Cheat Sheet

Get started with Intel® Extension for PyTorch* using the following commands:

Description	Command
Basic CPU Installation	python -m pip install intel_extension_for_pytorch
Basic GPU Installation	pip install torch==<version> --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ pip install intel_extension_for_pytorch==<version> --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/cpu/us/-xpu
Import Intel® Extension for PyTorch*	import intel_extension_for_pytorch as ipex
Capture a Verbose Log (Command Prompt)	export ONEDNN_VERBOSE=1
Optimization During Training	model = ... optimizer = ... model.train() model, optimizer = ipex.optimize(model, optimizer=optimizer)
Optimization During Inference	model = ... model.eval() model = ipex.optimize(model)
Optimization Using the Low-Precision Data Type bfloat16 During Training (Default FP32)	model = ... optimizer = ... model.train() model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=torch.bfloat16) with torch.no_grad(): with torch.cpu.amp.autocast(): model(data)
Optimization Using the Low-Precision Data Type bfloat16 During Inference (Default FP32)	model = ... model.eval() model = ipex.optimize(model, dtype=torch.bfloat16) with torch.cpu.amp.autocast(): model(data)
[Experimental] Fast BERT Optimization	from transformers import BertModel model = BertModel.from_pretrained("bert-base-uncased") model.eval() model = ipex.fast_bert(model, dtype=torch.bfloat16)
Run CPU Launch Script (Command Prompt): Automate Configuration Settings for Performance	ipexrun [knobs] <your_pytorch_script> [args]
[Experimental] Run HyperTune to perform hyperparameter/execution configuration search	python -m intel_extension_for_pytorch.cpu.hypertune --conf-file <your_conf_file> <your_python_script> [args]
[Experimental] Enable Graph capture	model = … model.eval() model = ipex.optimize(model, graph_mode=True)
Post-Training INT8 Quantization (Static)	model = … model.eval() data = … qconfig = ipex.quantization.default_static_qconfig prepared_model = ipex.quantization.prepare(model, qconfig, example_inputs=data, anyplace=False) for d in calibration_data_loader(): prepared_model(d) converted_model = ipex.quantization.convert(prepared_model)
Post-Training INT8 Quantization (Dynamic)	model = … model.eval() data = … qconfig = ipex.quantization.default_dynamic_qconfig prepared_model = ipex.quantization.prepare(model, qconfig, example_inputs=data) converted_model = ipex.quantization.convert(prepared_model)
[Experimental] Post-Training INT8 Quantization (Tuning Recipe):	model = … model.eval() data = … qconfig = ipex.quantization.default_static_qconfig prepared_model = ipex.quantization.prepare(model, qconfig, example_inputs=data, inplace=False) tuned_model = ipex.quantization.autotune(prepared_model, calibration_data_loader, eval_function, sampling_sizes=[100], accuracy_criterion={'relative': .01}, tuning_time=0) convert_model = ipex.quantization.convert(tuned_model)