For metrics we recommend using Tensorboard to log metrics directly to cloud storage along side your model. As the model trains you can launch a tensorboard instance locally to monitor your model progress:
$ tensorboard --log-dir provider://path/to/logs
Or you can use the
torchx.components.metrics.tensorboard() component as
part of your pipeline.
See the Trainer Example for an example on how to use the PyTorch Lightning TensorboardLogger.
PyTorch Tensorboard Tutorial https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html
PyTorch Lightning Loggers https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html
- torchx.components.metrics.tensorboard(logdir: str, image: str = 'ghcr.io/pytorch/torchx:0.6.0dev0', timeout: float = 3600, port: int = 6006, start_on_file: str = '', exit_on_file: str = '') AppDef [source]¶
This component runs a Tensorboard server which will render the logs specified by logdir.
Since Tensorboard runs as a service you need to specify the termination conditions. This consists of a timeout as well as an optional
exit_on_filewhich will cause the service to quit when that path is created.
The files are periodically polled for existence via fsspec and will trigger the corresponding behavior when created.
logdir – fsspec path to the Tensorboard logs
image – image to use
timeout – maximum time to run before exiting (seconds)
start_on_file – start the server when the fsspec path is created
exit_on_file – shutdown the server when the fsspec path is created