The pytorch_tensorboard.py example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoard.
The example does the following:
- Trains a simple deep neural network on the PyTorch built-in MNIST dataset.
- Creates an experiment named
pytorch with tensorboardin the
- ClearML automatically captures scalars and text logged using the TensorBoard
SummaryWriterobject, and the model created by PyTorch.
In the example script, the
test functions call the TensorBoard
SummaryWriter.add_scalar method to log loss.
These scalars, along with the resource utilization plots, which are titled :monitor: machine, appear in the experiment's
page in the ClearML web UI under SCALARS.
ClearML automatically tracks images and text output to TensorFlow. They appear in DEBUG SAMPLES.
ClearML automatically logs TensorFlow Definitions. They appear in CONFIGURATION > HYPERPARAMETERS > TF_DEFINE.
Text printed to the console for training progress, as well as all other console output, appear in CONSOLE.
Models created by the experiment appear in the experiment’s ARTIFACTS tab. ClearML automatically logs and tracks models and any snapshots created using PyTorch.
Clicking on a model's name takes you to the model’s page, where you can view the model’s details and access the model.