The pytorch_tensorboardX.py example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX.
The script does the following:
- Trains a simple deep neural network on the PyTorch built-in MNIST dataset
- Creates an experiment named
pytorch with tensorboardXin the
- ClearML automatically captures scalars and text logged using the TensorBoardX
SummaryWriterobject, and the model created by PyTorch
The loss and accuracy metric scalar plots appear in the experiment's page in the ClearML web UI, under SCALARS. The also includes resource utilization plots, which are titled :monitor: machine.
ClearML automatically logs command line options defined with
argparse. They appear in CONFIGURATION >
HYPERPARAMETERS > Args.
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 the model's name takes you to the model's page, where you can view the model's details and access the model.