spins up a PostgreSQL container for the MLflow backend store, a MinIO container for S3-compatible artifact storage (with an automated bucket-creation helper), and the MLflow server itself—configured to connect to both services and serve artifacts via its built-in proxy. With these three services orchestrated on a shared mlflow-network and durable named volumes (pgdata, minio-data), you can now bootstrap a complete MLflow tracking stack locally or in production with a single docker compose up -d.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/tracking: Tracking Service, tracking client APIs, autologging
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflows
• area/gateway: MLflow AI Gateway client APIs, server, and third-party integrations
• area/prompts: MLflow prompt engineering features, prompt templates, and prompt management
• area/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionality
• area/projects: MLproject format, project running backends
• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages

How should the PR be classified in the release notes? Choose one:

• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

Should this PR be included in the next patch release?

Yes should be selected for bug fixes, documentation updates, and other small changes. No should be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.

• Yes (this PR will be cherry-picked and included in the next patch release)
• No (this PR will be included in the next minor release)