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* Add how to guide: How to convert your PyTorch code to use TorchNano for training acceleration * Small nano how-to index format update for openvino inference * Update based on comments * Updated based on comments * Add how-to guide: How to wrap a PyTorch training loop through @nano decorator * Add reference to TorchNano guide in @nano guide * Some small fixes and updates * Small typo fix: bulit -> built * Updates based on comments * Remove validation dataloader based on comments * Order change of two guides * Update based on comments
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Nano How-to Guides
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=========================
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.. note::
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This page is still a work in progress. We are adding more guides.
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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.
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Training Optimization
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-------------------------
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PyTorch Lightning
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~~~~~~~~~~~~~~~~~~~~~~~~~
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* `How to accelerate a PyTorch Lightning application on training workloads through Intel® Extension for PyTorch* <Training/PyTorchLightning/accelerate_pytorch_lightning_training_ipex.html>`_
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* `How to accelerate a PyTorch Lightning application on training workloads through multiple instances <Training/PyTorchLightning/accelerate_pytorch_lightning_training_multi_instance.html>`_
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* `How to use the channels last memory format in your PyTorch Lightning application for training <Training/PyTorchLightning/pytorch_lightning_training_channels_last.html>`_
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* `How to conduct BFloat16 Mixed Precision training in your PyTorch Lightning application <Training/PyTorchLightning/pytorch_lightning_training_bf16.html>`_
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* `How to accelerate a computer vision data processing pipeline <Training/PyTorchLightning/pytorch_lightning_cv_data_pipeline.html>`_
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PyTorch
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~~~~~~~~~~~~~~~~~~~~~~~~~
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* |convert_pytorch_training_torchnano|_
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* |use_nano_decorator_pytorch_training|_
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.. |use_nano_decorator_pytorch_training| replace:: How to accelerate your PyTorch training loop with ``@nano`` decorator
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.. _use_nano_decorator_pytorch_training: Training/PyTorch/use_nano_decorator_pytorch_training.html
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.. |convert_pytorch_training_torchnano| replace:: How to convert your PyTorch training loop to use ``TorchNano`` for acceleration
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.. _convert_pytorch_training_torchnano: Training/PyTorch/convert_pytorch_training_torchnano.html
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TensorFlow
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~~~~~~~~~~~~~~~~~~~~~~~~~
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* `How to accelerate a TensorFlow Keras application on training workloads through multiple instances <Training/TensorFlow/accelerate_tensorflow_training_multi_instance.html>`_
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* |tensorflow_training_embedding_sparseadam_link|_
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.. |tensorflow_training_embedding_sparseadam_link| replace:: How to optimize your model with a sparse ``Embedding`` layer and ``SparseAdam`` optimizer
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.. _tensorflow_training_embedding_sparseadam_link: Training/TensorFlow/tensorflow_training_embedding_sparseadam.html
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General
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~~~~~~~~~~~~~~~~~~~~~~~~~
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* `How to choose the number of processes for multi-instance training <Training/General/choose_num_processes_training.html>`_
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Inference Optimization
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OpenVINO
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~~~~~~~~~~~~~~~~~~~~~~~~~
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* `How to run inference on OpenVINO model <Inference/OpenVINO/openvino_inference.html>`_
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* `How to run asynchronous inference on OpenVINO model <Inference/OpenVINO/openvino_inference_async.html>`_
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PyTorch
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~~~~~~~~~~~~~~~~~~~~~~~~~
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* `How to accelerate a PyTorch inference pipeline through ONNXRuntime <Inference/PyTorch/accelerate_pytorch_inference_onnx.html>`_
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* `How to accelerate a PyTorch inference pipeline through OpenVINO <Inference/PyTorch/accelerate_pytorch_inference_openvino.html>`_
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* `How to accelerate a PyTorch inference pipeline through JIT/IPEX <Inference/PyTorch/accelerate_pytorch_inference_jit_ipex.html>`_
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* `How to accelerate a PyTorch inference pipeline through multiple instances <Inference/PyTorch/multi_instance_pytorch_inference.html>`_
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* `How to quantize your PyTorch model for inference using Intel Neural Compressor <Inference/PyTorch/quantize_pytorch_inference_inc.html>`_
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* `How to quantize your PyTorch model for inference using OpenVINO Post-training Optimization Tools <Inference/PyTorch/quantize_pytorch_inference_pot.html>`_
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* |pytorch_inference_context_manager_link|_
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* `How to save and load optimized IPEX model <Inference/PyTorch/pytorch_save_and_load_ipex.html>`_
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* `How to save and load optimized JIT model <Inference/PyTorch/pytorch_save_and_load_jit.html>`_
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* `How to save and load optimized ONNXRuntime model <Inference/PyTorch/pytorch_save_and_load_onnx.html>`_
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* `How to save and load optimized OpenVINO model <Inference/PyTorch/pytorch_save_and_load_openvino.html>`_
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* `How to find accelerated method with minimal latency using InferenceOptimizer <Inference/PyTorch/inference_optimizer_optimize.html>`_
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.. |pytorch_inference_context_manager_link| replace:: How to use context manager through ``get_context``
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.. _pytorch_inference_context_manager_link: Inference/PyTorch/pytorch_context_manager.html
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Install
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-------------------------
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* `How to install BigDL-Nano in Google Colab <install_in_colab.html>`_
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* `How to install BigDL-Nano on Windows <windows_guide.html>`_ |