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pytorch_learn/tools/stats
Huy Do 7c6fe21a38 Fix monitoring script for macos (#88159) 1 year ago
..
README.md [ci/docs] add some documentation about the stats uploading process 1 year ago
__init__.py First step to rearrange files in tools folder (#60473) 2 years ago
export_test_times.py download test times during build to avoid race conditions (#81915) 1 year ago
import_test_stats.py use condensed disabled tests file (#84017) 1 year ago
monitor.py Fix monitoring script for macos (#88159) 1 year ago
print_test_stats.py Fix use-dict-literal lint (#83718) 1 year ago
s3_stat_parser.py Apply ufmt linter to all py files under tools (#81285) 1 year ago
scribe.py Apply ufmt linter to all py files under tools (#81285) 1 year ago
test_history.py Apply ufmt linter to all py files under tools (#81285) 1 year ago
upload_artifacts.py [BE] Upload GHA artifacts to S3 (#87827) 1 year ago
upload_sccache_stats.py Apply ufmt linter to all py files under tools (#81285) 1 year ago
upload_stats_lib.py [BE] Upload GHA artifacts to S3 (#87827) 1 year ago
upload_test_stats.py mac circleci workflows (#82780) 1 year ago

README.md

PyTorch CI Stats

We track various stats about each CI job.

  1. Jobs upload their artifacts to an intermediate data store (either GitHub
    Actions artifacts or S3, depending on what permissions the job has). Example:
    a9f6a35a33/.github/workflows/_linux-build.yml (L144-L151)
  2. When a workflow completes, a workflow_run event triggers
    upload-test-stats.yml    .
  3. upload-test-stats downloads the raw stats from the intermediate data store
    and uploads them as JSON to Rockset, our metrics backend.
graph LR
    J1[Job with AWS creds<br>e.g. linux, win] --raw stats--> S3[(AWS S3)]
    J2[Job w/o AWS creds<br>e.g. mac] --raw stats--> GHA[(GH artifacts)]

    S3 --> uts[upload-test-stats.yml]
    GHA --> uts

    uts --json--> R[(Rockset)]

Why this weird indirection? Because writing to Rockset requires special
permissions which, for security reasons, we do not want to give to pull request
CI. Instead, we implemented GitHub's recommended
pattern
for cases like this.

For more details about what stats we export, check out
upload-test-stats.yml

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Tensors and Dynamic neural networks in Python with strong GPU acceleration

C++ Python Cuda Text C other

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