mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
"""Example of using a custom image env and model.
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Both the model and env are trivial (and super-fast), so they are useful
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for running perf microbenchmarks.
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"""
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import argparse
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import ray
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import ray.tune as tune
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from ray.tune import sample_from
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from ray.rllib.examples.env.fast_image_env import FastImageEnv
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from ray.rllib.examples.models.fast_model import FastModel, TorchFastModel
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from ray.rllib.models import ModelCatalog
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-cpus", type=int, default=2)
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parser.add_argument("--torch", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=200)
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parser.add_argument("--stop-timesteps", type=int, default=100000)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=args.num_cpus or None)
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ModelCatalog.register_custom_model(
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"fast_model", TorchFastModel if args.torch else FastModel)
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config = {
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"env": FastImageEnv,
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"compress_observations": True,
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"model": {
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"custom_model": "fast_model"
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},
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"num_gpus": 0,
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"num_workers": 2,
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"num_envs_per_worker": 10,
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"num_data_loader_buffers": 1,
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"num_aggregation_workers": 1,
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"broadcast_interval": 50,
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"rollout_fragment_length": 100,
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"train_batch_size": sample_from(
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lambda spec: 1000 * max(1, spec.config.num_gpus)),
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"fake_sampler": True,
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"framework": "torch" if args.torch else "tf",
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}
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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}
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tune.run("IMPALA", config=config, stop=stop)
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ray.shutdown()
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