Model tracing

DFTracer can trace the execution of PyTorch models, providing insights into the model’s performance and behavior during training.

DFTracer Python APIs

This section describes how to use DFTracer for profiling pytorch applications.

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Installing DFTracer with model tracing

Install DFTracer with model tracing

Function decorator style profiling

With python applications, after initializing the dftracer module developers can use decorator provided within dftracer.dynamo to tag functions that need to be profiled. To use the function decorators, they can be initialized in place or globally to reuse within many functions.

dftracer.dynamo import dft_fn as dyn_fn
from dftracer.logger import dftracer

log_inst = dftracer.initialize_log(logfile=None, data_dir=None, process_id=-1)

dyn = dyn_fn("dynamo")

class SimpleModel(torch.nn.Module):
  def __init__(self):
      super(SimpleModel, self).__init__()
      self.conv = torch.nn.Conv2d(3, 16, 3, 1)
      self.fc = torch.nn.Linear(16 * 15 * 15, 10)

  @dyn.compile
  def forward(self, x):
      x = self.conv(x)
      x = torch.nn.functional.relu(x)
      x = torch.nn.functional.max_pool2d(x, 2)
      x = x.view(x.size(0), -1)
      x = self.fc(x)
      return x

Model profiling