Uber upgrades platform with scalable GPU clusters [Video]

Artificial intelligence is already part of catching a ride, as Uber Technologies Inc. continues to upgrade its platform with scalable GPU clusters.

Building upon its history of containerized workloads, Uber has adopted Anyscale Ray to run clusters with greater speed and scale, according to Zhitao Li (pictured), director of engineering, AI and model infrastructure at Uber. 

“Before Ray, we were operating distributed Spark clusters that trie[d] to run machine learning model training on the inside, but it’s very difficult to retrofitted those things to run on the GPU native cluster, managing the life cycle of the workload,” said Zhitao Li, who highlighted Uber’s early adoption of containers. “It’s almost a decade of history of the containers. So, these containers provide the best isolation of the workload and allowed us to utilize the GPUs and run heterogeneous computation cluster in the most maintainable and scalable way. With Ray, we are able to run many clusters. I believe we are doing more than 20,000 model …