How to update the conda-forge recipes for AutoGluon

• autogluon.common-feedstock
• autogluon.features-feedstock
• autogluon.core-feedstock
• autogluon.tabular-feedstock
• autogluon.multimodal-feedstock
• autogluon.timeseries-feedstock
• autogluon-feedstock

• autogluon.common-feedstock
• autogluon.features-feedstock
• autogluon.core-feedstock
• autogluon.tabular-feedstock
• autogluon.multimodal-feedstock
• autogluon.timeseries-feedstock
• autogluon-feedstock

• autogluon.common-feedstock
• autogluon.features-feedstock
• autogluon.core-feedstock
• autogluon.tabular-feedstock
• autogluon.multimodal-feedstock
• autogluon.timeseries-feedstock
• autogluon-feedstock

1. Go to the AutoGluon release page on GitHub. Under the Assets section, right click on Source code (tar.gz) to copy the link address. It should be something like this:

   https://github.com/autogluon/autogluon/archive/refs/tags/v0.7.0.tar.gz
   

2. Click on the link above to download the source code. The file should be named something like autogluon-0.7.0.tar.gz.

3. Use openssl to generate the sha256 hash of the downloaded file, e.g.,

   openssl sha256 autogluon-0.7.0.tar.gz

   The output should be something like this:

   455831de3c9de8fbe11b100054b8f150661d0651212fcfa4ec2e42417fdac355
   

4. Fork the autogluon.common-feedstock repo to your GitHub account.

5. Clone the forked repo to your local machine.

6. Create a new branch, e.g., v0.7.0

7. Open the recipe/meta.yaml file in your favorite text editor. Update the version, sha256 and number fields. The version field should be the version number of the new release. For example, if the new release is v0.7.0, then the version field should be 0.7.0. The sha256 field should be the hash generated in step 4. The number field should be reset to 0 for a new release. If the version number stays the same, then the number field should be incremented by 1. This is usually the case when you are updating the dependencies of the package but not updating the package version.

8. Update the package dependencies and version bounds for each recipe based on the release. For autogluon.common, the dependency list can be found at common/setup.py, but the version bounds can be found at core/_setup_utils.py

9. Commit the changes and push to your forked repo. Then create a pull request to the autogluon.common-feedstock repo.

10. Comment on the pull request with the following text to trigger the CI build:

    @conda-forge-admin, please rerender
    

11. Once the CI build is successful, merge the pull request.

12. Repeat steps above for the other six packages.

Optionally, you can build the conda-forge packages locally to test if the recipes are correct. This is especially useful when you are updating the dependencies of the package. The steps are as follows:

1. Install docker on your machine. See here for instructions.

2. Install Anaconda or Miniconda on your machine. See here for instructions.

3. Install mamba in the base environment of your conda installation. This is a faster version of conda that is recommended for conda-forge builds.

   conda install -n base mamba -c conda-forge

4. Create a new conda environment named ag with Python 3.9 or higher that's supported by AutoGluon.

   mamba create -n ag python=3.9

5. Navigate to the root directory of the cloned repo of the package you want to build. For example, if you want to build autogluon.multimodal, then you should be in the root directory of the autogluon.multimodal-feedstock repo.

    chmod 777 -R autogluon.multimodal-feedstock
    cd autogluon.multimodal-feedstock
    python build-locally.py

6. Choose an option that you want to build that matches your computer configuration. For example, if you want to build the linux_64_cuda package with Python 3.9, then choose option 3.

7. If the build is successful, you should find the built packages in the build_artifacts directory under the root directory of the cloned repo.

8. Install the built packages in the ag environment you created in step 4.

    mamba install -n ag -c "file://${PWD}/build_artifacts" -c conda-forge  autogluon.multimodal

   Make sure to check the pytorch version in the list of packages to install. If you see cuda in the version number, that means the package is built with CUDA support. Otherwise, it's built without CUDA support.

1. Please ask the existing maintainers if you want to be added as a maintainer. Only the existing maintainers can add new maintainers.
2. The existing maintainer needs to go to the feedstock repo, e.g., autogluon.common-feedstock.
3. Open a new issue and choose the Bot commands template. Click on Get started to open the issue.
4. Enter the following text in the title of the issue. Be sure to replace @username with the GitHub username of the maintainer you want to add.

@conda-forge-admin, please add user @username

5. Click on Submit new issue to submit the issue.
6. Once the issue is submitted and the CI build is successful, merge the pull request.