PyTorch
Definition
PyTorch ist ein beliebtes deep learning framework mit einem imperativen, pythonischen Stil. Es ist weit verbreitet in Forschung und Industry and is the base for many libraries (Hugging Face, torchvision, etc.).
It competes with TensorFlow; PyTorch wird oft preferred for research and rapid iteration due to Eager-Ausführung and clear debugging. Used for LLMs, vision, NLP, and RL; production deployment uses TorchScript, ONNX, or dedicated runtimes.
Funktionsweise
Man definiert Modelle als Python-Klassen (nn.Module); der Forward-Pass läuft sofort (Eager-Modus), und Autograd records operations for backprop. Optimizers (z. B. 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 (z. B. Hugging Face Accelerate). The same code can run on CPU, GPU, or multiple GPUs mit minimalem changes.
Anwendungsfälle
PyTorch is a natural fit for research, prototyping, and production wenn Sie want flexibility and a Python-first workflow.
- Research and prototyping (z. B. new architectures, experiments)
- Training and Feinabstimmung models (vision, NLP, RL)
- Production deployment via TorchScript, ONNX, or mobile