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Cross version testing

What is cross version testing?

Cross version testing is a testing strategy to ensure ML integrations in MLflow such as mlflow.sklearn work properly with their associated packages across various versions.

Key files

File (relative path from the root) Role
mlflow/ml-package-versions.yml Define which versions to test for each ML package.
dev/set_matrix.py Generate a test matrix from ml-package-versions.yml.
dev/update_ml_package_versions.py Update ml-package-versions.yml when releasing a new version.
.github/workflows/cross-version-tests.yml Define a Github Actions workflow for cross version testing.

Configuration keys in ml-package-versions.yml

# Note this is just an example and not the actual sklearn configuration.

# The top-level key specifies the integration name.
sklearn:
  package_info:
    # [Required] `pip_release` specifies the package this integration depends on.
    pip_release: "scikit-learn"

    # [Optional] `install_dev` specifies a set of commands to install the dev version of the package.
    # For example, the command below builds a wheel from the latest main branch of
    # the scikit-learn repository and installs it.
    #
    # The aim of testing the dev version is to spot issues as early as possible before they get
    # piled up, and fix them incrementally rather than fixing them at once when the package
    # releases a new version.
    install_dev: |
      pip install git+https://github.com/scikit-learn/scikit-learn.git

  # [At least one of `models` and `autologging` must be specified]
  # `models` specifies the configuration for model serialization and serving tests.
  # `autologging` specifies the configuration for autologging tests.
  models or autologging:
    # [Optional] `requirements` specifies additional pip requirements required for running tests.
    # For example, '">= 0.24.0": ["xgboost"]' is interpreted as 'if the version of scikit-learn
    # to install is newer than or equal to 0.24.0, install xgboost'.
    requirements:
      ">= 0.24.0": ["xgboost"]

    # [Required] `minimum` specifies the minimum supported version for the latest release of MLflow.
    minimum: "0.20.3"

    # [Required] `maximum` specifies the maximum supported version for the latest release of MLflow.
    maximum: "1.0"

    # [Optional] `unsupported` specifies a list of versions that should NOT be supported due to
    # unacceptable issues or bugs.
    unsupported: ["0.21.3"]

    # [Required] `run` specifies a set of commands to run tests.
    run: |
      pytest tests/sklearn/test_sklearn_model_export.py --large

How do we determine which versions to test?

We determine which versions to test based on the following rules:

  1. Only test final (e.g. 1.0.0) and post (1.0.0.post0) releases.
  2. Only test the latest micro version in each minor version. For example, if 1.0.0, 1.0.1, and 1.0.2 are available, we only test 1.0.2.
  3. The maximum version defines the maximum major version to test. For example, if the value of maximum is 1.0.0, we test 1.1.0 (if available) but not 2.0.0.
  4. Always test the minimum version.

The table below describes which scikit-learn versions to test for the example configuration in the previous section:

Version Tested Comment
0.20.3 ✅ The value of minimum
0.20.4 ✅ The latest micro version of 0.20
0.21rc2
0.21.0
0.21.1
0.21.2 ✅ The latest micro version of 0.21 without0.21.3
0.21.3 Excluded by unsupported
0.22rc2.post1
0.22rc3
0.22
0.22.1
0.22.2
0.22.2.post1 ✅ The latest micro version of 0.22
0.23.0rc1
0.23.0
0.23.1
0.23.2 ✅ The latest micro version of 0.23
0.24.dev0
0.24.0rc1
0.24.0
0.24.1
0.24.2 ✅ The latest micro version of 0.24
1.0rc1
1.0rc2
1.0 The value of maximum
1.0.1 ✅ The latest micro version of 1.0
1.1.dev ✅ The version installed by install_dev

Why do we run tests against development versions?

In cross-version testing, we run daily tests against both publicly available and pre-release development versions for all dependent libraries that are used by MLflow. This section explains why.

Without dev version test

First, let's take a look at what would happen without dev version test.

  |
  ├─ XGBoost merges a change on the master branch that breaks MLflow's XGBoost integration.
  |
  ├─ MLflow 1.20.0 release date
  |
  ├─ XGBoost 1.5.0 release date
  ├─ ❌ We notice the change here and might need to make a patch release if it's critical.
  |
  v
time
  • We didn't notice the change until after XGBoost 1.5.0 was released.
  • MLflow 1.20.0 doesn't work with XGBoost 1.5.0.

With dev version test

Then, let's take a look at what would happen with dev version test.

  |
  ├─ XGBoost merges a change on the master branch that breaks MLflow's XGBoost integration.
  ├─ ✅ Tests for the XGBoost integration fail -> We can notice the change and apply a fix for it.
  |
  ├─ MLflow 1.20.0 release date
  |
  ├─ XGBoost 1.5.0 release date
  |
  v
time
  • We can notice the change before XGBoost 1.5.0 is released and apply a fix for it before releasing MLflow 1.20.0.
  • MLflow 1.20.0 works with XGBoost 1.5.0.

When do we run cross version tests?

  1. Daily at 7:00 UTC using a cron scheduler. README on the repository root has a badge (badge-img) that indicates the status of the most recent cron run.
  2. When a PR that affects the ML integrations is created. Note we only run tests relevant to the affected ML integrations. For example, a PR that affects files in mlflow/sklearn triggers cross version tests for sklearn.

How to run cross version test for dev versions on a pull request

By default, cross version tests for dev versions are disabled on a pull request. To enable them, the following steps are required.

  1. Click Labels in the right sidebar.
  2. Click the enable-dev-tests label and make sure it's applied on the pull request.
  3. Push a new commit or re-run the cross-version-tests workflow.

See also:

How to run cross version tests manually

The cross-version-tests.yml workflow can be run manually without creating a pull request.

  1. Open https://github.com/mlflow/mlflow/actions/workflows/cross-version-tests.yml.
  2. Click Run workflow.
  3. Fill in the input parameters.
  4. Click Run workflow at the bottom of the parameter input form.

See also: