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b05531177c
#24421 increased the default maximum GRPC limit to 250MB, which broke a Tune test that catches too large training functions. This PR fixes this test by increasing the size of the experiment. However, please note that this leads to an inconsistency: For training functions of size 100 < fn < 250, an error will be raised only at runtime when trying to start the actor: ``` ValueError: The actor ImplicitFunc is too large (125 MiB > FUNCTION_SIZE_ERROR_THRESHOLD=95 MiB). Check that its definition is not implicitly capturing a large array or other object in scope. Tip: use ray.put() to put large objects in the Ray object store. ``` But it will successfully pass the registration stage `self._run_identifier = Experiment.register_if_needed(run)`. cc @ericl should we set the default limit back to 100 MB (or maybe set the FUNCTION_SIZE_ERROR_THRESHOLD to 250 or whatever the GRPC limit is?) |
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| .. | ||
| ray | ||
| requirements | ||
| asv.conf.json | ||
| build-wheel-macos-arm64.sh | ||
| build-wheel-macos.sh | ||
| build-wheel-manylinux2014.sh | ||
| build-wheel-windows.sh | ||
| MANIFEST.in | ||
| README-building-wheels.md | ||
| requirements.txt | ||
| requirements_linters.txt | ||
| requirements_ml_docker.txt | ||
| setup.py | ||