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CONTRIBUTING.rst

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Contributing to MLflow

We welcome community contributions to MLflow. This page describes how to develop/test your changes to MLflow locally.

The majority of the MLflow codebase is 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

We recommend installing MLflow in its own conda environment for development, as follows:

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

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

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

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()'

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:

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.

Building Protobuf Files

To build protobuf files, simply run generate-protos.sh. The required protoc version is 3.6.0. Verify that .proto files and autogenerated code are in sync by running ./test-generate-protos.sh.

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

Install the necessary Python dependencies via pip install -r dev-requirements.txt. Then run

cd docs
make livehtml