See the Quick Start Guide on how to install and configure the profiler.
To profile PyTorch, add the following code around a code step, e.g. training batch or a prediction. Only some steps will be profiled; the profiler decides which steps to profile for optimal statistics and low overhead. See profiling API reference for full documentation.
Profile PyTorch using
with context manager:
with profile_step(): # training batch, prediction, etc.
from graphsignal.profilers.pytorch import profile_step step = profile_step() # training batch, prediction, etc. step.stop()
The PyTorch MNIST example illustrates where and how to add the
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.