PyTorch Lightning

See the Quick Start Guide on how to install and configure the profiler.

To profile PyTorch Lightning training and inference, use a callback. Only some of the training, validation and test batches will be profiled; the profiler decides which batches to profile for optimal statistics and low overhead.

Profile training, validation and prediction:

from graphsignal.profilers.pytorch_lightning import GraphsignalCallback

trainer = Trainer(..., callbacks=GraphsignalCallback()])

Note: avoid using other PyTorch Lightning profilers simultaneously.


The PyTorch Lightning MNIST example illustrates how to add the profiler callback.

Distributed workloads

Graphsignal provides built-in support for distributed training and inference. It is only necessary to provide the same run ID to all workers. Refer to Distributed Workloads section for more information.