PyTorch provides a tensor computation library with GPU acceleration and a tape-based automatic differentiation system for dynamic neural network construction. Its core components include torch for tensor operations, torch.autograd for reverse-mode auto-differentiation enabling arbitrary network modifications without static graph constraints, torch.nn for flexible neural network modules, torch.multiprocessing with shared tensor memory across processes, and torch.utils for data loading utilities. The framework integrates Intel MKL, NVIDIA cuDNN, and NCCL for optimized CPU and GPU backends, and employs custom GPU memory allocators to maximize model capacity. PyTorch supports CUDA (requiring cuDNN v9+), AMD ROCm 4.0+ on Linux, and Intel XPU on Linux and Windows, with build-time toggles via USE_CUDA, USE_ROCM, and USE_XPU environment variables. Building from source requires Python 3.10+, a C++20-compatible compiler (GCC 11.3+ or Clang), Visual Studio BuildTools on Windows, 10 GB disk space, and 30–60 minutes for initial compilation. Pre-built binaries are distributed via Conda, pip wheels (including Jetson platforms with JetPack 4.2+), and Docker images (CUDA 12.6, cuDNN v9, Docker v23+). The project uses a BSD-style license and maintains CI health signals at hud.pytorch.org.