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Add a README.md for benchmarking infra #5403

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pytorch-bot bot commented Sep 17, 2024

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/5403

Note: Links to docs will display an error until the docs builds have been completed.

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@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 17, 2024
@guangy10 guangy10 force-pushed the doc_benchinfra branch 3 times, most recently from b35c3e8 to fde8fd7 Compare September 17, 2024 00:18
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@guangy10 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

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| Model | Backend | Model Load Time (ms) | Avg Inference Latency (ms) | Device |
|-----------------------|--------------|----------------------|----------------------------|------------------------|
| DeepLabV3 (dl3) | XNN Q8 | [90.616](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985758) | [666.219](https://github.com/pytorch/executorch/actions/runs/10875550238/job/30218985758) | Samsung S22 |
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Just FYI, the GitHub link here expires after 90 days, so let's work on getting the dashboard ready before that date :)


## Scheduling On-Demand Benchmarking

Users can schedule a benchmarking workflow on a pull request through GitHub Actions using the workflow dispatch UI. Follow the steps below to trigger benchmarking:
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Not sure if we want to be clear here, only people with write permission to the repo can run the workflow.

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Will add it to the readme. Having write permission will limit the usage of it, especially for backend vendors. Except for explicitly grant write permissions to individuals, is there other more convenient way for vendors to start dogfooding?

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There are similar questions/requests to GH from what I see https://github.com/orgs/community/discussions/49688. We could consider building something like @pytorchbot here, for example, posting a comment like @executorchbot benchmark to run perf benchmark. There are pros and cons, but that's a familiar approach that PyTorch is using

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@guangy10 merged this pull request in c5d1661.

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@pytorchbot cherry-pick --onto release/0.4 -c docs

@guangy10 guangy10 deleted the doc_benchinfra branch September 17, 2024 03:56
pytorchbot pushed a commit that referenced this pull request Sep 17, 2024
Summary: Pull Request resolved: #5403

Reviewed By: mergennachin, huydhn

Differential Revision: D62790095

Pulled By: guangy10

fbshipit-source-id: fb0ec5e7bfcc49db6de86ca3a52729026827d567
(cherry picked from commit c5d1661)
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Cherry picking #5403

The cherry pick PR is at #5413 The following tracker issues are updated:

Details for Dev Infra team Raised by workflow job

dvorjackz pushed a commit that referenced this pull request Sep 17, 2024
Add a README.md for benchmarking infra (#5403)

Summary: Pull Request resolved: #5403

Reviewed By: mergennachin, huydhn

Differential Revision: D62790095

Pulled By: guangy10

fbshipit-source-id: fb0ec5e7bfcc49db6de86ca3a52729026827d567
(cherry picked from commit c5d1661)

Co-authored-by: Guang Yang <guangyang@fb.com>
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