PyTorch

What is PyTorch?

PyTorch is an open-source deep learning framework for building and training neural networks in Python. It began at Meta's AI research group and is now governed by the PyTorch Foundation, part of the Linux Foundation.

Key capabilities of PyTorch

• Dynamic computation graphs: Define models as ordinary Python code that can change per run.
• GPU acceleration: Run tensors and models on GPUs with CUDA and other backends.
• torch.compile: Compile models for faster execution.
• Distributed training: Train across multiple GPUs and machines.
• Autograd: Automatic differentiation of tensor operations.

How PyTorch works

Developers write models in Python using tensors and modules, and PyTorch records operations so gradients can be computed automatically for training. Code runs eagerly for easy debugging, and torch.compile can optimize it. Training can scale across GPUs, and models can be exported for deployment, for example via ONNX.

Who uses PyTorch?

Researchers, ML engineers and companies building deep learning systems use it, and many published papers and open models are released in PyTorch. It requires programming skills and is not a no-code tool.

PyTorch pricing

PyTorch is free and open source, with no paid tiers. Costs come from the compute and cloud services used to run it, which are priced by those providers.

PyTorch alternatives

TensorFlow is Google's deep learning framework, JAX offers composable function transformations on accelerators, and MLflow manages experiment tracking rather than training itself. PyTorch is known for its Python-first style.