Deep Learning in Python

PyTorch is the default deep learning framework for most research and a large share of production work: eager execution, ordinary Python debugging and a wide ecosystem built on top of it. PyTorch Lightning removes the training loop boilerplate (device placement, checkpointing, logging, multi-GPU training) while your models stay plain PyTorch modules. TensorFlow, from Google, remains common in established production systems and has its own serving and deployment tooling.

JAX takes a functional approach: NumPy-style arrays with composable transformations for automatic differentiation, JIT compilation through XLA and automatic vectorization. It suits research that needs custom numerics at scale, especially on TPUs. Keras 3 is a high-level API that runs on JAX, TensorFlow or PyTorch, so the same model code is not tied to one backend.

For reinforcement learning, Gymnasium (the maintained successor to OpenAI Gym) defines the standard environment interface, and Stable-Baselines3 provides tested PyTorch implementations of algorithms such as PPO, SAC and DQN to train agents against those environments. Because both follow the same interface, you can swap environments or algorithms without rewriting the training loop, and custom environments only need to implement the reset and step methods. For new deep learning work outside reinforcement learning, PyTorch with or without Lightning is the safest default, with JAX worth considering when you need its transformations or run on TPUs.