- PyTorch
PyTorch + vLLM = ♥️ Key takeaways: PyTorch and vLLM are both critical to the AI ecosystem and are increasingly being used together for cutting-edge generative AI applications, including inference, post-training, and agentic systems at…
- Get Started - PyTorch
To install the latest PyTorch code, you will need to build PyTorch from source Prerequisites Install pip Install CUDA, if your machine has a CUDA-enabled GPU If you want to build on Windows, Visual Studio with MSVC toolset, and NVTX are also needed The exact requirements of those dependencies could be found out here
- PyTorch 2. 7 Release
We are excited to announce the release of PyTorch® 2 7 (release notes)! This release features: support for the NVIDIA Blackwell GPU architecture and pre-built wheels for CUDA 12 8 across Linux x86 and arm64 architectures
- PyTorch – PyTorch
PyTorch is an open source machine learning framework that accelerates the path from research prototyping to production deployment Built to offer maximum flexibility and speed, PyTorch supports dynamic computation graphs, enabling researchers and developers to iterate quickly and intuitively
- End-to-end Machine Learning Framework – PyTorch
PyTorch enables fast, flexible experimentation and efficient production through a user-friendly front-end, distributed training, and ecosystem of tools and libraries
- PyTorch 2. 6 Release Blog
Application developers and researchers seeking to fine-tune, inference and develop with PyTorch models on Intel® Core™ Ultra AI PCs and Intel® Arc™ discrete graphics will now be able to directly install PyTorch with binary releases for Windows, Linux and Windows Subsystem for Linux 2
- PyTorch 2. x
Learn about PyTorch 2 x: faster performance, dynamic shapes, distributed training, and torch compile
- Learn the Basics — PyTorch Tutorials 2. 7. 0+cu126 documentation
Most machine learning workflows involve working with data, creating models, optimizing model parameters, and saving the trained models This tutorial introduces you to a complete ML workflow implemented in PyTorch, with links to learn more about each of these concepts
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