wandb#
Source code: tianshou/utils/logger/wandb.py
- class WandbLogger(train_interval: int = 1000, test_interval: int = 1, update_interval: int = 1000, info_interval: int = 1, save_interval: int = 1000, write_flush: bool = True, project: str | None = None, name: str | None = None, entity: str | None = None, run_id: str | None = None, config: Namespace | dict | None = None, monitor_gym: bool = True)[source]#
Weights and Biases logger that sends data to https://wandb.ai/.
This logger creates three panels with plots: train, test, and update. Make sure to select the correct access for each panel in weights and biases:
Example of usage:
logger = WandbLogger() logger.load(SummaryWriter(log_path)) result = OnpolicyTrainer(policy, train_collector, test_collector, logger=logger).run()
- Parameters:
train_interval – the log interval in log_train_data(). Default to 1000.
test_interval – the log interval in log_test_data(). Default to 1.
update_interval – the log interval in log_update_data(). Default to 1000.
info_interval – the log interval in log_info_data(). Default to 1.
save_interval – the save interval in save_data(). Default to 1 (save at the end of each epoch).
write_flush – whether to flush tensorboard result after each add_scalar operation. Default to True.
project (str) – W&B project name. Default to “tianshou”.
name (str) – W&B run name. Default to None. If None, random name is assigned.
entity (str) – W&B team/organization name. Default to None.
run_id (str) – run id of W&B run to be resumed. Default to None.
config (argparse.Namespace) – experiment configurations. Default to None.
- restore_data() tuple[int, int, int] [source]#
Return the metadata from existing log.
If it finds nothing or an error occurs during the recover process, it will return the default parameters.
- Returns:
epoch, env_step, gradient_step.
- save_data(epoch: int, env_step: int, gradient_step: int, save_checkpoint_fn: Callable[[int, int, int], str] | None = None) None [source]#
Use writer to log metadata when calling
save_checkpoint_fn
in trainer.- Parameters:
epoch – the epoch in trainer.
env_step – the env_step in trainer.
gradient_step – the gradient_step in trainer.
save_checkpoint_fn (function) – a hook defined by user, see trainer documentation for detail.
- write(step_type: str, step: int, data: dict[str, int | Number | number | ndarray | float]) None [source]#
Specify how the writer is used to log data.
- Parameters:
step_type (str) – namespace which the data dict belongs to.
step – stands for the ordinate of the data dict.
data – the data to write with format
{key: value}
.