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https://github.com/vale981/ray
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d8a61f801f
* Create a core set of algorithms tests to run nightly. * Run release tests under tf, tf2, and torch frameworks. * Fix * Add eager_tracing option for tf2 framework. * make sure core tests can run in parallel. * cql * Report progress while running nightly/weekly tests. * Innclude SAC in nightly lineup. * Revert changes to learning_tests * rebrand to performance test. * update build_pipeline.py with new performance_tests name. * Record stats. * bug fix, need to populate experiments dict. * Alphabetize yaml files. * Allow specifying frameworks. And do not run tf2 by default. * remove some debugging code. * fix * Undo testing changes. * Do not run CQL regression for now. * LINT. Co-authored-by: sven1977 <svenmika1977@gmail.com>
29 lines
913 B
Python
29 lines
913 B
Python
"""Core Learning regression tests for RLlib (torch and tf).
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Runs Atari/PyBullet benchmarks for the most popular algorithms.
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"""
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import json
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import os
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from pathlib import Path
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from ray.rllib.utils.test_utils import run_learning_tests_from_yaml
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if __name__ == "__main__":
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# Get path of this very script to look for yaml files.
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abs_yaml_path = Path(__file__).parent
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print("abs_yaml_path={}".format(abs_yaml_path))
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yaml_files = abs_yaml_path.rglob("*.yaml")
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yaml_files = sorted(
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map(lambda path: str(path.absolute()), yaml_files), reverse=True)
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# Run all tests in the found yaml files.
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results = run_learning_tests_from_yaml(yaml_files=yaml_files)
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test_output_json = os.environ.get("TEST_OUTPUT_JSON",
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"/tmp/rllib_learning_test_core.json")
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with open(test_output_json, "wt") as f:
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json.dump(results, f)
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print("Ok.")
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