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- """Example of using a custom image env and model.
- Both the model and env are trivial (and super-fast), so they are useful
- for running perf microbenchmarks.
- """
- import argparse
- import os
- import ray
- import ray.tune as tune
- from ray.tune import sample_from
- from ray.rllib.examples.env.fast_image_env import FastImageEnv
- from ray.rllib.examples.models.fast_model import FastModel, TorchFastModel
- from ray.rllib.models import ModelCatalog
- parser = argparse.ArgumentParser()
- parser.add_argument("--num-cpus", type=int, default=4)
- parser.add_argument(
- "--framework",
- choices=["tf", "tf2", "tfe", "torch"],
- default="tf",
- help="The DL framework specifier.")
- parser.add_argument("--stop-iters", type=int, default=200)
- parser.add_argument("--stop-timesteps", type=int, default=100000)
- if __name__ == "__main__":
- args = parser.parse_args()
- ray.init(num_cpus=args.num_cpus or None)
- ModelCatalog.register_custom_model(
- "fast_model", TorchFastModel
- if args.framework == "torch" else FastModel)
- config = {
- "env": FastImageEnv,
- "compress_observations": True,
- "model": {
- "custom_model": "fast_model"
- },
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- "num_workers": 2,
- "num_envs_per_worker": 10,
- "num_multi_gpu_tower_stacks": 1,
- "num_aggregation_workers": 1,
- "broadcast_interval": 50,
- "rollout_fragment_length": 100,
- "train_batch_size": sample_from(
- lambda spec: 1000 * max(1, spec.config.num_gpus or 1)),
- "fake_sampler": True,
- "framework": args.framework,
- }
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- }
- tune.run("IMPALA", config=config, stop=stop, verbose=1)
- ray.shutdown()
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