PyTorch Recipes
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Recipes are bite-sized bite-sized, actionable examples of how to use specific PyTorch features, different from our full-length tutorials.

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.. Basics

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: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

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:header: Defining a Neural Network
:card_description: Learn how to use PyTorch's torch.nn package to create and define a neural network the MNIST dataset.
:image: ../_static/img/thumbnails/cropped/defining-a-network.PNG
:link: ../recipes/recipes/defining_a_neural_network.html
:tags: Basics

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:header: What is a state_dict in PyTorch
:card_description: Learn how state_dict objects, 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

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: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

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: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

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: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

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: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

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: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

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:header: Zeroing out gradients in PyTorch
:card_description: Learn when you should zero out graidents 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

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: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.html
:tags: Basics

.. Customization

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:header: Custom Datasets, Transforms & Dataloaders
:card_description: Learn how to leverage the PyTorch dataset API to easily create a custom dataset and custom dataloader.
:image: ../_static/img/thumbnails/cropped/custom-datasets-transforms-and-dataloaders.png
:link: ../recipes/recipes/custom_dataset_transforms_loader.html
:tags: Data-Customization

.. Interpretability

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: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

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: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

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: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

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: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

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: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

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: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

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: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

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.. Page TOC
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.. toctree::
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/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
/recipes/recipes/custom_dataset_transforms_loader
/recipes/recipes/Captum_Recipe
/recipes/recipes/tensorboard_with_pytorch
/recipes/recipes/dynamic_quantization
/recipes/torchscript_inference
/recipes/deployment_with_flask
/recipes/distributed_rpc_profiling