Learn how to use PyTorch's torch.nn package to create and define a neural network for the MNIST dataset.

Basics

Learn how state_dict objects and Python dictionaries are used in saving or loading models from PyTorch.

Basics

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.

Basics

Learn when you should zero out gradients and how doing so can help increase the accuracy of your model.

Basics

Learn how to use PyTorch's benchmark module to measure and compare the performance of your code

Basics

Learn how to measure snippet run times and collect instructions.

Basics

Learn how to use PyTorch's profiler to measure operators time and memory consumption

Basics

Learn how to use PyTorch's profiler with Instrumentation and Tracing Technology API (ITT API) to visualize operators labeling in Intel® VTune™ Profiler GUI

Basics

Learn how to use torch.compiler.set_stance

Compiler

Learn how to use the meta device to reason about shapes in your model.

Basics

Learn tips for loading an nn.Module from a checkpoint.

Basics

Learn how to use the torch logging APIs to observe the compilation process.

Basics

New extension points in nn.Module.

Basics

Learn an end-to-end example of how to use AOTInductor for python runtime.

Basics

Learn how to export models for popular usecases

Compiler,TorchCompile

Learn how to use Captum attribute the predictions of an image classifier to their corresponding image features and visualize the attribution results.

Interpretability,Captum

Learn basic usage of TensorBoard with PyTorch, and how to visualize data in TensorBoard UI

Visualization,TensorBoard

Inspect dispatched ops, tensor hashes, and module boundaries to debug eager and ``torch.compile`` runs.

Interpretability,Compiler

Use torch.cuda.amp to reduce runtime and save memory on NVIDIA GPUs.

Model-Optimization

Tips for achieving optimal performance.

Model-Optimization

How to use run_cpu script for optimal runtime configurations on Intel® Xeon CPUs.

Model-Optimization

Override torch operators with Torch Function modes and torch.compile

Model-Optimization

Speed up the optimizer using torch.compile

Model-Optimization

Speed up training with LRScheduler and torch.compiled optimizer

Model-Optimization

Horizontally fuse pointwise ops with torch.compile

Model-Optimization

Learn how to use user-defined kernels with ``torch.compile``

Model-Optimization

Learn how to use compile time caching in ``torch.compile``

Model-Optimization

Learn how to configure compile time caching in ``torch.compile``

Model-Optimization

Learn how to use regional compilation to control cold start compile time

Model-Optimization

Ease-of-use quantization for PyTorch with Intel® Neural Compressor.

Quantization,Model-Optimization

Learn how to use DeviceMesh

Distributed-Training

How to use ZeroRedundancyOptimizer to reduce memory consumption.

Distributed-Training

How to use RPC with direct GPU-to-GPU communication.

Distributed-Training

Learn how to checkpoint distributed models with Distributed Checkpoint package.

Distributed-Training

Learn how to checkpoint distributed models with Distributed Checkpoint package.

Distributed-Training

Learn how to use CommDebugMode for DTensors

Distributed-Training

Learn how to use regional compilation to control AoT cold start compile time

Model-Optimization