compilers_index

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Compilers

Explore PyTorch compilers to optimize and deploy models efficiently. Learn about APIs like torch.compile and torch.export that let you enhance model performance and streamline deployment processes. Explore advanced topics such as compiled autograd, dynamic compilation control, as well as third-party backend solutions.

Warning

TorchScript is no longer in active development.

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torch.compile Tutorial

Speed up your models with minimal code changes using torch.compile, the latest PyTorch compiler solution.

Model-Optimization,torch.compile

Compiled Autograd: Capturing a larger backward graph for ``torch.compile``

Learn how to use compiled autograd to capture a larger backward graph.

Model-Optimization,CUDA,torch.compile

Inductor CPU Backend Debugging and Profiling

Learn the usage, debugging and performance profiling for ``torch.compile`` with Inductor CPU backend.

Model-Optimization,torch.compile

Dynamic Compilation Control with ``torch.compiler.set_stance``

Learn how to use torch.compiler.set_stance

Model-Optimization,torch.compile

Demonstration of torch.export flow, common challenges and the solutions to address them

Learn how to export models for popular usecases

Model-Optimization,torch.compile

(beta) Compiling the Optimizer with torch.compile

Speed up the optimizer using torch.compile

Model-Optimization,torch.compile

(beta) Running the compiled optimizer with an LR Scheduler

Speed up training with LRScheduler and torch.compiled optimizer

Model-Optimization,torch.compile

Using User-Defined Triton Kernels with ``torch.compile``

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

Model-Optimization,torch.compile

Compile Time Caching in ``torch.compile``

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

Model-Optimization,torch.compile

Compile Time Caching Configurations

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

Model-Optimization,torch.compile

Reducing torch.compile cold start compilation time with regional compilation

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

Model-Optimization,torch.compile

torch.export AOTInductor Tutorial for Python runtime

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

Basics,torch.export

Deep dive into torch.export

Learn how to use torch.export to export PyTorch models into standardized model representations.

Basics,torch.export

(optional) Exporting a PyTorch model to ONNX using TorchDynamo backend and Running it using ONNX Runtime

Build a image classifier model in PyTorch and convert it to ONNX before deploying it with ONNX Runtime.

Production,ONNX,Backends

Extending the ONNX exporter operator support

Demonstrate end-to-end how to address unsupported operators in ONNX.

Production,ONNX,Backends

Exporting a model with control flow to ONNX

Demonstrate how to handle control flow logic while exporting a PyTorch model to ONNX.

Production,ONNX,Backends

Building a Convolution/Batch Norm fuser in FX

Build a simple FX pass that fuses batch norm into convolution to improve performance during inference.

FX

Building a Simple Performance Profiler with FX

Build a simple FX interpreter to record the runtime of op, module, and function calls and report statistics

FX

Introduction to TorchScript

Introduction to TorchScript, an intermediate representation of a PyTorch model (subclass of nn.Module) that can then be run in a high-performance environment such as C++.

Production,TorchScript

Loading a TorchScript Model in C++

Learn how PyTorch provides to go from an existing Python model to a serialized representation that can be loaded and executed purely from C++, with no dependency on Python.

Production,TorchScript

Distributed Optimizer with TorchScript support

How to enable TorchScript support for Distributed Optimizer.

Distributed-Training,TorchScript

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.

TorchScript

Deploying with Flask

Learn how to use Flask, a lightweight web server, to quickly setup a web API from your trained PyTorch model.

Production,TorchScript