PyTorch Recipes --------------------------------------------- Recipes are bite-sized, actionable examples of how to use specific PyTorch features, different from our full-length tutorials. .. raw:: html
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Basics .. customcarditem:: :header: Loading data in PyTorch :card_description: Learn how to use PyTorch packages to prepare and load common datasets for your model. :image: ../_static/img/thumbnails/cropped/loading-data.PNG :link: ../recipes/recipes/loading_data_recipe.html :tags: Basics .. customcarditem:: :header: Defining a Neural Network :card_description: Learn how to use PyTorch's torch.nn package to create and define a neural network for the MNIST dataset. :image: ../_static/img/thumbnails/cropped/defining-a-network.PNG :link: ../recipes/recipes/defining_a_neural_network.html :tags: Basics .. customcarditem:: :header: What is a state_dict in PyTorch :card_description: Learn how state_dict objects and Python dictionaries are used in saving or loading models from PyTorch. :image: ../_static/img/thumbnails/cropped/what-is-a-state-dict.PNG :link: ../recipes/recipes/what_is_state_dict.html :tags: Basics .. customcarditem:: :header: Saving and loading models for inference in PyTorch :card_description: Learn about the two approaches for saving and loading models for inference in PyTorch - via the state_dict and via the entire model. :image: ../_static/img/thumbnails/cropped/saving-and-loading-models-for-inference.PNG :link: ../recipes/recipes/saving_and_loading_models_for_inference.html :tags: Basics .. customcarditem:: :header: Saving and loading a general checkpoint in PyTorch :card_description: Saving and loading a general checkpoint model for inference or resuming training can be helpful for picking up where you last left off. In this recipe, explore how to save and load multiple checkpoints. :image: ../_static/img/thumbnails/cropped/saving-and-loading-general-checkpoint.PNG :link: ../recipes/recipes/saving_and_loading_a_general_checkpoint.html :tags: Basics .. customcarditem:: :header: Saving and loading multiple models in one file using PyTorch :card_description: In this recipe, learn how saving and loading multiple models can be helpful for reusing models that you have previously trained. :image: ../_static/img/thumbnails/cropped/saving-multiple-models.PNG :link: ../recipes/recipes/saving_multiple_models_in_one_file.html :tags: Basics .. customcarditem:: :header: Warmstarting model using parameters from a different model in PyTorch :card_description: Learn how warmstarting the training process by partially loading a model or loading a partial model can help your model converge much faster than training from scratch. :image: ../_static/img/thumbnails/cropped/warmstarting-models.PNG :link: ../recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html :tags: Basics .. customcarditem:: :header: Saving and loading models across devices in PyTorch :card_description: Learn how saving and loading models across devices (CPUs and GPUs) is relatively straightforward using PyTorch. :image: ../_static/img/thumbnails/cropped/saving-and-loading-models-across-devices.PNG :link: ../recipes/recipes/save_load_across_devices.html :tags: Basics .. customcarditem:: :header: Zeroing out gradients in PyTorch :card_description: Learn when you should zero out gradients and how doing so can help increase the accuracy of your model. :image: ../_static/img/thumbnails/cropped/zeroing-out-gradients.PNG :link: ../recipes/recipes/zeroing_out_gradients.html :tags: Basics .. customcarditem:: :header: PyTorch Benchmark :card_description: Learn how to use PyTorch's benchmark module to measure and compare the performance of your code :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/benchmark.html :tags: Basics .. customcarditem:: :header: PyTorch Benchmark (quick start) :card_description: Learn how to measure snippet run times and collect instructions. :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/timer_quick_start.html :tags: Basics .. customcarditem:: :header: PyTorch Profiler :card_description: Learn how to use PyTorch's profiler to measure operators time and memory consumption :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/profiler_recipe.html :tags: Basics .. Interpretability .. customcarditem:: :header: Model Interpretability using Captum :card_description: Learn how to use Captum attribute the predictions of an image classifier to their corresponding image features and visualize the attribution results. :image: ../_static/img/thumbnails/cropped/model-interpretability-using-captum.png :link: ../recipes/recipes/Captum_Recipe.html :tags: Interpretability,Captum .. customcarditem:: :header: How to use TensorBoard with PyTorch :card_description: Learn basic usage of TensorBoard with PyTorch, and how to visualize data in TensorBoard UI :image: ../_static/img/thumbnails/tensorboard_scalars.png :link: ../recipes/recipes/tensorboard_with_pytorch.html :tags: Visualization,TensorBoard .. Quantization .. customcarditem:: :header: Dynamic Quantization :card_description: Apply dynamic quantization to a simple LSTM model. :image: ../_static/img/thumbnails/cropped/using-dynamic-post-training-quantization.png :link: ../recipes/recipes/dynamic_quantization.html :tags: Quantization,Text,Model-Optimization .. Production Development .. customcarditem:: :header: TorchScript for Deployment :card_description: Learn how to export your trained model in TorchScript format and how to load your TorchScript model in C++ and do inference. :image: ../_static/img/thumbnails/cropped/torchscript_overview.png :link: ../recipes/torchscript_inference.html :tags: TorchScript .. customcarditem:: :header: Deploying with Flask :card_description: Learn how to use Flask, a lightweight web server, to quickly setup a web API from your trained PyTorch model. :image: ../_static/img/thumbnails/cropped/using-flask-create-restful-api.png :link: ../recipes/deployment_with_flask.html :tags: Production,TorchScript .. customcarditem:: :header: PyTorch Mobile Performance Recipes :card_description: List of recipes for performance optimizations for using PyTorch on Mobile (Android and iOS). :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/mobile_perf.html :tags: Mobile,Model-Optimization .. customcarditem:: :header: Making Android Native Application That Uses PyTorch Android Prebuilt Libraries :card_description: Learn how to make Android application from the scratch that uses LibTorch C++ API and uses TorchScript model with custom C++ operator. :image: ../_static/img/thumbnails/cropped/android.png :link: ../recipes/android_native_app_with_custom_op.html :tags: Mobile .. customcarditem:: :header: Fuse Modules recipe :card_description: Learn how to fuse a list of PyTorch modules into a single module to reduce the model size before quantization. :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/fuse.html :tags: Mobile .. customcarditem:: :header: Quantization for Mobile Recipe :card_description: Learn how to reduce the model size and make it run faster without losing much on accuracy. :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/quantization.html :tags: Mobile,Quantization .. customcarditem:: :header: Script and Optimize for Mobile :card_description: Learn how to convert the model to TorchScipt and (optional) optimize it for mobile apps. :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/script_optimized.html :tags: Mobile .. customcarditem:: :header: Model Preparation for iOS Recipe :card_description: Learn how to add the model in an iOS project and use PyTorch pod for iOS. :image: ../_static/img/thumbnails/cropped/ios.png :link: ../recipes/model_preparation_ios.html :tags: Mobile .. customcarditem:: :header: Model Preparation for Android Recipe :card_description: Learn how to add the model in an Android project and use the PyTorch library for Android. :image: ../_static/img/thumbnails/cropped/android.png :link: ../recipes/model_preparation_android.html :tags: Mobile .. customcarditem:: :header: Profiling PyTorch RPC-Based Workloads :card_description: How to use the PyTorch profiler to profile RPC-based workloads. :image: ../_static/img/thumbnails/cropped/profile.png :link: ../recipes/distributed_rpc_profiling.html :tags: Production .. Automatic Mixed Precision .. customcarditem:: :header: Automatic Mixed Precision :card_description: Use torch.cuda.amp to reduce runtime and save memory on NVIDIA GPUs. :image: ../_static/img/thumbnails/cropped/amp.png :link: ../recipes/recipes/amp_recipe.html :tags: Model-Optimization .. Performance .. customcarditem:: :header: Performance Tuning Guide :card_description: Tips for achieving optimal performance. :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/tuning_guide.html :tags: Model-Optimization .. Distributed Training .. customcarditem:: :header: Shard Optimizer States with ZeroRedundancyOptimizer :card_description: How to use ZeroRedundancyOptimizer to reduce memory consumption. :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/zero_redundancy_optimizer.html :tags: Distributed-Training .. customcarditem:: :header: Direct Device-to-Device Communication with TensorPipe RPC :card_description: How to use RPC with direct GPU-to-GPU communication. :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/cuda_rpc.html :tags: Distributed-Training .. End of tutorial card section .. raw:: html
.. ----------------------------------------- .. Page TOC .. ----------------------------------------- .. toctree:: :hidden: /recipes/recipes/loading_data_recipe /recipes/recipes/defining_a_neural_network /recipes/recipes/what_is_state_dict /recipes/recipes/saving_and_loading_models_for_inference /recipes/recipes/saving_and_loading_a_general_checkpoint /recipes/recipes/saving_multiple_models_in_one_file /recipes/recipes/warmstarting_model_using_parameters_from_a_different_model /recipes/recipes/save_load_across_devices /recipes/recipes/zeroing_out_gradients /recipes/recipes/profiler_recipe /recipes/recipes/Captum_Recipe /recipes/recipes/tensorboard_with_pytorch /recipes/recipes/dynamic_quantization /recipes/recipes/amp_recipe /recipes/recipes/tuning_guide /recipes/torchscript_inference /recipes/deployment_with_flask /recipes/distributed_rpc_profiling /recipes/zero_redundancy_optimizer /recipes/cuda_rpc