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ElasticDL: Kubernetes-Native Distributed Deep Learning with Elastic Scheduling


In addition to online services, users have been running AI jobs on Kubernetes. Related projects add Kubernetes operators to start distributed deep learning programs calling TensorFlow or PyTorch. In these solutions, Kubernetes plays the role to launch pods and restart preempted ones. But such retrials often fail due to the same reason - the lack of resources. Jobs would fail if not maintaining the constant number of worker pods. Maintaining the constant number, it becomes gang scheduling. Either case leads to low utilization.

ElasticDL changes the paradox by realizing elastic scheduling - making deep learning jobs tolerable with varying numbers of workers. It introduces a master pod per job that coordinates both the learning process and resource scheduling, to replace the Kubernetes operator per cluster. It makes full use of residual resources and improves the utilization significantly.

Benefits to the Ecosystem

ElasticDL boosts the cluster utilization up to 90%, on on-premise clusters at Ant Financial and on Google Cloud, as it makes full use of residual resources to run deep learning jobs with elastic scheduling. Moreover, it enables the jobs to run in lower priority than online services on the same cluster. ElasticDL senses and uses resource left by online services.

The master is important to elastic scheduling and takes three roles.

  1. It dynamically partitions the data so to decouple the number of partitions and workers.
  2. It works with Kubernetes to watch the cluster utilization and a good chance to restart failed workers.
  3. It starts parameter servers when training large models using the asynchronous SGD algorithm, and cooperate workers to implement a Kubernetes-native fault-tolerable AllReduce operation for the synchronous SGD.

Deep learning researchers like ElasticDL as it reduces the pending time of each job. As Deep learning jobs depend on many parameters, users are eager to see the status of the first few iterations to ensure the configuration is mathematically correct. Making full use of the residual resource, ElasticDL runs as many concurrent experiments as possible and shortens the total time to run a batch of training jobs.

ElasticDL provides an easy-to-use interface. Users define models using TensorFlow 2.x API just like filling the map and reduce functions required by the MapReduce framework, without the need to consider anything about distributed programming.

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