PyTorch

PyTorch is a deep learning framework that provides flexible tools for building, training, and deploying neural networks. Developed by Meta AI Research, it uses dynamic computational graphs that are constructed on the fly during execution, making it natural to debug with standard Python tools and to write models with conditional logic that would be awkward in static graph frameworks.

The framework provides tensor operations with automatic differentiation, GPU acceleration through CUDA, and a rich ecosystem of libraries for computer vision (torchvision), natural language processing (torchtext), and audio processing (torchaudio). TorchServe handles model serving in production, and ONNX export enables running PyTorch models in other runtimes including web browsers through ONNX.js.

For web developers, PyTorch matters because many AI features integrated into web applications are built with it. Understanding how PyTorch models work helps developers build effective API interfaces for model inference, manage model versioning and deployment, and set appropriate expectations about latency and resource requirements. As AI capabilities become standard features in web applications, the boundary between web development and machine learning engineering continues to overlap.