PyTorch is an open source machine learning framework that accelerates the path from research prototyping to production deployment. It is widely used for developing and deploying deep learning applications, such as computer vision, natural language processing, reinforcement learning and more.
PyTorch offers the following key features and capabilities:
- Production Ready: Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.
- Distributed Training: Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.
- Robust Ecosystem: A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
- Cloud Support: PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.
Some of the practical applications of PyTorch include:
- Developing state-of-the-art models for image classification, object detection, semantic segmentation, face recognition, etc. using PyTorch’s computer vision library torchvision.
- Building natural language understanding systems for tasks such as machine translation, text summarization, question answering, sentiment analysis, etc. using PyTorch’s natural language processing library torchaudio.
- Creating generative models for text, images, audio and video using PyTorch’s generative modeling library torchtext.
- Implementing reinforcement learning algorithms for games, robotics, control and optimization using PyTorch’s reinforcement learning library torchrl.
- Optimizing and deploying PyTorch models on various hardware platforms and accelerators using PyTorch’s optimization and deployment tools such as ONNX Runtime, DeepSpeed, TorchServe and more.