PyTorch
deep-learning framework
Deep learning framework preferred in research
Supported languages
Concepts
tensorsautogradnn.ModuleDataLoaderoptimizers
Deployment Options:
torchserve onnx triton
Pros and Cons
Ventajas
- + Dynamic graphs (easy debugging)
- + Very Pythonic API
- + Dominant in research
- + Very active community
- + Excellent documentation
- + Dynamic computational graphs (eager execution)
- + Intuitive debugging like native Python
- + Preferred in research and academia
- + Excellent GPU/CUDA support
- + Rich ecosystem (torchvision, torchaudio)
Desventajas
- - Deployment more complex than TF
- - TensorBoard requires configuration
- - Consumes more memory
- - TensorBoard requires config
- - Fewer production tools
- - More complex deployment than TensorFlow
- - Higher memory consumption than alternatives
Casos de Uso
- Deep learning research
- Computer vision
- NLP and LLMs
- Generative AI
- Natural language processing
- Generative networks (GANs)
- Reinforcement learning