Refactor wheel upload job to a separate job running on GH ephemeral runner #4877
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malfet
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Jan 13, 2024
malfet
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Jan 13, 2024
huydhn
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Jan 15, 2024
I made a mistake in #4877 when not explicitly select `test-infra` as the repo to checkout for the GHA. When running on domains, the default repos are domains, i.e. `pytorch/vision`, and they don't have the GHA we need, namely `setup-binary-upload`. An example failure when testing on vision nightly https://github.com/pytorch/vision/actions/runs/7533535440/job/20506343331
This was referenced Jan 16, 2024
malfet
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Jan 16, 2024
…unner (#4886) Similar to #4877, this moves conda upload into a separate job on GH ephemeral runner: * I need a new `_binary_conda_upload` reusable workflow because conda upload uses anaconda client to upload to conda, not awscli to upload to S3. * The build job doesn't have access to `pytorchbot-env` anymore, thus it has no access to `CONDA_PYTORCHBOT_TOKEN` and `CONDA_PYTORCHBOT_TOKEN_TEST` secrets. Only the upload job has this access.
huydhn
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…unner (#4877) To run the upload part in a separate upload job on GH ephemeral runners, we need: 1. Specific artifact name for each binary, so the upload job could find the correct one. 2. Create a new GHA `setup-binary-upload` to: 1. Download the artifacts from GitHub 2. Running `pkg-helpers` is needed to figure out the correct S3 bucket and path to upload to. 3. Create a new GHA reusable workflow `_binary_upload` to upload the artifacts to S3. 1. Run on GH ephemeral runner `ubuntu-22.04`. 2. Only this job has access to the credential, the build job doesn't have that privilege anymore. A small caveat here is that the upload job will depend on the build job with all its configuration matrix, so it can only be run after all build configurations finish successfully, not when individual builds finish. The PR is quite big, so I will do a similar follow up for conda build after this using the same `_binary_upload` reusable workflow.
huydhn
added a commit
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Feb 12, 2024
I made a mistake in #4877 when not explicitly select `test-infra` as the repo to checkout for the GHA. When running on domains, the default repos are domains, i.e. `pytorch/vision`, and they don't have the GHA we need, namely `setup-binary-upload`. An example failure when testing on vision nightly https://github.com/pytorch/vision/actions/runs/7533535440/job/20506343331
huydhn
added a commit
that referenced
this pull request
Feb 12, 2024
…unner (#4886) Similar to #4877, this moves conda upload into a separate job on GH ephemeral runner: * I need a new `_binary_conda_upload` reusable workflow because conda upload uses anaconda client to upload to conda, not awscli to upload to S3. * The build job doesn't have access to `pytorchbot-env` anymore, thus it has no access to `CONDA_PYTORCHBOT_TOKEN` and `CONDA_PYTORCHBOT_TOKEN_TEST` secrets. Only the upload job has this access.
This was referenced Feb 12, 2024
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To run the upload part in a separate upload job on GH ephemeral runners, we need:
setup-binary-uploadto:pkg-helpersis needed to figure out the correct S3 bucket and path to upload to._binary_uploadto upload the artifacts to S3.ubuntu-22.04.A small caveat here is that the upload job will depend on the build job with all its configuration matrix, so it can only be run after all build configurations finish successfully, not when individual builds finish.
The PR is quite big, so I will do a similar follow up for conda build after this using the same
_binary_uploadreusable workflow.