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Release Process

This document discusses the release process for MLServer.

⚠️ NOTE: This is work in progress. This is a very initial version of the release process. The process may change. Please, always check this document before conducting a release and verify if everything goes as expected.

Before releasing a new version of MLServer, please read this page carefully and make sure you understand the different parts of the process.

Process Summary

NB: The release workflows take care of setting the specified version, so you do not need to run the script for updating the version beforehand.

  1. If this is a new major or minor official release, create a release branch from master. Alternatively, if this is a patch official release, checkout the existing release branch and cherry-pick any relevant commits into it.

    Note that, for dev releases, the above is not needed and you can trigger the MLServer Release action straight from master.

  2. Trigger a MLServer Release from Github Actions.

    1. Provide the version tag that you want to release. MLServer Release

  3. Monitor the triggered workflow, until it's finished.

    1. Once it's done, all the release artifacts will be pushed to Docker Hub and PyPI.
    2. Additionally, a release draft will get created in the repository Releases section.

  4. For official releases, update the release draft (created in step 3.2) in the Releases section of the repository.

    1. Once you are happy with the draft, click Publish.

Release Branches

For official releases, MLServer uses long-lived release branches. These branches will always follow the release/<major>.<minor>.x pattern (e.g. release/1.2.x) and will be used for every <major>.<minor>.x official release (e.g. the release/1.2.x will be used for 1.2.0, 1.2.1, etc.). Note that these branches will always be pushed straight to the main github.com/SeldonIO/MLServer and not to a fork and will never get merged with master.

Therefore, when starting a new major or minor official release please create a release/<major>.<minor>.x branch. Alternatively, when preparing a patch official release, please cherry-pick all relevant merged PRs from master into the existing release/<major>.<minor>.x branch.

Versioning Scheme

The MLServer project publishes three types of release versions:

  • Dev pre-releases, used to test new features before an official release. They will follow the schema <next-minor-version>.dev<incremental-index> (e.g. 1.2.0.dev3).
  • Release candidates, used to test an official release before the actual release occurs. This type of releases can be useful to test minor releases across different projects. They follow the schema <next-minor-version>.rc<incremental-index> (e.g. 1.2.0.rc1).
  • Official releases, used only for actual public releases. The version tag will only contain the next minor version (e.g. 1.2.0), without any suffixes.

Based on the above, a usual release cycle between two official releases would generally look like the following (where stability increases as you go down on the chart):

Versioning Scheme

Release Artefacts

Each release of MLServer will build and publish a set of artifacts, both at the runtime level and the wider MLServer level:

  • Docker image containing every inference runtime maintained within the MLServer repo, tagged as seldonio/mlserver:<version> (e.g. seldonio/mlserver:1.2.0). Note that this image can grow quite large.
  • Slim Docker image containing only the core MLServer package (i.e. without any runtimes), tagged as seldonio/mlserver:<version>-slim (e.g. seldonio/mlserver:1.2.0-slim). This image is used, as the default for custom runtimes.
  • Python package for the core MLServer modules (i.e. without any runtime), which will get published in PyPI, named simply mlserver.
  • For each inference runtime (e.g. mlserver-sklearn, mlserver-xgboost, etc.):
    • Docker image containing only that specific runtime, tagged as seldonio/mlserver:<version>-<runtime> (e.g. seldonio/mlserver:1.2.0-sklearn).
    • Python package for the specific runtime, which will get published in PyPI (e.g. mlserver-sklearn==1.2.0).