NVIDIA, Facebook team up on AI

NVIDIA and Facebook are working together to advance artificial intelligence with Caffe2, a new AI deep learning framework contributed by Facebook to the open-source community.

Facebook is developing new AI systems to help manage this information so people can better understand the world and more effectively communicate, even as the volume of information increases.

Caffe2 allows developers and researchers to create large-scale distributed training scenarios and build machine learning applications for edge devices.

Increasingly, the processing of lightning-fast AI services requires GPU-accelerated computing, such as that offered by Facebook’s Big Basin servers, as well as highly optimized deep learning software that can leverage the full capability of the accelerated hardware.

“NVIDIA and Facebook are delivering AI acceleration through our work on the Caffe2 deep learning framework,” said Kristin Bryson, PR director for the data center business at NVIDIA.

“Thanks to our joint engineering, we’ve fine-tuned Caffe2 from the ground up to take full advantage of the NVIDIA GPU deep learning platform,” said Bryson.

Caffe2 uses the latest NVIDIA Deep Learning SDK libraries — cuDNN, cuBLAS and NCCL — to deliver high-performance, multi-GPU accelerated training and inference.

Bryson said Caffe2 is designed to be a fast, scalable and portable deep learning framework. It delivers near-linear scaling of deep learning training with 57x throughput acceleration on eight networked Facebook Big Basin AI servers with 64 NVIDIA Tesla P100 GPU accelerators. This means developers can train and iterate AI models faster than ever.

As part of the two companies’ collaboration, the NVIDIA DGX-1 AI supercomputer will be the first AI system to offer Caffe2 within the optimized software stack for deep learning. Together, DGX-1 and Caffe2 deliver high performance and fast training.

“Through NVIDIA’s Deep Learning Institute, we’ve helped more than 10,000 developers worldwide learn to use frameworks to design, train and deploy neural network-powered machine learning for a variety intelligent applications and services,” said Bryson.