Bring back TIMM model inductor CI test #87730
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Summary: #87588 has solved the inductor compilation speed regression, so we can try to run TIMM models with fewer shards and also enable pretained model downloading which should resolve the flakyness we have seen previously. [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/87730
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| drop_block_rate=None, | ||
| # Skip downloading pretrained models for speedy CI | ||
| pretrained=not self.args.ci, | ||
| pretrained=True, |
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@malfet , this change should eliminate the flakiness we have seen. I will keep an eye on HUD after landing this.
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@pytorchbot merge |
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Hey @desertfire. |
Summary: pytorch#87588 has solved the inductor compilation speed regression, so we can try to run TIMM models with fewer shards and also enable pretained model downloading which should resolve the flakyness we have seen previously. cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 Pull Request resolved: pytorch#87730 Approved by: https://github.com/anijain2305
Summary: pytorch#87588 has solved the inductor compilation speed regression, so we can try to run TIMM models with fewer shards and also enable pretained model downloading which should resolve the flakyness we have seen previously. cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 Pull Request resolved: pytorch#87730 Approved by: https://github.com/anijain2305
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Stack from ghstack (oldest at bottom):
Summary: #87588 has solved the
inductor compilation speed regression, so we can try to run TIMM models
with fewer shards and also enable pretained model downloading which
should resolve the flakyness we have seen previously.
cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305