PyTorch
Définition
PyTorch is a popular deep learning framework with an imperative, Pythonic style. C'est widely used in research and industry and is the base for many libraries (Hugging Face, torchvision, etc.).
It competes with TensorFlow; PyTorch is often preferred for research and rapid iteration due to eager execution and clear debugging. Used for LLMs, vision, NLP, and RL; production deployment uses TorchScript, ONNX, or dedicated runtimes.
Comment ça fonctionne
On définit les modèles comme des classes Python (nn.Module) ; le passage forward s'exécute immédiatement (mode eager), et autograd records operations for backprop. Optimizers (par ex. Adam) update parameters from computed gradients. Training loops are explicit (for epoch, for batch, loss.backward(), optimizer.step()), which makes experimentation and debugging straightforward. For deployment, export to TorchScript (Python-free), ONNX (cross-framework), or PyTorch Mobile. Distributed training uses torch.distributed or higher-level APIs (par ex. Hugging Face Accelerate). The same code can run on CPU, GPU, or multiple GPUs with minimal changes.
Cas d'utilisation
PyTorch is a natural fit for research, prototyping, and production when you want flexibility and a Python-first workflow.
- Research and prototyping (par ex. new architectures, experiments)
- Training and fine-tuning models (vision, NLP, RL)
- Production deployment via TorchScript, ONNX, or mobile