Getting started

In this class, we will use Jupyter notebooks for all the assignments. Jupyter notebooks provide a web browser-based programming enviroment where you can execude code in blocks, display the output figure, and annotate in Markdown in situ. With these properties, it’s been widely used in the scientific community to make research project reproducible. You can work on your assignment either locally or on a virtual machine through Google Cloud. The following instructions will give you a quick-start guide for both ways.

Working from Google Cloud

Deep learning tends to require extremely large networks and millions if not billions of training points, so for this class we will be using the Google Cloud Platform to run and store our models (which can then be transferred locally). You will each be provided with a coupon for $50, which roughly translates to 5 hours of compute time/week. You will use the same account for the entire semester - including the final project - so don’t spend it all in one place!

Set up a Google Cloud project

Redeem your Google Cloud education grant

You can do this either by clicking on “Redeem now” in the email coupon, or by visiting this link and entering the code from the email. Click “Accept and continue” to accept the terms. Your account should now have a $50 credit.

Create a new project

  • Go to Google Cloud Resource Manager (you must be signed in on Google)
  • Enter a project name
  • Select “Computational Systems Biology: Deep Learning” as the billing account, if it is not already selected
    • If you do not see a field to select a billing account, continue to the next step, and make sure to follow the instructions under “Verify billing information”
  • Select “No organization” as the location (this should be the default)
  • Click “Create”
  • The project will be given a unique project id e.g. affable-ring-631231. Remember this id for a later step.

More information for creating, updating, or deleting a Cloud Platform project can be found here.

Verify billing information

You can proceed directly to the next step if you have already selected “Computational Systems Biology: Deep Learning” as the billing account, or if this is your first time using Google Cloud (in which case billing should automatically be set up to use the credits you redeemed earlier). Otherwise, follow the instructions here to verify that billing is set up correctly (i.e. to use the $50 credit, rather than another source, like your own credit card).

Enable the Google Compute Engine and Cloud Source Repositories APIs for the project

  • Go to the enable APIs webpage
  • Select the project you just created
  • Click “Continue”
  • Wait for the APIs to be enabled (it may take awhile). The page should change, and you should hit “Continue” again.

Install and initialize the Cloud SDK

The Cloud SDK is a set of command line tools that allow you to manage resources and applications hosted on Google Cloud Platform. The following is an abbreviated version of the installation instructions available here, which should be sufficient for most users.


  • Download the Cloud SDK installer here
  • Launch the installer and follow the prompts
  • After installation has completed, accept the following options
    • Start Cloud SDK Shell
    • Run gcloud init
  • A terminal should pop up. Follow the prompts to complete the installation:
    • Log in when prompted (the login window will pop up in your browser, log in using your Google account)
    • Pick the cloud project to use, using the unique id assigned to the project you just created (see the Create a new project section)
    • Set the default “Compute Region and Zone” to “us-east1-d”

MacOS and Linux

  • Download the Cloud SDK for MacOS google-cloud-sdk-277.0.0-darwin-x86_64.tar.gz or for Linux google-cloud-sdk-277.0.0-linux-x86_64.tar.gz
  • Extract the file to your preferred installation location. There should now be a new folder named “google-cloud-sdk”
  • Add the Cloud SDK to your path
    • Open a terminal and navigate to the directory containing the new “google-cloud-sdk” directory
    • Run the following command and follow the on-screen instructions. Hitting enter for all prompts should generally be sufficient.
  • Initialize the SDK
    • Open a new terminal and run the command
        gcloud init
    • Log in when prompted (the login window will pop up in your browser, log in using your Google account)
    • Pick the cloud project to use, using the unique id assigned to the project you just created (see the Create a new project section)
    • Set the default “Compute Region and Zone” to “us-east1-d”

Set up a Cloud Datalab

We will make heavy use of Jupyter notebooks in this class for the sake of presentation and reproducibility of results. The easiest way to do this in the Google Cloud Platform is by setting up a VM instance to run Cloud Datalab Notebooks.

Install datalab with gcloud

Run the following, saying yes to all prompts

gcloud components update
gcloud components install datalab

Create a datalab instance

Create a Datalab instance with the name example-datalab and just 1 CPU

datalab create example-datalab --machine-type n1-standard-1

A full list of machine types can be viewed here.

Connect to a Datalab instance

When you create a Datalab instance using datalab create ... you should automatically be connected through port 8081 (i.e. going to http://localhost:8081 should take you to the datalab filesystem). However if your session is disconected you can reconnect to the instance using:

datalab connect {instance-name}

where you would replace {instance-name} with the name of the instance you created with the datalab create command. You can also use this command to connect to an existing datalab instance that is stopped, but not deleted.

You can see the list of all of the instances you have avalible to connect to by the following steps:

  1. Point your browser to the Google Cloud Platform Dashboard
  2. Select the project name that you created when initializing GCloud from the top dropdown list (to the right of the “Google Cloud Platform” header).
  3. Click the hambuger menu in the top left and click “Compute Engine > VM instances”.
  4. You should now see a list of your VM Instances.

You can reconnect to any of these instances by using the datalab-connect command as described above, using the name provided in the “Name” column.

Run a Jupyter Notebook on the Datalab instance

In your browser, navigate to http://localhost:8081

Now, upload the Jupyter notebook you want to run to the datalab instance via the “Upload” button.

Close the Datalab instance

Finally, when you are done, you can kill the active command (by typing Ctrl-C in the terminal). Then, delete the instance to avoid further billing with:

datalab delete --delete-disk example-datalab

NOTE: Ensure you have downloadeded your work before deleting the instance.

If you intend to reconnect to an instance you can instead use the stop command.

datalab stop {instance-name}

where you would replace {instance-name} with the name of the instance you would like to stop. To reconnect to this instance you would follow the instructions described in the “Connect to a Datalab instance” section above. Stopping the instance should preserve the file system while preventing further charges from running the virtual machine (however, there can still be a small trickle charge for maintaing the disk). NOTE: Even if you are only stopping your instance, it is important to download and backup your work before doing so.

You can visit the Google Cloud Console for an overview of your cloud environment e.g. to view billing information.