logging.rst

Logging

d3rlpy.logging

d3rlpy provides a customizable interface for logging metrics, LoggerAdapter and LoggerAdapterFactory.

import d3rlpy

dataset, env = d3rlpy.datasets.get_cartpole()

dqn = d3rlpy.algos.DQNConfig().create()

dqn.fit(
   dataset=dataset,
   n_steps=100000,
   # set FileAdapterFactory to save metrics as CSV files
   logger_adapter=d3rlpy.logging.FileAdapterFactory(root_dir="d3rlpy_logs"),
)

LoggerAdapterFactory is a parent interface that instantiates LoggerAdapter at the beginning of training. You can also use CombineAdapter to combine multiple LoggerAdapter in the same training.

# combine FileAdapterFactory and TensorboardAdapterFactory
logger_adapter = d3rlpy.logging.CombineAdapterFactory([
   d3rlpy.logging.FileAdapterFactory(root_dir="d3rlpy_logs"),
   d3rlpy.logging.TensorboardAdapterFactory(root_dir="tensorboard_logs"),
])

dqn.fit(dataset=dataset, n_steps=100000, logger_adapter=logger_adapter)

LoggerAdapter

LoggerAdapter is an inner interface of LoggerAdapterFactory. You can implement your own LoggerAdapter for 3rd-party visualizers.

import d3rlpy

class CustomAdapter(d3rlpy.logging.LoggerAdapter):
    def write_params(self, params: Dict[str, Any]) -> None:
        # save dictionary as json file
        with open("params.json", "w") as f:
            f.write(json.dumps(params, default=default_json_encoder, indent=2))

    def before_write_metric(self, epoch: int, step: int) -> None:
        pass

    def write_metric(
        self, epoch: int, step: int, name: str, value: float
    ) -> None:
        with open(f"{name}.csv", "a") as f:
            print(f"{epoch},{step},{value}", file=f)

    def after_write_metric(self, epoch: int, step: int) -> None:
        pass

    def save_model(self, epoch: int, algo: Any) -> None:
        algo.save(f"model_{epoch}.d3")

    def close(self) -> None:
        pass

d3rlpy.logging.LoggerAdapter d3rlpy.logging.FileAdapter d3rlpy.logging.TensorboardAdapter d3rlpy.logging.NoopAdapter d3rlpy.logging.CombineAdapter

LoggerAdapterFactory

LoggerAdapterFactory is an interface that instantiates LoggerAdapter at the beginning of training. You can implement your own LoggerAdapterFactory for 3rd-party visualizers.

import d3rlpy

class CustomAdapterFactory(d3rlpy.logging.LoggerAdapterFactory):
    def create(self, experiment_name: str) -> d3rlpy.logging.FileAdapter:
        return CustomAdapter()

d3rlpy.logging.LoggerAdapterFactory d3rlpy.logging.FileAdapterFactory d3rlpy.logging.TensorboardAdapterFactory d3rlpy.logging.NoopAdapterFactory d3rlpy.logging.CombineAdapterFactory