mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
"""Example of a custom gym environment and model. Run this for a demo.
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This example shows:
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- using a custom environment
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- using a custom model
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- using Tune for grid search
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You can visualize experiment results in ~/ray_results using TensorBoard.
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"""
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import argparse
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import ray
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from ray import tune
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from ray.rllib.examples.env.gpu_requiring_env import GPURequiringEnv
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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from ray.rllib.utils.test_utils import check_learning_achieved
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tf1, tf, tfv = try_import_tf()
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torch, nn = try_import_torch()
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
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)
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parser.add_argument(
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"--framework",
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choices=["tf", "tf2", "tfe", "torch"],
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default="tf",
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help="The DL framework specifier.",
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)
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parser.add_argument("--num-gpus", type=float, default=0.5)
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parser.add_argument("--num-workers", type=int, default=1)
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parser.add_argument("--num-gpus-per-worker", type=float, default=0.0)
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parser.add_argument("--num-envs-per-worker", type=int, default=1)
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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.",
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)
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parser.add_argument(
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"--stop-iters", type=int, default=50, help="Number of iterations to train."
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)
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parser.add_argument(
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"--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
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)
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parser.add_argument(
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"--stop-reward", type=float, default=180.0, help="Reward at which we stop training."
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)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=4)
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# These configs have been tested on a p2.8xlarge machine (8 GPUs, 16 CPUs),
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# where ray was started using only one of these GPUs:
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# $ ray start --num-gpus=1 --head
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# Note: A strange error could occur when using tf:
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# "NotImplementedError: Cannot convert a symbolic Tensor
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# (default_policy/cond/strided_slice:0) to a numpy array."
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# In rllib/utils/exploration/random.py.
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# Fix: Install numpy version 1.19.5.
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# Tested arg combinations (4 tune trials will be setup; see
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# tune.grid_search over 4 learning rates below):
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# - num_gpus=0.5 (2 tune trials should run in parallel).
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# - num_gpus=0.3 (3 tune trials should run in parallel).
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# - num_gpus=0.25 (4 tune trials should run in parallel)
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# - num_gpus=0.2 + num_gpus_per_worker=0.1 (1 worker) -> 0.3
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# -> 3 tune trials should run in parallel.
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# - num_gpus=0.2 + num_gpus_per_worker=0.1 (2 workers) -> 0.4
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# -> 2 tune trials should run in parallel.
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# - num_gpus=0.4 + num_gpus_per_worker=0.1 (2 workers) -> 0.6
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# -> 1 tune trial should run in parallel.
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config = {
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# Setup the test env as one that requires a GPU, iff
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# num_gpus_per_worker > 0.
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"env": GPURequiringEnv if args.num_gpus_per_worker > 0.0 else "CartPole-v0",
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# How many GPUs does the local worker (driver) need? For most algos,
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# this is where the learning updates happen.
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# Set this to > 1 for multi-GPU learning.
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"num_gpus": args.num_gpus,
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# How many RolloutWorkers (each with n environment copies:
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# `num_envs_per_worker`)?
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"num_workers": args.num_workers,
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# How many GPUs does each RolloutWorker (`num_workers`) need?
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"num_gpus_per_worker": args.num_gpus_per_worker,
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# This setting should not really matter as it does not affect the
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# number of GPUs reserved for each worker.
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"num_envs_per_worker": args.num_envs_per_worker,
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# 4 tune trials altogether.
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"lr": tune.grid_search([0.005, 0.003, 0.001, 0.0001]),
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"framework": args.framework,
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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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"episode_reward_mean": args.stop_reward,
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}
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# Note: The above GPU settings should also work in case you are not
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# running via tune.run(), but instead do:
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# >> from ray.rllib.algorithms.ppo import PPO
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# >> trainer = PPO(config=config)
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# >> for _ in range(10):
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# >> results = trainer.train()
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# >> print(results)
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results = tune.run(args.run, config=config, stop=stop)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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