MONAI 1.5.1 Release Checklist #8569

This issue is to collect other issues and PRs that should be closed/merged for the upcoming 1.5.1 release in time for MICCAI 2025.

Important PRs:

• Path traversal issue security fix #8568
• Torch and Pickle Safe Load Fixes #8566
• Torchvision pretrain fix #8563
• Create SECURITY.md #8546
• Fix hardcoded input dim in DiffusionModelEncoder #8514
• Transformers version #8574
• Fix gdown fails #8576
• Diffusion Model Encoder has an output layer set in forward method and this leads to problems #8578
  • Removing instantiation in forward #8580
• Release 1.5.1 Updates #8575
• Update Frequency Transform  #8537 (reserve for next release)

Important issues:

• Vulnerabilities in MONAI #8565
• Feature: Remove Pickle usage to improve security of Monai #8527
• Gdown failed for downloading files #8549
• Update Torchvision Usage #8552
• Compatibility with PyTorch >= 2.8 for Future Hardware (e.g., RTX 50 series) #8507
• pkg_resources is deprecated as an API for python 3.13.5 #8536
• New high vuln found in transformers #8341
• DiffusionModelEncoder output layer is set outside of the initiation and left out of trainable parameters and set to the wrong device #8577

Other Issues/PRs:

• docs: add Google Colab setup and troubleshooting section tutorials#2025
• ONNX Runtime Needed for RetinaNet Tests #8533
• added ReduceTrait and FlattenSequence  #8531
  Stacking two or more MultiSampleTrait-like transforms breaks execute_compose() #8528
• Fix incorrect docstring defaults for DiffusionModelUNet #8571
• Device mismatch issue in anomalydetection_tutorial_classifier_guidance.ipynb tutorials#2030
• KeyError: 'Series UID' in TCIA_PROSTATEx_Prostate_MRI_Anatomy_Model.ipynb tutorials#2029
• Updating workflow to use Github CPU runner tutorials#2032

Checklist

Release a candidate version

• Tag and release PYPI version [X.Y.Z]rc[K] following the contributing guide, need trigger release pipeline on blossom.
• Make sure all CI pipelines pass.

Verify the new PyPI release

• Check the PyPI page
• Test pip install monai==[X.Y.Z]rc[K] and run a few MONAI modules locally
• Test all installation commands mentioned in the installation guide and the primary README.

Quality assurance for the relevant repos

The new PyPI release candidate should be checked against all the relevant repositories under Project-MONAI, for example, the tutorials repo:

• Run all Jupyter notebooks, replace pip install monai with pip install monai==[X.Y.Z]rc[K].
• File new tickets and create new pull requests to address any technical issues.
• Release a new candidate after addressing all the issues.
• Testing related repositories
  • MONAI unit and integration tests
  • MONAI tutorial tests
  • MONAILabel unit and integration tests
  • MONAI model zoo tests
  • QA regression tests

Prepare documentation

• Draft a release note on GitHub.
• Draft a release note with a pull request to modify the CHANGELOG.
• Create a pull request to highlight the features of the upcoming release, for example, in docs/source/highlights.md.
• Create a pull request to update the primary README.md.
• Update the monai.io website (https://github.com/Project-MONAI/project-monai.github.io).
• Check all the URLs in https://monai.io
• Check all the URLs in https://docs.monai.io.

Release a milestone version

• Tag and release PyPI version [X.Y.Z] following the contributing guide.
• Check the dockerhub auto-build by running docker pull projectmonai/monai:X.Y.Z and docker run ...
• Check jupyter lab/notebook in the docker container
• Check readme frontpage on dockerhub, pypi
• Update https://github.com/Project-MONAI/MONAI/blob/dev/CITATION.cff
• Publish the release note on GitHub.
• Update the weekly-preview tagging https://github.com/Project-MONAI/MONAI/blob/master/.github/workflows/weekly-preview.yml#L32 and https://github.com/Project-MONAI/MONAI/blob/master/.github/workflows/weekly-preview.yml#L69
• Release the corresponding conda-forge version https://github.com/conda-forge/monai-feedstock