Help Center/ ModelArts/ FAQs/ Training Jobs/ Functional Consulting/ In a Multi-Node Training, the TensorFlow PS Node Functioning as a Server Will Be Continuously Suspended. How Does ModelArts Determine Whether the Training Is Complete? Which Node Is a Worker?
Updated on 2024-06-11 GMT+08:00

In a Multi-Node Training, the TensorFlow PS Node Functioning as a Server Will Be Continuously Suspended. How Does ModelArts Determine Whether the Training Is Complete? Which Node Is a Worker?

In a TensorFlow-powered distributed training, the PS task and worker task are started. The worker task is a key task. ModelArts will use a process exit code of the worker task to determine whether the training job is complete.

A task name will be used to determine which node is a worker. A Volcano job is issued for training, which contains a PS task and a worker task. The startup commands of the two tasks are different. The hyperparameter task_name will be automatically generated, which is ps for the PS task and worker for the worker task.