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Building Machine Learning Projects with TensorFlow by Rodolfo Bonnin

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Distributed TensorFlow

Distributed TensorFlow is a complementary technology, which aims to easily and efficiently create clusters of computing nodes, and to distribute the jobs between nodes in a seamless way.

It is the standard way to create distributed computing environments, and to execute the training and running of models at a massive scale, so it's very important to be able to do the main task found in production, high volume data setups.

Technology components

In this section, we will describe all the components on a distributed TensorFlow computing setup, from the most fine-grained task elements, to the whole cluster description.

Jobs

Jobs define a group of homogeneous tasks, normally aimed to the same subset of the problem-solving area.

Examples ...

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