PyTorch Recipes¶
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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Run in Google Colab
Colab
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Notebook
View on GitHub
GitHub
Loading data in PyTorch
Learn how to use PyTorch packages to prepare and load common datasets for your model.
Defining a Neural Network
Learn how to use PyTorch's torch.nn package to create and define a neural network the MNIST dataset.
What is a state_dict in PyTorch
Learn how state_dict objects, Python dictionaries, are used in saving or loading models from PyTorch.
Saving and loading models for inference in PyTorch
Learn about the two approaches for saving and loading models for inference in PyTorch - via the state_dict and via the entire model.
Saving and loading a general checkpoint in PyTorch
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.
Saving and loading multiple models in one file using PyTorch
In this recipe, learn how saving and loading multiple models can be helpful for reusing models that you have previously trained.
Warmstarting model using parameters from a different model in PyTorch
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.
Saving and loading models across devices in PyTorch
Learn how saving and loading models across devices (CPUs and GPUs) is relatively straightforward using PyTorch.
Zeroing out gradients in PyTorch
Learn when you should zero out graidents and how doing so can help increase the accuracy of your model.
Custom Datasets, Transforms & Dataloaders
Learn how to leverage the PyTorch dataset API to easily create a custom dataset and custom dataloader.
Model Interpretability using Captum
Learn how to use Captum attribute the predictions of an image classifier to their corresponding image features and visualize the attribution results.
How to use TensorBoard with PyTorch
Learn basic usage of TensorBoard with PyTorch, and how to visualize data in TensorBoard UI
Dynamic Quantization
Apply dynamic quantization to a simple LSTM model.
TorchScript for Deployment
Learn how to export your trained model in TorchScript format and how to load your TorchScript model in C++ and do inference.
Deploying with Flask
Learn how to use Flask, a lightweight web server, to quickly setup a web API from your trained PyTorch model.
