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PyTorch Distributed Training Scalable distributed training and performance optimization in research and production is enabled by the torch distributed backend
Get Started - PyTorch CUDA 13 0 ROCm 6 4 CPU pip3 install torch torchvision --index-url https: download pytorch org whl cu126
PyTorch documentation — PyTorch 2. 9 documentation Extending PyTorch Extending torch func with autograd Function Frequently Asked Questions Getting Started on Intel GPU Gradcheck mechanics HIP (ROCm) semantics Features for large-scale deployments LibTorch Stable ABI MKLDNN backend Bfloat16 (BF16) on MKLDNN backend Modules MPS backend Multiprocessing best practices Numerical accuracy Out Notes
PyTorch 2. 7 Release Enable torch compile on Windows 11 for Intel GPUs, delivering the performance advantages over eager mode as on Linux Optimize the performance of PyTorch 2 Export Post Training Quantization (PT2E) on Intel GPU to provide a full graph mode quantization pipelines with enhanced computational efficiency
Previous PyTorch Versions OSX macOS is currently not supported in LTS Linux and Windows # CUDA 10 2 pip3 install torch==1 8 2 torchvision==0 9 2 torchaudio==0 8 2 --extra-index-url https: download pytorch org whl lts 1 8 cu102 # CUDA 11 1 pip3 install torch==1 8 2 torchvision==0 9 2 torchaudio==0 8 2 --extra-index-url https: download pytorch org whl lts 1 8 cu111
torch — PyTorch 2. 9 documentation The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors Additionally, it provides many utilities for efficient serialization of Tensors and arbitrary types, and other useful utilities
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 Its Pythonic design and deep integration with native Python tools make it an accessible and powerful
PyTorch 2. x Learn about PyTorch 2 x: faster performance, dynamic shapes, distributed training, and torch compile
End-to-end Machine Learning Framework – PyTorch ## Save your model torch jit script (model) save ("my_mobile_model pt") ## iOS prebuilt binary pod ‘LibTorch’ ## Android prebuilt binary implementation 'org pytorch:pytorch_android:1 3 0' ## Run your model (Android example) Tensor input = Tensor fromBlob (data, new long[]{1, data length});