PyTorch Deep Learning Framework

PyTorch is a free and open-source machine learning framework developed by Meta AI (formerly Facebook AI Research). Originally created by Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan in 2016, PyTorch has become the most popular deep learning framework in academia and is widely used in industry for NLP and computer vision. Key features: dynamic computation graphs: PyTorch uses dynamic computation graphs (define-by-run), where the graph is built on-the-fly as operations are executed. This enables intuitive debugging with standard Python tools, dynamic model architectures, and a more Pythonic API. Tensors: the core data structure is torch.Tensor, a multidimensional array with GPU acceleration, automatic gradient tracking, and CPU/GPU transfer. Autograd: provides automatic differentiation for all tensor operations. By calling tensor.requires_grad_(True), PyTorch tracks operations and computes gradients via tensor.backward(). nn.Module: the base class for neural network models. Modules can contain submodules, parameters, and buffers, allowing complex architectures from simple components. Optim: optimization algorithms including SGD (with momentum), Adam, AdamW, RMSprop, LBFGS, and learning rate schedulers. Data loading: Dataset and DataLoader for efficient data loading with multi-processing and batching. Distributed training: DistributedDataParallel (DDP) for multi-GPU, Fully Sharded Data Parallel (FSDP) for large models. TorchScript: serialize models for production. TorchServe: production serving. TorchVision/TorchText/TorchAudio: domain-specific libraries. C++ core (ATen) with Python frontend. BSD-style license.