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Using Distributed TensorFlow with Cloud ML Engine and Cloud Datalab

90m access · 60m completion
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7 Credits

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01:30:00

Using Distributed TensorFlow with Cloud ML Engine and Cloud Datalab

GSP140

Google Cloud Self-Paced Labs

Overview

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 MNIST—a dataset that is widely used in machine learning training to recognize handwritten digits. You will be using TensorBoard to visualize the training process and Google Cloud Datalab to test 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.

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Score

—/25

Creating a Cloud Storage Bucket for MNIST Files

Run Step

/ 5

Submit a training job to Cloud ML Engine

Run Step

/ 5

Deploy the model and set the default version.

Run Step

/ 5

Create a Cloud Datalab

Run Step

/ 5

Download notebook into the directory 'content/datalab'

Run Step

/ 5

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