Nano How-to Guides ========================= .. note:: This page is still a work in progress. We are adding more guides. In Nano How-to Guides, you could expect to find multiple task-oriented, bite-sized, and executable examples. These examples will show you various tasks that BigDL-Nano could help you accomplish smoothly. Training Optimization ------------------------- PyTorch Lightning ~~~~~~~~~~~~~~~~~~~~~~~~~ * `How to accelerate a PyTorch Lightning application on training workloads through IntelĀ® Extension for PyTorch* `_ * `How to accelerate a PyTorch Lightning application on training workloads through multiple instances `_ * `How to use the channels last memory format in your PyTorch Lightning application for training `_ * `How to conduct BFloat16 Mixed Precision training in your PyTorch Lightning application `_ * `How to accelerate a computer vision data processing pipeline `_ .. toctree:: :maxdepth: 1 :hidden: Training/PyTorchLightning/accelerate_pytorch_lightning_training_ipex Training/PyTorchLightning/accelerate_pytorch_lightning_training_multi_instance Training/PyTorchLightning/pytorch_lightning_training_channels_last Training/PyTorchLightning/pytorch_lightning_training_bf16 Training/PyTorchLightning/pytorch_lightning_cv_data_pipeline Inference Optimization ------------------------- PyTorch ~~~~~~~~~~~~~~~~~~~~~~~~~ * `How to accelerate a PyTorch inference pipeline through ONNXRuntime `_ * `How to accelerate a PyTorch inference pipeline through OpenVINO `_ * `How to quantize your PyTorch model for inference using Intel Neural Compressor `_ * `How to quantize your PyTorch model for inference using OpenVINO Post-training Optimization Tools `_ .. toctree:: :maxdepth: 1 :hidden: Inference/PyTorch/accelerate_pytorch_inference_onnx Inference/PyTorch/accelerate_pytorch_inference_openvino Inference/PyTorch/quantize_pytorch_inference_inc Inference/PyTorch/quantize_pytorch_inference_pot Install ------------------------- * `How to install BigDL-Nano in Google Colab `_ .. toctree:: :maxdepth: 1 :hidden: install_in_colab