[release] update torch_tune_serve_test to use anyscale connect #16754
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amogkam
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Jun 29, 2021
release/golden_notebook_tests/workloads/torch_tune_serve_test.py
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amogkam
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Jun 29, 2021
| @@ -186,7 +189,8 @@ def test_predictions(test_mode=False): | |||
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| print("Retrieving best model.") | |||
| best_checkpoint = analysis.best_checkpoint | |||
| model_id = get_best_model(best_checkpoint) | |||
| get_best_model_remote = ray.remote(get_best_model) | |||
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Is it possible to test out the ray.client().download_results() workflow here instead of wrapping in a task?
amogkam
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Jul 3, 2021
krfricke
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Jul 6, 2021
| model_state = torch.load(best_model_checkpoint_path) | ||
| def get_remote_model(remote_model_checkpoint_path): | ||
| address = os.environ.get("RAY_ADDRESS") | ||
| if address is not None and address.startswith("anyscale:https://"): |
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Would this also work with the Ray OSS client?
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Yep, for OSS this would go to the else block and use ray.remote to fetch the model. I updated the code with some better path isolation and successfully ran this script on a Ray cluster!
jiaodong
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Jul 11, 2021
* [release] update torch_tune_serve_test to use anyscale connect * use download_results to download model checkpoint * clean up code to support both OSS and Anyscale
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Why are these changes needed?
As part of the Release Test process, these tests are being migrated to use the Anyscale connect API.
This Golden Notebook test was originally added in #16619.
Note
get_best_modelis wrapped inray.remoteso that the checkpoint is loaded from the head node.Related issue number
n/a
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e2e job
scripts/format.shto lint the changes in this PR.