NVIDIA Optimized Frameworks
- Preparing To Use Docker Containers
- This guide provides the first-step instructions for preparing to use Docker containers on your DGX system. You must setup your DGX system before you can access the NVIDIA GPU Cloud (NGC) container registry to pull a container.
- Containers For Deep Learning Frameworks User Guide
- This guide provides a detailed overview about containers and step-by-step instructions for pulling and running a container and customizing and extending containers.
Support Matrix
- Frameworks Support Matrix
- This support matrix is for NVIDIA® optimized frameworks. The matrix provides a single view into the supported software and specific versions that come packaged with the frameworks based on the container image.
Optimized Frameworks Release Notes
- CUDA DL Release Notes
- CUDA Deep Learning image extends the CUDA images by adding networking support and additional libraries to accelerate deep learning workloads like cuDNN, cuTensor, NCCL, and HPC-x. These images are provided for use as a base layer upon which to build your own GPU-accelerated application container image. Two flavors of CUDA Deep Learning containers are provided: runtime and devel, where the latter adds compilers and development tools. This document describes the key features, software enhancements and improvements, known issues, and how to run this container.
- TensorRT Release Notes
- The TensorRT container is an easy to use container for TensorRT development. The container allows for the TensorRT samples to be built, modified, and executed. These release notes provide a list of key features, packaged software included in the container, software enhancements and improvements, and known issues. The TensorRT container is released monthly to provide you with the latest NVIDIA deep learning software libraries and GitHub code contributions that have been sent upstream. The libraries and contributions have all been tested, tuned, and optimized.
- JAX Release Notes
- Starting January 2026, the JAX containers are released on a monthly basis. Separately, the GitHub code contributions are being sent upstream regularly. Nightly builds of the containers are available on JAX Toolbox. All libraries and contributions have all been tested, tuned, and optimized for use on NVIDIA hardware.
- PyTorch Release Notes
- These release notes describe the key features, software enhancements and improvements, known issues, and how to run this container. The PyTorch framework enables you to develop deep learning models with flexibility, use Python packages, such as SciPy, NumPy, and so on. The PyTorch framework is convenient and flexible, with examples that cover reinforcement learning, image classification, and machine translation as the more common use cases. The PyTorch container is released monthly to provide you with the latest NVIDIA deep learning software libraries and GitHub code contributions that have been sent upstream. The libraries and contributions have all been tested, tuned, and optimized.
- PyG Release Notes
- NVIDIA PyG containers are built on top of the optimized deep learning framework container Pytorch NGC with the latest stable PyG open source. This document describes the key features, software enhancements and improvements, known issues, and how to run this container for the current release.
- SGLang Release Notes
- These release notes describe the key features, software enhancements, improvements, and known issues for this release of SGLang. SGLang is a high-performance runtime system and programming language designed for Large Language Models (LLMs). The framework enables developers to write complex, structured generation programs with simple Python syntax and seamlessly integrates with a wide array of models from hubs like Hugging Face. Through core innovations like RadixAttention and a dedicated LLM compiler, SGLang is designed to be expressive and exceptionally efficient for demanding, multi-step generation tasks. Common use cases include developing complex agents, implementing chain-of-thought reasoning, and creating sophisticated few-shot prompting strategies. The SGLang container is released monthly to provide you with the latest NVIDIA deep learning software libraries and GitHub code contributions that have been sent upstream. The libraries and contributions have all been tested, tuned, and optimized.
- vLLM Release Notes
- Through core innovations like PagedAttention and continuous batching, vLLM is designed to be powerful and efficient for the most demanding inference workloads. Common use cases include powering generative AI applications, chatbots, and APIs for text generation, summarization, and translation. The vLLM container is released monthly to provide you with the latest NVIDIA deep learning software libraries and GitHub code contributions that have been sent upstream. The libraries and contributions have all been tested, tuned, and optimized.
Installing Frameworks for Jetson
- Installing PyTorch for Jetson Platform
- This guide provides the instructions for installing PyTorch on Jetson Platform. The Jetson Platform includes modules such as Jetson AGX Xavier and Jetson AGX Orin. This guide describes the prerequisites for installing PyTorch on Jetson Platform, the detailed steps for the installation and verification, and best practices for optimizing the performance of the Jetson Platform.
- PyTorch for Jetson Platform Release Notes
- This document contains the release notes for installing PyTorch for Jetson Platform. The Jetson Platform includes modules such as Jetson AGX Xavier and Jetson AGX Orin. These release notes describe the key features, software enhancements, and known issues when installing PyTorch for Jetson Platform.