PyTorch- Facebook’s Default AI Framework
By MYBRANDBOOK
Facebook recently said it would migrate all its AI systems to PyTorch. Facebook’s AI models currently perform trillions of inference operations every day for the billions of people that use its technology. Its AI tools and frameworks help fast track research work at Facebook, educational institutions and businesses globally.
Facebook has been using two distinct but synergistic frameworks for deep learning: PyTorch and Caffe2. PyTorch is optimised for research, while Caffe2 is optimised for production. Caffe2 is Facebook’s in-house production framework for training and deploying large-scale machine learning models. Facebook said adopting PyTorch as Facebook’s default AI framework ensures that all the experiences across its technologies will run optimally at scale.
Lin Qiao, Engineering Director at Facebook AI, said, “Over a year into the migration to PyTorch, there are more than 1.7K inference models in full production, and 93 percent of our new training models are on PyTorch. PyTorch not only makes our engineering and research work more efficient, collaborative and effective, but also allows us to share our work and learn from the advances made by thousands of PyTorch developers around the world.”
Facebook is looking to create a smoother end-to-end developer experience for its engineers and developers and accelerate its reach-to-production pipeline by using a single platform.
Advantages of migrating to PyTorch
· ML/AI models are now easier to build, program, test and debug
· Research and production environments are brought closer than ever
· Deployment on-device (PyTorch Mobile) is accelerating. PyTorch Mobile currently runs on devices like the Oculus Quest and Portal, as well as on desktops, and the Android and iOS mobile apps for Facebook, Instagram, and Messenger
· On-device AI will play a crucial role with emerging hardware technologies such as wearable AR
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