Scheduling is a key component of making AI applications cost efficient at scale. This talk discusses the key challenges of building a scheduling system for AI applications: diversity of application requirements and performance. We will walk through two Ray AI applications: model serving and data preprocessing for distributed training and show how Ray scheduling makes them run faster and cheaper. In this talk, we will cover several Ray scheduling features including placement groups, graceful node draining and label based scheduling.
• Audiences will understand different ray scheduling features
• How to use them for their requirements
• How to make their applications run faster and cheaper.
Jiajun Yao is a software engineer at Anyscale and a committer for the Ray project. He is interested in making distributed computing easily accessible to everyone. Before joining Anyscale, Jiajun was a software engineer at LinkedIn building the graph database.
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