Dean Wampler - Ray and how it enables easier DevOps
Speaker Bio -
Dean Wample is the Head of Developer Relations at Anyscale.
Talk Abstract -
Ray was developed at U. C. Berkeley’s RISELab in response to the performance challenges and compute requirements for reinforcement learning, hyperparameter tuning, model serving, and other scenarios requiring large-scale computation using clusters. Ray is a Python library for distributing Python tasks across a cluster with an easy to use API. It also provides facilities for distributed state management.
In this talk I’ll discuss several ML APIs implemented with Ray, including RLlib for reinforcement learning, Tune for hyperparameter tuning, and a few others. I’ll explain the performance and scalability problems they solve and how Ray facilitates these solutions.
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