What is a deep learning framework?
A deep learning framework is a software library that provides the building blocks for defining, training, and running neural networks. It handles tensor arithmetic, automatic differentiation for backpropagation, optimizers, standard layers, data loading, and execution on GPUs and other accelerators. A developer describes the network and the training loop in a few dozen lines, and the framework does the rest. PyTorch and TensorFlow are the two best known, and JAX is a third used mostly in research.
The frameworks are open source under permissive licenses, and almost everything above them depends on them: model hubs distribute weights in framework formats, inference servers for language models are built on PyTorch, and fine-tuning tools assume one framework or the other.
Why a deep learning framework matters
For a business, the framework choice shapes three things: the pool of people who can maintain the work, which hardware is supported and how well, and the deployment path, from exporting to ONNX for an edge device to serving through a dedicated inference server. The operational pain is mostly about versions: the framework, the GPU driver, and the GPU compute libraries must match, and containers are the standard way to keep that combination fixed.
What to watch for
- Pin the framework, driver, and compute library versions together in a container image.
- Pick the framework your inference server and target hardware support rather than the one a contractor prefers.
- Framework lock-in matters less than it once did, because models can be exported to ONNX for serving.