Distributed training allows computational resources to be used on the whole cluster and thus speed up training of deep learning models. TensorFlow is a machine learning framework that natively supports distributed neural network training, inference and other computations.Using this ability, we can calculate gradients on the nodes the data are stored on, reduce them and then finally update model parameters.In case of TensorFlow on Apache Ignite does in a server in cluster we must run a tensorflow worker for doing work on its data?
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