Flash-Decoding for long-context inference
Micro-benchmark of the multi-head attention, run-time in us. Flash-Decoding achieves almost constant run-time as the sequence length scales to up to 64k. The up to 8x speedup end-to-end measured earlier is made possible because the attention itself is up to 50x faster than FlashAttention. Up until sequence length 32k, the attention time is roughly constant, because Flash-Decoding manages to fully
Introducing nvFuser, a deep learning compiler for PyTorch
by Christian Sarofeen, Piotr Bialecki, Jie Jiang, Kevin Stephano, Masaki Kozuki, Neal Vaidya, Stas Bekman nvFuser is a Deep Learning Compiler for NVIDIA GPUs that automatically just-in-time compiles fast and flexible kernels to reliably accelerate users’ networks. It provides significant speedups for deep learning networks running on Volta and later CUDA accelerators by generating fast custom “fus
PyTorch Profiler With TensorBoard — PyTorch Tutorials 2.3.0+cu121 documentation
PyTorch Recipes See All Recipes See All Prototype Recipes Introduction to PyTorch Learn the Basics Quickstart Tensors Datasets & DataLoaders Transforms Build the Neural Network Automatic Differentiation with torch.autograd Optimizing Model Parameters Save and Load the Model Introduction to PyTorch on YouTube Introduction to PyTorch - YouTube Series Introduction to PyTorch Introduction to PyTorch T
Learning PyTorch with Examples — PyTorch Tutorials 1.0.0.dev20181001 documentation
Learn Get Started Run PyTorch locally or get started quickly with one of the supported cloud platforms Tutorials Whats new in PyTorch tutorials Learn the Basics Familiarize yourself with PyTorch concepts and modules PyTorch Recipes Bite-size, ready-to-deploy PyTorch code examples Intro to PyTorch - YouTube Series Master PyTorch basics with our engaging YouTube tutorial series
TorchServe — PyTorch/Serve master documentation
TorchServe¶ TorchServe is a performant, flexible and easy to use tool for serving PyTorch models in production. What’s going on in TorchServe? High performance Llama 2 deployments with AWS Inferentia2 using TorchServe Naver Case Study: Transition From High-Cost GPUs to Intel CPUs and oneAPI powered Software with performance Run multiple generative AI models on GPU using Amazon SageMaker multi-mode
For Researchers
PyTorch Hub For Researchers Explore and extend models from the latest cutting edge research.
Deep Learning with PyTorch | PyTorch
Deep Learning with PyTorch Download a free copy of the full book and learn how to get started with AI / ML development using PyTorch Deep Learning with PyTorch provides a detailed, hands-on introduction to building and training neural networks with PyTorch, a popular open source machine learning framework. This full book includes: Introduction to deep learning and the PyTorch library Pre-trained n
torchvision 0.3: segmentation, detection models, new datasets and more..
by Francisco Massa PyTorch domain libraries like torchvision provide convenient access to common datasets and models that can be used to quickly create a state-of-the-art baseline. Moreover, they also provide common abstractions to reduce boilerplate code that users might have to otherwise repeatedly write. The torchvision 0.3 release brings several new features including models for semantic segme
What is torch.nn really? — PyTorch Tutorials 2.1.0+cu121 documentation
What is torch.nn really?¶ Authors: Jeremy Howard, fast.ai. Thanks to Rachel Thomas and Francisco Ingham. We recommend running this tutorial as a notebook, not a script. To download the notebook (.ipynb) file, click the link at the top of the page. PyTorch provides the elegantly designed modules and classes torch.nn , torch.optim , Dataset , and DataLoader to help you create and train neural networ
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Datasets — Torchvision main documentation
Jacobians, Hessians, hvp, vhp, and more: composing functorch transforms — functorch nightly documentation
Understanding GPU Memory 1: Visualizing All Allocations over Time
PyTorch on XLA Devices — PyTorch/XLA master documentation
High performance Llama 2 deployments with AWS Inferentia2 using TorchServe
Automatic Mixed Precision package - torch.amp — PyTorch 2.2 documentation
PyTorch Profiler — PyTorch Tutorials 2.3.0+cu121 documentation