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Train a TensorFlow model in the cloud

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In this tutorial, we will train a TensorFlow model using the MNIST dataset on an Azure Deep Learning virtual machine.

The MNIST database has a training set of 60,000 examples, and a test set of 10,000 examples of handwritten digits.

Prerequisites

Before you begin, ensure you have the following installed and configured:

Setup Azure Deep Learning Virtual Machine

Note

Set OS type to Linux.

Instructions for setting up Deep Learning Virtual Machine can be found here.

Remove comment in parens

echo -e ". /etc/profile\n$(cat ~/.bashrc)" > ~/.bashrc

Download sample code

Download this GitHub repository containing samples for getting started with deep learning on TensorFlow, CNTK, Theano, and more.

Open project

  • Launch Visual Studio and select File > Open > Project/Solution.

  • Select the Tensorflow Examples folder from the samples repository downloaded and open the TensorflowExamples.sln file.

    Open project

    Open solution

Add Azure Remote VM

In Server Explorer, right click the Remote Machines node under the AI Tools node and select "Add…". Enter the Remote Machine display name, IP host, SSH port, user name, and password/key file.

Add a new remote machine

Submit job to Azure VM

Right-click on MNIST project in Solution Explorer and select Submit Job.

Job submission to a remote machine

In the submission window:

  • In the list of Cluster to use, select the remote machine (with "rm:" prefix) to submit the job to.

  • Enter a Job name.

  • Click Submit.

Check status of job

To see status and details of jobs: expand the virtual machine you submitted the job to in the Server Explorer. Double-click on Jobs.

Job browser

Clean up resources

Stop the VM if you plan on using it in the near future. If you are finished with this tutorial, run the following command to clean up your resources:

az group delete --name myResourceGroup