Get Started

The following instructions assume you have installed the Intel® Extension for PyTorch*. For installation instructions, refer to Installation.

To start using the Intel® Extension for PyTorch* in your code, you need to make the following changes:

1. Import the extension with import intel_extension_for_pytorch as ipex.

2. Invoke the optimize() function to apply optimizations.

3. Convert the imperative model to a graph model.

   • For TorchScript, invoke torch.jit.trace() and torch.jit.freeze()

   • For TorchDynamo, invoke torch.compile(model, backend="ipex")(Experimental feature)

Important: It is highly recommended to import intel_extension_for_pytorch right after import torch, prior to importing other packages.

The example below demostrates how to use the Intel® Extension for PyTorch*:

import torch
############## import ipex ###############
import intel_extension_for_pytorch as ipex
##########################################

model = Model()
model.eval()
data = ...

############## TorchScript ###############
model = ipex.optimize(model, dtype=torch.bfloat16)

with torch.no_grad(), torch.cpu.amp.autocast():
  model = torch.jit.trace(model, data)
  model = torch.jit.freeze(model)
  model(data)
##########################################

############## TorchDynamo ###############
model = ipex.optimize(model, weights_prepack=False)

model = torch.compile(model, backend="ipex")
with torch.no_grad():
  model(data)
##########################################

More examples, including training and usage of low precision data types are available in the Examples section.

In Cheat Sheet, you can find more commands that can help you start using the Intel® Extension for PyTorch*.