mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
#!/usr/bin/env python
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# Runs one or more regression tests. Retries tests up to 3 times.
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#
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# Example usage:
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# $ python run_regression_tests.py regression-tests/cartpole-es-[tf|torch].yaml
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#
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# When using in BAZEL (with py_test), e.g. see in ray/rllib/BUILD:
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# py_test(
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# name = "run_regression_tests",
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# main = "tests/run_regression_tests.py",
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# tags = ["learning_tests"],
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# size = "medium", # 5min timeout
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# srcs = ["tests/run_regression_tests.py"],
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# data = glob(["tuned_examples/regression_tests/*.yaml"]),
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# # Pass `BAZEL` option and the path to look for yaml regression files.
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# args = ["BAZEL", "tuned_examples/regression_tests"]
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# )
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import argparse
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import os
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from pathlib import Path
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import sys
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import yaml
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import ray
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from ray.tune import run_experiments
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from ray.rllib import _register_all
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--torch",
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action="store_true",
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help="Runs all tests with PyTorch enabled.")
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parser.add_argument(
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"--yaml-dir",
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type=str,
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help="The directory in which to find all yamls to test.")
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if __name__ == "__main__":
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args = parser.parse_args()
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# Bazel regression test mode: Get path to look for yaml files from argv[2].
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# Get the path or single file to use.
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rllib_dir = Path(__file__).parent.parent
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print("rllib dir={}".format(rllib_dir))
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if not os.path.isdir(os.path.join(rllib_dir, args.yaml_dir)):
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raise ValueError("yaml-dir ({}) not found!".format(args.yaml_dir))
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yaml_files = rllib_dir.rglob(args.yaml_dir + "/*.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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print("Will run the following regression tests:")
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for yaml_file in yaml_files:
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print("->", yaml_file)
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# Loop through all collected files.
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for yaml_file in yaml_files:
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experiments = yaml.load(open(yaml_file).read())
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assert len(experiments) == 1,\
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"Error, can only run a single experiment per yaml file!"
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print("== Test config ==")
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print(yaml.dump(experiments))
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# Add torch option to exp configs.
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for exp in experiments.values():
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if args.torch:
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exp["config"]["framework"] = "torch"
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# Try running each test 3 times and make sure it reaches the given
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# reward.
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passed = False
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for i in range(3):
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try:
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ray.init(num_cpus=5)
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trials = run_experiments(experiments, resume=False, verbose=1)
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finally:
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ray.shutdown()
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_register_all()
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for t in trials:
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if (t.last_result["episode_reward_mean"] >=
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t.stopping_criterion["episode_reward_mean"]):
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passed = True
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break
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if passed:
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print("Regression test PASSED")
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break
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else:
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print("Regression test FAILED on attempt {}", i + 1)
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if not passed:
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print("Overall regression FAILED: Exiting with Error.")
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sys.exit(1)
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