mirror of
https://github.com/vale981/ray
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138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
"""
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This example script demonstrates how one can define a custom logger
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object for any RLlib Trainer via the Trainer's config dict's
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"logger_config" key.
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By default (logger_config=None), RLlib will construct a tune
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UnifiedLogger object, which logs JSON, CSV, and TBX output.
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Below examples include:
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- Disable logging entirely.
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- Using only one of tune's Json, CSV, or TBX loggers.
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- Defining a custom logger (by sub-classing tune.logger.py::Logger).
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"""
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import argparse
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import os
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from ray.rllib.utils.test_utils import check_learning_achieved
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from ray.tune.logger import Logger
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--run",
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type=str,
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default="PPO",
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help="The RLlib-registered algorithm to use.")
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parser.add_argument("--num-cpus", type=int, default=0)
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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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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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parser.add_argument(
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"--stop-iters",
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type=int,
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default=200,
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help="Number of iterations to train.")
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parser.add_argument(
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"--stop-timesteps",
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type=int,
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default=100000,
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help="Number of timesteps to train.")
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parser.add_argument(
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"--stop-reward",
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type=float,
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default=150.0,
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help="Reward at which we stop training.")
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class MyPrintLogger(Logger):
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"""Logs results by simply printing out everything.
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"""
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def _init(self):
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# Custom init function.
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print("Initializing ...")
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# Setting up our log-line prefix.
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self.prefix = self.config.get("logger_config").get("prefix")
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def on_result(self, result: dict):
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# Define, what should happen on receiving a `result` (dict).
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print(f"{self.prefix}: {result}")
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def close(self):
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# Releases all resources used by this logger.
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print("Closing")
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def flush(self):
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# Flushing all possible disk writes to permanent storage.
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print("Flushing ;)", flush=True)
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if __name__ == "__main__":
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import ray
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from ray import tune
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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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config = {
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"env": "CartPole-v0"
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if args.run not in ["DDPG", "TD3"] else "Pendulum-v0",
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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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"framework": args.framework,
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# Run with tracing enabled for tfe/tf2.
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"eager_tracing": args.framework in ["tfe", "tf2"],
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# Setting up a custom logger config.
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# ----------------------------------
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# The following are different examples of custom logging setups:
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# 1) Disable logging entirely.
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# "logger_config": {
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# # Use the tune.logger.NoopLogger class for no logging.
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# "type": "ray.tune.logger.NoopLogger",
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# },
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# 2) Use tune's JsonLogger only.
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# Alternatively, use `CSVLogger` or `TBXLogger` instead of
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# `JsonLogger` in the "type" key below.
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# "logger_config": {
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# "type": "ray.tune.logger.JsonLogger",
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# # Optional: Custom logdir (do not define this here
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# # for using ~/ray_results/...).
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# "logdir": "/tmp",
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# },
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# 3) Custom logger (see `MyPrintLogger` class above).
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"logger_config": {
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# Provide the class directly or via fully qualified class
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# path.
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"type": MyPrintLogger,
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# `config` keys:
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"prefix": "ABC",
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# Optional: Custom logdir (do not define this here
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# for using ~/ray_results/...).
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# "logdir": "/somewhere/on/my/file/system/"
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}
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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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results = tune.run(
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args.run, config=config, stop=stop, verbose=2, loggers=[MyPrintLogger])
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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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