PyTorch

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed. Our trunk health (Continuous Integration signals) can be found at hud.pytorch.org. More About PyTorch A GPU-Ready Tensor Library Dynamic Neural Networks: Tape-Based Autograd Python First Imperative Experiences Fast and Lean Extensions Without Pain Installation Binaries NVIDIA Jetson Platforms From Source Prerequisites NVIDIA CUDA Support AMD ROCm Support Intel GPU Support Get the PyTorch Source Install Dependencies Install PyTorch Adjust Build Options (Optional) Docker Image Using pre-built images Building the image yourself Building the Documentation Troubleshooting CI Errors Building a PDF Previous Versions Getting Started Resources Communication Releases and Contributing The Team License torch A Tensor library like NumPy, with strong GPU support torch.autograd A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch torch.jit A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code torch.nn A neural networks library deeply integrated with autograd designed for maximum flexibility torch.multiprocessing Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training torch.utils DataLoader and other utility functions for convenience PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount. We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, reductions. And they are fast! PyTorch has a unique way of building neural networks: using and replaying a tape recorder. Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world. One has to build a neural network and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch. PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use NumPy / SciPy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate. PyTorch is designed to be intuitive, linear in thought, and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger or receive error messages and stack traces, understanding them is straightforward. The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines. PyTorch has minimal framework overhead. We integrate acceleration libraries such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed. At the core, its CPU and GPU Tensor and neural network backends are mature and have been tested for years. Hence, PyTorch is quite fast — whether you run small or large neural networks. The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before. Writing new neural network modules, or interfacing with PyTorch's Tensor API, was designed to be straightforward and with minimal abstractions. You can write new neural network layers in Python using the torch API or your favorite NumPy-based libraries such as SciPy. They require JetPack 4.2 and above, and @dusty-nv and @ptrblck are maintaining them. Prerequisites If you are installing from source, you will need: Python 3.10 o