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https://github.com/vale981/ray
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Add large scale regression test for RLlib (#6093)
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Eric Liang
GitHub
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commit
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4 changed files with 210 additions and 37 deletions
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@ -40,8 +40,10 @@ This document describes the process for creating new releases.
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ray/ci/stress_tests/run_stress_tests.sh <release-version> <release-commit>
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ray/ci/stress_tests/run_application_stress_tests.sh <release-version> <release-commit>
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rllib train -f rllib/tuned_examples/compact-regression-test.yaml
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and make sure they pass. If they pass, it will be obvious that they passed.
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and make sure they pass. For the RLlib regression tests, see the comment on the
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file for the pass criteria. For the rest, it will be obvious if they passed.
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This will use the autoscaler to start a bunch of machines and run some tests.
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**Caution!**: By default, the stress tests will require expensive GPU instances.
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@ -213,41 +213,50 @@ def run(run_or_experiment,
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queue_trials=queue_trials,
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reuse_actors=reuse_actors,
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ray_auto_init=ray_auto_init)
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experiment = run_or_experiment
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if not isinstance(run_or_experiment, Experiment):
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run_identifier = Experiment._register_if_needed(run_or_experiment)
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experiment = Experiment(
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name=name,
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run=run_identifier,
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stop=stop,
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config=config,
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resources_per_trial=resources_per_trial,
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num_samples=num_samples,
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local_dir=local_dir,
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upload_dir=upload_dir,
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sync_to_driver=sync_to_driver,
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trial_name_creator=trial_name_creator,
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loggers=loggers,
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checkpoint_freq=checkpoint_freq,
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checkpoint_at_end=checkpoint_at_end,
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keep_checkpoints_num=keep_checkpoints_num,
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checkpoint_score_attr=checkpoint_score_attr,
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export_formats=export_formats,
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max_failures=max_failures,
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restore=restore,
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sync_function=sync_function)
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if isinstance(run_or_experiment, list):
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experiments = run_or_experiment
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else:
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experiments = [run_or_experiment]
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if len(experiments) > 1:
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logger.info(
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"Running multiple concurrent experiments is experimental and may "
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"not work with certain features.")
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for i, exp in enumerate(experiments):
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if not isinstance(exp, Experiment):
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run_identifier = Experiment._register_if_needed(exp)
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experiments[i] = Experiment(
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name=name,
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run=run_identifier,
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stop=stop,
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config=config,
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resources_per_trial=resources_per_trial,
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num_samples=num_samples,
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local_dir=local_dir,
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upload_dir=upload_dir,
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sync_to_driver=sync_to_driver,
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trial_name_creator=trial_name_creator,
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loggers=loggers,
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checkpoint_freq=checkpoint_freq,
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checkpoint_at_end=checkpoint_at_end,
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keep_checkpoints_num=keep_checkpoints_num,
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checkpoint_score_attr=checkpoint_score_attr,
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export_formats=export_formats,
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max_failures=max_failures,
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restore=restore,
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sync_function=sync_function)
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else:
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logger.debug("Ignoring some parameters passed into tune.run.")
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if sync_to_cloud:
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assert experiment.remote_checkpoint_dir, (
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"Need `upload_dir` if `sync_to_cloud` given.")
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for exp in experiments:
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assert exp.remote_checkpoint_dir, (
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"Need `upload_dir` if `sync_to_cloud` given.")
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runner = TrialRunner(
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search_alg=search_alg or BasicVariantGenerator(),
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scheduler=scheduler or FIFOScheduler(),
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local_checkpoint_dir=experiment.checkpoint_dir,
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remote_checkpoint_dir=experiment.remote_checkpoint_dir,
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local_checkpoint_dir=experiments[0].checkpoint_dir,
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remote_checkpoint_dir=experiments[0].remote_checkpoint_dir,
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sync_to_cloud=sync_to_cloud,
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checkpoint_period=global_checkpoint_period,
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resume=resume,
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@ -256,7 +265,8 @@ def run(run_or_experiment,
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verbose=bool(verbose > 1),
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trial_executor=trial_executor)
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runner.add_experiment(experiment)
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for exp in experiments:
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runner.add_experiment(exp)
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if IS_NOTEBOOK:
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reporter = JupyterNotebookReporter(overwrite=verbose < 2)
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@ -269,7 +279,7 @@ def run(run_or_experiment,
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dict) and "gpu" in resources_per_trial:
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# "gpu" is manually set.
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pass
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elif _check_default_resources_override(experiment.run_identifier):
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elif _check_default_resources_override(experiments[0].run_identifier):
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# "default_resources" is manually overriden.
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pass
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else:
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@ -329,7 +339,8 @@ def run_experiments(experiments,
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queue_trials=False,
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reuse_actors=False,
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trial_executor=None,
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raise_on_failed_trial=True):
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raise_on_failed_trial=True,
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concurrent=False):
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"""Runs and blocks until all trials finish.
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Examples:
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@ -357,10 +368,9 @@ def run_experiments(experiments,
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# and it conducts the implicit registration.
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experiments = convert_to_experiment_list(experiments)
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trials = []
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for exp in experiments:
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trials += run(
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exp,
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if concurrent:
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return run(
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experiments,
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search_alg=search_alg,
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scheduler=scheduler,
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with_server=with_server,
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@ -372,4 +382,20 @@ def run_experiments(experiments,
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trial_executor=trial_executor,
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raise_on_failed_trial=raise_on_failed_trial,
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return_trials=True)
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return trials
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else:
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trials = []
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for exp in experiments:
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trials += run(
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exp,
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search_alg=search_alg,
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scheduler=scheduler,
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with_server=with_server,
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server_port=server_port,
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verbose=verbose,
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resume=resume,
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queue_trials=queue_trials,
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reuse_actors=reuse_actors,
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trial_executor=trial_executor,
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raise_on_failed_trial=raise_on_failed_trial,
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return_trials=True)
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return trials
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@ -177,7 +177,8 @@ def run(args, parser):
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experiments,
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scheduler=_make_scheduler(args),
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queue_trials=args.queue_trials,
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resume=args.resume)
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resume=args.resume,
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concurrent=True)
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if __name__ == "__main__":
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144
rllib/tuned_examples/compact-regression-test.yaml
Normal file
144
rllib/tuned_examples/compact-regression-test.yaml
Normal file
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@ -0,0 +1,144 @@
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# This file runs on a single g3.16xl or p3.16xl node. It is suggested
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# to run these in a DLAMI / tensorflow_p36 env. Note that RL runs are
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# inherently high variance, so you'll have to check to see if the
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# rewards reached seem reasonably in line with previous results.
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#
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# The reference results for 0.7.6 are as follows:
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# +----------------------------------------+------------+-----------+--------+------------------+-------------+----------+
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# | Trial name | status | loc | iter | total time (s) | timesteps | reward |
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# |----------------------------------------+------------+-----------+--------+------------------+-------------+----------|
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# | IMPALA_BreakoutNoFrameskip-v4_1acd2414 | TERMINATED | pid=32600 | 296 | 3606.77 | 7152000 | 296.42 |
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# | IMPALA_BreakoutNoFrameskip-v4_1acdd4ea | TERMINATED | pid=32604 | 296 | 3606.33 | 7096500 | 337.74 |
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# | IMPALA_BreakoutNoFrameskip-v4_1ace550a | TERMINATED | pid=32628 | 296 | 3605.84 | 7140000 | 353.77 |
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# | IMPALA_BreakoutNoFrameskip-v4_1acec238 | TERMINATED | pid=32592 | 296 | 3606.5 | 7111000 | 376.08 |
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# | PPO_BreakoutNoFrameskip-v4_1acf6b7a | TERMINATED | pid=32581 | 606 | 3602.28 | 3030000 | 19.2 |
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# | PPO_BreakoutNoFrameskip-v4_1acff068 | TERMINATED | pid=32580 | 595 | 3602.06 | 2975000 | 26.39 |
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# | PPO_BreakoutNoFrameskip-v4_1ad08af0 | TERMINATED | pid=53836 | 594 | 3604.43 | 2970000 | 67.88 |
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# | PPO_BreakoutNoFrameskip-v4_1ad10ad4 | TERMINATED | pid=53869 | 594 | 3601.85 | 2970000 | 28.07 |
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# | APEX_BreakoutNoFrameskip-v4_1ad190f8 | TERMINATED | pid=32605 | 114 | 3622.9 | 4689280 | 30.86 |
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# | APEX_BreakoutNoFrameskip-v4_1ad24f34 | TERMINATED | pid=53855 | 114 | 3621.4 | 4519840 | 28.53 |
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# | APEX_BreakoutNoFrameskip-v4_1ad2d530 | TERMINATED | pid=53891 | 114 | 3618.97 | 4512000 | 31.59 |
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# | APEX_BreakoutNoFrameskip-v4_1ad34dc6 | TERMINATED | pid=53894 | 114 | 3626.85 | 4547360 | 26.49 |
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# | A2C_BreakoutNoFrameskip-v4_1ad40824 | TERMINATED | pid=59452 | 351 | 3607.43 | 3625500 | 29.32 |
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# | A2C_BreakoutNoFrameskip-v4_1ad47fac | TERMINATED | pid=59422 | 351 | 3607.31 | 3625500 | 29.05 |
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# | A2C_BreakoutNoFrameskip-v4_1ad4e5c8 | TERMINATED | pid=59414 | 351 | 3607.11 | 3630000 | 32.25 |
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# | A2C_BreakoutNoFrameskip-v4_1ad5984c | TERMINATED | pid=59199 | 351 | 3610.29 | 3631500 | 26.82 |
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# | DQN_BreakoutNoFrameskip-v4_1ad621cc | TERMINATED | pid=53851 | 24 | 3693.75 | 240000 | 12.89 |
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# | DQN_BreakoutNoFrameskip-v4_1ad69cb0 | TERMINATED | pid=53860 | 24 | 3681.93 | 240000 | 14.72 |
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# | DQN_BreakoutNoFrameskip-v4_1ad75c5e | TERMINATED | pid=53864 | 24 | 3662.21 | 240000 | 13.84 |
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# | DQN_BreakoutNoFrameskip-v4_1ad7d42c | TERMINATED | pid=53885 | 24 | 3697.62 | 240000 | 11.22 |
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# +----------------------------------------+------------+-----------+--------+------------------+-------------+----------+
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atari-impala:
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env: BreakoutNoFrameskip-v4
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run: IMPALA
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num_samples: 4
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stop:
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time_total_s: 3600
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config:
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sample_batch_size: 50
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train_batch_size: 500
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num_workers: 10
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num_envs_per_worker: 5
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clip_rewards: True
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lr_schedule: [
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[0, 0.0005],
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[20000000, 0.000000000001],
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]
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num_gpus: 1
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atari-ppo:
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env: BreakoutNoFrameskip-v4
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run: PPO
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num_samples: 4
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stop:
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time_total_s: 3600
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config:
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lambda: 0.95
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kl_coeff: 0.5
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clip_rewards: True
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clip_param: 0.1
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vf_clip_param: 10.0
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entropy_coeff: 0.01
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train_batch_size: 5000
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sample_batch_size: 100
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sgd_minibatch_size: 500
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num_sgd_iter: 10
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num_workers: 10
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num_envs_per_worker: 5
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batch_mode: truncate_episodes
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observation_filter: NoFilter
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vf_share_layers: true
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num_gpus: 1
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apex:
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env: BreakoutNoFrameskip-v4
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run: APEX
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num_samples: 4
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stop:
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time_total_s: 3600
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config:
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double_q: false
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dueling: false
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num_atoms: 1
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noisy: false
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n_step: 3
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lr: .0001
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adam_epsilon: .00015
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hiddens: [512]
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buffer_size: 1000000
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schedule_max_timesteps: 2000000
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exploration_final_eps: 0.01
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exploration_fraction: .1
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prioritized_replay_alpha: 0.5
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beta_annealing_fraction: 1.0
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final_prioritized_replay_beta: 1.0
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num_gpus: 1
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num_workers: 8
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num_envs_per_worker: 8
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sample_batch_size: 20
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train_batch_size: 512
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target_network_update_freq: 50000
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timesteps_per_iteration: 25000
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atari-a2c:
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env: BreakoutNoFrameskip-v4
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run: A2C
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num_samples: 4
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stop:
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time_total_s: 3600
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config:
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sample_batch_size: 20
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clip_rewards: True
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num_workers: 5
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num_envs_per_worker: 5
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num_gpus: 1
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lr_schedule: [
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[0, 0.0007],
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[20000000, 0.000000000001],
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]
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atari-basic-dqn:
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env: BreakoutNoFrameskip-v4
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run: DQN
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num_samples: 4
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stop:
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time_total_s: 3600
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config:
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double_q: false
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dueling: false
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num_atoms: 1
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noisy: false
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prioritized_replay: false
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n_step: 1
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target_network_update_freq: 8000
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lr: .0000625
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adam_epsilon: .00015
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hiddens: [512]
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learning_starts: 20000
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buffer_size: 1000000
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sample_batch_size: 4
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train_batch_size: 32
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schedule_max_timesteps: 2000000
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exploration_final_eps: 0.01
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exploration_fraction: .1
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prioritized_replay_alpha: 0.5
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beta_annealing_fraction: 1.0
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final_prioritized_replay_beta: 1.0
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num_gpus: 0.2
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timesteps_per_iteration: 10000
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