67 lines
2.4 KiB
ReStructuredText
67 lines
2.4 KiB
ReStructuredText
Logging Experiments
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===================
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Tianshou comes with multiple experiment tracking and logging solutions to manage and reproduce your experiments.
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The dashboard loggers currently available are:
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* :class:`~tianshou.utils.TensorboardLogger`
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* :class:`~tianshou.utils.WandbLogger`
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* :class:`~tianshou.utils.LazyLogger`
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TensorboardLogger
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-----------------
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Tensorboard tracks your experiment metrics in a local dashboard. Here is how you can use TensorboardLogger in your experiment:
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::
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.utils import TensorboardLogger
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log_path = os.path.join(args.logdir, args.task, "dqn")
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger = TensorboardLogger(writer)
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result = trainer(..., logger=logger)
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WandbLogger
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-----------
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:class:`~tianshou.utils.WandbLogger` can be used to visualize your experiments in a hosted `W&B dashboard <https://wandb.ai/home>`_. 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:
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::
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from tianshou.utils import WandbLogger
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from torch.utils.tensorboard import SummaryWriter
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logger = WandbLogger(...)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger.load(writer)
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result = trainer(..., logger=logger)
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Please refer to :class:`~tianshou.utils.WandbLogger` documentation for advanced configuration.
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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:
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::
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def save_checkpoint_fn(epoch, env_step, gradient_step):
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ckpt_path = ...
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# save model
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return ckpt_path
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Then, use this function with ``WandbLogger`` to automatically version your experiment checkpoints after every ``save_interval`` step.
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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.
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The example scripts are under `test_psrl.py <https://github.com/thu-ml/tianshou/blob/master/test/modelbased/test_psrl.py>`_ and `atari_dqn.py <https://github.com/thu-ml/tianshou/blob/master/examples/atari/atari_dqn.py>`_.
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LazyLogger
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----------
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This is a place-holder logger that does nothing.
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