Using Distributed TensorFlow with Cloud ML Engine and Cloud Datalab
This lab shows you how to use a distributed configuration of TensorFlow code in Python on Google Cloud Machine Learning Engine to train a convolutional neural network model by using the MNIST dataset. You use TensorBoard to visualize the training process and Google Cloud Datalab to test the predictions.
TensorFlow is Google's open source library for machine learning, developed by researchers and engineers in Google's Machine Intelligence organization, which is part of Research at Google. TensorFlow is designed to run on multiple computers to distribute the training workloads, and Cloud Machine Learning Engine provides a managed service where you can run TensorFlow code in a distributed manner by using service APIs.
The MNIST dataset enables handwritten digit recognition, and is widely used in machine learning as a training set for image recognition.
In this lab, the term node refers to an application container that runs parallel computations during training.
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