Logging Experiments#

Tianshou comes with multiple experiment tracking and logging solutions to manage and reproduce your experiments. The dashboard loggers currently available are:

  • TensorboardLogger

  • WandbLogger

  • LazyLogger


Tensorboard tracks your experiment metrics in a local dashboard. Here is how you can use TensorboardLogger in your experiment:

from torch.utils.tensorboard import SummaryWriter
from tianshou.utils import TensorboardLogger

log_path = os.path.join(args.logdir, args.task, "dqn")
writer = SummaryWriter(log_path)
writer.add_text("args", str(args))
logger = TensorboardLogger(writer)
result = trainer(..., logger=logger)


WandbLogger can be used to visualize your experiments in a hosted W&B dashboard. It can be installed via pip install wandb. You can also save your checkpoints in the cloud and restore your runs from those checkpoints. Here is how you can enable WandbLogger:

from tianshou.utils import WandbLogger
from torch.utils.tensorboard import SummaryWriter

logger = WandbLogger(...)
writer = SummaryWriter(log_path)
writer.add_text("args", str(args))
result = trainer(..., logger=logger)

Please refer to WandbLogger documentation for advanced configuration.

For logging checkpoints on any device, you need to define a save_checkpoint_fn which saves the experiment checkpoint and returns the path of the saved checkpoint:

def save_checkpoint_fn(epoch, env_step, gradient_step):
    ckpt_path = ...
    # save model
    return ckpt_path

Then, use this function with WandbLogger to automatically version your experiment checkpoints after every save_interval step.

For resuming runs from checkpoint artifacts on any device, pass the W&B run_id of the run that you want to continue in WandbLogger. It will then download the latest version of the checkpoint and resume your runs from the checkpoint.

The example scripts are under test_psrl.py and atari_dqn.py.


This is a place-holder logger that does nothing.