KubeCon + CloudNativeCon | Open Source Summit China 2019

The focus of this talk is the deployments of large scale distributed deep learning with Kubernetes. The usage of operators to manage and automate training processes for machine learning are discussed. We share our experiences and compare two open source Kubernetes operators, tf-operator and mpi-operator in this talk. Both operators manage training jobs for TensorFlow but they have different distribution strategies, which lead to different performance results with respect to the utilization ratio among CPU, GPU, and network.

Deep learning tasks are both network and GPU intensive such that a proper optimization for orchestration is very important. There could easily be an imbalance leads to idle compute capacity which is too expensive for GPU nodes (compared with CPUs). We will share our experiences with the hope to provide helpful insight for better economics with machine learning tasks.