TensorBoardX

The pytorch_tensorboardX.py example demonstrates the integration of ClearML into code which uses PyTorch and TensorBoardX. It trains a simple deep neural network on the PyTorch built-in MNIST dataset. It creates a TensorBoardX SummaryWriter object to log scalars during training, scalars and debug samples during testing, and a test text message to the console (a test message to demonstrate ClearML). When the script runs, it creates an experiment named pytorch with tensorboardX which is associated with the examples project in the ClearML Web UI.

Scalars

The loss and accuracy metric scalar plots appear in RESULTS > SCALARS, along with the resource utilization plots, which are titled :monitor: machine.

image

Hyperparameters

ClearML automatically logs command line options when you use argparse. They appear in CONFIGURATIONS > HYPER PARAMETERS > Args.

image

Log

Text printed to the console for training progress, as well as all other console output, appear in RESULTS > LOG.

image

Artifacts

Model artifacts associated with the experiment appear in the experiment info panel (in the EXPERIMENTS tab), and in the model info panel (in the MODELS tab).

The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).

image

The model info panel contains the model details, including the model URL, framework, and snapshot locations.

image