mirror of
https://github.com/vale981/ray
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fcf897ee72
update rllib example to use Tuner API. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
# Explains/tests Issues:
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# https://github.com/ray-project/ray/issues/6928
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# https://github.com/ray-project/ray/issues/6732
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import argparse
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from gym.spaces import Discrete, Box
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import numpy as np
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import os
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from ray import air, tune
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from ray.rllib.examples.env.random_env import RandomEnv
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from ray.rllib.examples.models.mobilenet_v2_with_lstm_models import (
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MobileV2PlusRNNModel,
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TorchMobileV2PlusRNNModel,
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)
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from ray.rllib.models import ModelCatalog
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from ray.rllib.utils.framework import try_import_tf
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tf1, tf, tfv = try_import_tf()
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cnn_shape = (4, 4, 3)
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# The torch version of MobileNetV2 does channels first.
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cnn_shape_torch = (3, 224, 224)
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parser = argparse.ArgumentParser()
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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("--stop-iters", type=int, default=200)
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parser.add_argument("--stop-reward", type=float, default=0.0)
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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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# Register our custom model.
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ModelCatalog.register_custom_model(
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"my_model",
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TorchMobileV2PlusRNNModel
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if args.framework == "torch"
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else MobileV2PlusRNNModel,
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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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# Configure our Trainer.
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config = {
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"env": RandomEnv,
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"framework": args.framework,
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"model": {
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"custom_model": "my_model",
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# Extra config passed to the custom model's c'tor as kwargs.
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"custom_model_config": {
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# By default, torch CNNs use "channels-first",
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# tf "channels-last".
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"cnn_shape": cnn_shape_torch
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if args.framework == "torch"
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else cnn_shape,
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},
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"max_seq_len": 20,
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"vf_share_layers": True,
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},
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"num_workers": 0, # no parallelism
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"env_config": {
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"action_space": Discrete(2),
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# Test a simple Image observation space.
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"observation_space": Box(
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0.0,
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1.0,
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shape=cnn_shape_torch if args.framework == "torch" else cnn_shape,
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dtype=np.float32,
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),
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},
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}
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tune.Tuner(
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"PPO", param_space=config, run_config=air.RunConfig(stop=stop, verbose=1)
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).fit()
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