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Contributing to MLflow

We welcome community contributions to MLflow. This page provides useful information about contributing to MLflow.

Table of Contents

Contribution process

The MLflow contribution process starts with filing a GitHub issue. MLflow defines four categories of issues: feature requests, bug reports, documentation fixes, and installation issues. Details about each issue type and the issue lifecycle are discussed in the MLflow Issue Policy.

MLflow committers actively triage and respond to GitHub issues. In general, we recommend waiting for feebdack from an MLflow committer or community member before proceeding to implement a feature or patch. This is particularly important for significant changes.

After you have agreed upon an implementation strategy for your feature or patch with an MLflow committer, the next step is to introduce your changes (see developing changes) as a pull request against the MLflow Repository or as a standalone MLflow Plugin. MLflow committers actively review pull requests and are also happy to provide implementation guidance for Plugins.

Once your pull request against the MLflow Repository has been merged, your corresponding changes will be automatically included in the next MLflow release. Every change is listed in the MLflow release notes and Changelog. Congratulations, you have just contributed to MLflow! We appreciate your contribution!

Contribution guidelines

In this section, we provide guidelines to consider as you develop new features and patches for MLflow.

Write designs for significant changes

For significant changes to MLflow, we recommend outlining a design for the feature or patch and discussing it with an MLflow committer before investing heavily in implementation. This is particularly important if your proposed implementation:

  • Introduces changes or additions to the MLflow REST API
    • The MLflow REST API is implemented by a variety of open source and proprietary platforms. Changes to the REST API impact all of these platforms. Accordingly, we encourage developers to thoroughly explore alternatives before attempting to introduce REST API changes.
  • Introduces new user-facing MLflow APIs
    • MLflow's API surface is carefully designed to generalize across a variety of common ML operations. It is important to ensure that new APIs are broadly useful to ML developers, easy to work with, and simple yet powerful.
  • Adds new library dependencies to MLflow
  • Makes changes to critical internal abstractions. Examples include: the Tracking Artifact Repository, the Tracking Abstract Store, and the Model Registry Abstract Store.

Make changes backwards compatibile

MLflow's users rely on specific platform and API behaviors in their daily workflows. As new versions of MLflow are developed and released, it is important to ensure that users' workflows continue to operate as expected. Accordingly, please take care to consider backwards compatibility when introducing changes to the MLflow code base. If you are unsure of the backwards compatibility implications of a particular change, feel free to ask an MLflow committer or community member for input.

Consider introducing new features as MLflow Plugins

MLflow Plugins enable integration of third-party modules with many of MLflow’s components, allowing you to maintain and iterate on certain features independently of the MLflow Repository. Before implementing changes to the MLflow code base, consider whether your feature might be better structured as an MLflow Plugin. MLflow Plugins are a great choice for the following types of changes:

  1. Supporting a new storage platform for MLflow artifacts
  2. Introducing a new implementation of the MLflow Tracking backend (Abstract Store) for a particular platform
  3. Introducing a new implementation of the Model Registry backend (Abstract Store) for a particular platform
  4. Automatically capturing and recording information about MLflow Runs created in specific environments

MLflow committers and community members are happy to provide assistance with the development and review of new MLflow Plugins.

Finally, MLflow maintains a list of Plugins developed by community members, which is located at https://mlflow.org/docs/latest/plugins.html#community-plugins. This is an excellent way to inform MLflow users about your exciting new Plugins. To list your plugin, simply introduce a new pull request against the corresponding docs section of the MLflow code base.

For more information about Plugins, see https://mlflow.org/docs/latest/plugins.html.

Developing and testing changes to MLflow

The majority of the MLflow codebase is developed in Python. This includes the CLI, Tracking Server, Artifact Repositories (e.g., S3 or Azure Blob Storage backends), and of course the Python fluent, tracking, and model APIs.

Prerequisites

First, install the Python MLflow package from source - this is required for developing & testing changes across all languages and APIs. We recommend installing MLflow in its own conda environment by running the following from your checkout of MLflow:

conda create --name mlflow-dev-env python=3.6
source activate mlflow-dev-env
pip install -r dev-requirements.txt
pip install -r test-requirements.txt
pip install -e .  # installs mlflow from current checkout

You may need to run conda install cmake for the test requirements to properly install, as onnx needs cmake.

Ensure Docker is installed.

npm is required to run the Javascript dev server and the tracking UI. You can verify that npm is on the PATH by running npm -v, and install npm if needed.

If contributing to MLflow's R APIs, install R. For changes to R documentation, also install pandoc 2.2.1 or above, verifying the version of your installation via pandoc --version. If using Mac OSX, note that the homebrew installation of pandoc may be out of date - you can find newer pandoc versions at https://github.com/jgm/pandoc/releases.

If contributing to MLflow's Java APIs or modifying Java documentation, install Java and Apache Maven.

Install Node Modules

Before running the Javascript dev server or building a distributable wheel, install Javascript dependencies via:

cd mlflow/server/js
npm install
cd - # return to root repository directory

If modifying dependencies in mlflow/server/js/package.json, run npm update within mlflow/server/js to install the updated dependencies.

Java

Certain MLflow modules are implemented in Java, under the mlflow/java/ directory. These are the Java Tracking API client (mlflow/java/client) and the Model Scoring Server for Java-based models like MLeap (mlflow/java/scoring).

Other Java functionality (like artifact storage) depends on the Python package, so first install the Python package in a conda environment as described above. Install the Java 8 JDK (or above), and download and install Maven. You can then build and run tests via:

cd mlflow/java
mvn compile test

If opening a PR that makes API changes, please regenerate API documentation as described in Writing Docs and commit the updated docs to your PR branch.

R

The mlflow/R/mlflow directory contains R wrappers for the Projects, Tracking and Models components. These wrappers depend on the Python package, so first install the Python package in a conda environment:

# Note that we don't pass the -e flag to pip, as the R tests attempt to run the MLflow UI
# via the CLI, which will not work if we run against the development tracking server
pip install .

Install R, then run the following to install dependencies for building MLflow locally:

cd mlflow/R/mlflow
NOT_CRAN=true Rscript -e 'install.packages("devtools", repos = "https://cloud.r-project.org")'
NOT_CRAN=true Rscript -e 'devtools::install_deps(dependencies = TRUE)'

Build the R client via:

R CMD build .

Run tests:

R CMD check --no-build-vignettes --no-manual --no-tests mlflow*tar.gz
cd tests
NOT_CRAN=true LINTR_COMMENT_BOT=false Rscript ../.travis.R
cd -

Run linter:

Rscript -e 'lintr::lint_package()'

If opening a PR that makes API changes, please regenerate API documentation as described in Writing Docs and commit the updated docs to your PR branch.

When developing, you can make Python changes available in R by running (from mlflow/R/mlflow):

Rscript -e 'reticulate::conda_install("r-mlflow", "../../../.", pip = TRUE)'

Please also follow the recommendations from the Advanced R - Style Guide regarding naming and styling.

Python

Verify that the unit tests & linter pass before submitting a pull request by running:

./lint.sh
./travis/run-small-python-tests.sh
# Optionally, run large tests as well. Travis will run large tests on your pull request once
# small tests pass. Note: models and model deployment tests are considered "large" tests. If
# making changes to these components, we recommend running the relevant tests (e.g. tests under
# tests/keras for changes to Keras model support) locally before submitting a pull request.
./travis/run-large-python-tests.sh

Python tests are split into "small" & "large" categories, with new tests falling into the "small" category by default. Tests that take 10 or more seconds to run should be marked as large tests via the @pytest.mark.large annotation. Dependencies for small and large tests can be added to travis/small-requirements.txt and travis/large-requirements.txt, respectively.

We use pytest to run Python tests. You can run tests for one or more test directories or files via pytest [--large] [file_or_dir] ... [file_or_dir], where specifying --large tells pytest to run tests annotated with @pytest.mark.large. For example, to run all pyfunc tests (including large tests), you can run:

pytest tests/pyfunc --large

Note: Certain model tests are not well-isolated (can result in OOMs when run in the same Python process), so simply invoking pytest or pytest tests may not work. If you'd like to run multiple model tests, we recommend doing so via separate pytest invocations, e.g. pytest --verbose tests/sklearn --large && pytest --verbose tests/tensorflow --large

Note also that some tests do not run as part of PR builds on Travis. In particular, PR builds exclude:

  • Tests marked with @pytest.mark.requires_ssh. These tests require that passwordless SSH access to localhost be enabled, and can be run via pytest --requires-ssh.
  • Tests marked with @pytest.mark.release. These tests can be run via pytest --release.

In addition, the tests in tests/examples are run as part of a nightly build on Travis and will not run on Travis jobs triggered by push requests. If your PR changes anything tested by the tests or the tests themselves, Travis will detect this and run the nightly tests automatically with the regular build.

If you need to retrigger Travis tests on a PR, you can push an empty commit to your branch. To create an empty commit, you can use the --allow-empty` option, e.g. ``git commit --allow-empty -m "Trigger rebuild". Note that this will retrigger an entire rebuild - it is currently not possible to retrigger individual tests.

If opening a PR that changes or adds new APIs, please update or add Python documentation as described in Writing Docs and commit the docs to your PR branch.

Writing Python Tests

If your PR includes code that isn't currently covered by our tests (e.g. adding a new flavor, adding autolog support to a flavor, etc.), you should write tests that cover your new code. MLflow currently uses pytest==3.2.1 for testing. Your tests should be added to the relevant file under tests, or if there is no appropriate file, in a new file prefixed with test_ so that pytest includes that file for testing.

If your tests require usage of a tracking URI, the pytest fixture tracking_uri_mock is automatically set up for every tests. It sets up a mock tracking URI that will set itself up before your test runs and tear itself down after. If you want to deactivate the mock for your test, mark the test with @pytest.mark.notrackingurimock operator.

Adding New Model Flavor Support

If you are adding new framework flavor support, you'll need to modify pytest and Travis configurations so tests for your code can run properly. Generally, the files you'll have to edit are:

  1. .travis.yml: exclude your tests in the Windows bash script
  2. travis/run-small-python-tests.sh: add your tests to the list of ignored framework tests
  3. travis/run-large-python-tests.sh:
  1. Add your tests to the ignore list, where the other frameworks are ignored
  2. Add a pytest command for your tests along with the other framework tests (as a separate command to avoid OOM issues)
  1. travis/large-requirements.txt: add your framework and version to the list of requirements

You can see an example flavor PR here.

Building Protobuf Files

To build protobuf files, simply run generate-protos.sh. The required protoc version is 3.6.0. You can find the URL of a system-appropriate installation of protoc at https://github.com/protocolbuffers/protobuf/releases/tag/v3.6.0, e.g. https://github.com/protocolbuffers/protobuf/releases/download/v3.6.0/protoc-3.6.0-osx-x86_64.zip if you're on 64-bit Mac OSX.

Then, run the following to install protoc:

# Update PROTOC_ZIP if on a platform other than 64-bit Mac OSX
PROTOC_ZIP=protoc-3.6.0-osx-x86_64.zip
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v3.6.0/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
rm -f $PROTOC_ZIP

Verify that .proto files and autogenerated code are in sync by running ./test-generate-protos.sh.

Database Schema Changes

MLflow's Tracking component supports storing experiment and run data in a SQL backend. To make changes to the tracking database schema, run the following from your checkout of MLflow:

# starting at the root of the project
$ pwd
~/mlflow
$ cd mlflow
# MLflow relies on Alembic (https://alembic.sqlalchemy.org) for schema migrations.
$ alembic -c mlflow/store/db_migrations/alembic.ini revision -m "add new field to db"
  Generating ~/mlflow/mlflow/store/db_migrations/versions/b446d3984cfa_add_new_field_to_db.py

These commands generate a new migration script (e.g. at ~/mlflow/mlflow/alembic/versions/12341123_add_new_field_to_db.py) that you should then edit to add migration logic.

Launching the Development UI

We recommend Running the Javascript Dev Server - otherwise, the tracking frontend will request files in the mlflow/server/js/build directory, which is not checked into Git. Alternatively, you can generate the necessary files in mlflow/server/js/build as described in Building a Distributable Artifact.

Running the Javascript Dev Server

Install Node Modules, then run the following:

In one shell:

mlflow ui

In another shell:

cd mlflow/server/js
npm start

The MLflow Tracking UI will show runs logged in ./mlruns at http://localhost:3000.

Building a Distributable Artifact

Install Node Modules, then run the following:

Generate JS files in mlflow/server/js/build:

cd mlflow/server/js
npm run build

Build a pip-installable wheel in dist/:

cd -
python setup.py bdist_wheel

Writing Docs

First, install dependencies for building docs as described in Prerequisites.

To generate a live preview of Python & other rst documentation, run the following snippet. Note that R & Java API docs must be regenerated separately after each change and are not live-updated; see subsequent sections for instructions on generating R and Java docs.

cd docs
make livehtml

Generate R API rst doc files via:

cd docs
make rdocs

Generate Java API rst doc files via:

cd docs
make javadocs

Generate API docs for all languages via:

cd docs
make html

If changing existing Python APIs or adding new APIs under existing modules, ensure that references to the modified APIs are updated in existing docs under docs/source. Note that the Python doc generation process will automatically produce updated API docs, but you should still audit for usages of the modified APIs in guides and examples.

If adding a new public Python module, create a corresponding doc file for the module under docs/source/python_api - see here for an example.