Which Google Cloud Platform service is the easiest for running Tensorflow?

Question

While working on Udacity Deep Learning assignments, I encountered memory problem. I need to switch to a cloud platform. I worked with AWS EC2 before but now I would like to

Answer 1

Summing up the answers:

• AI Platform Notebooks - One click Jupyter Lab environment
• Deep Learning VMs images - Raw VMs with ML libraries pre-installed
• Deep Learning Container Images - Containerized versions of the DLVM images
• Cloud ML
• Manual installation on Compute Engine. See instructions below.

Instructions to manually run TensorFlow on Compute Engine:

1. Create a project
2. Open the Cloud Shell (a button at the top)
3. List machine types: gcloud compute machine-types list. You can change the machine type I used in the next command.
4. Create an instance:

gcloud compute instances create tf \
  --image container-vm \
  --zone europe-west1-c \
  --machine-type n1-standard-2

4. Run sudo docker run -d -p 8888:8888 --name tf b.gcr.io/tensorflow-udacity/assignments:0.5.0 (change the image name to the desired one)
5. Find your instance in the dashboard and edit default network.
6. Add a firewall rule to allow your IP as well as protocol and port tcp:8888.
7. Find the External IP of the instance from the dashboard. Open IP:8888 on your browser. Done!
8. When you are finished, delete the created cluster to avoid charges.

This is how I did it and it worked. I am sure there is an easier way to do it.

More Resources

You might be interested to learn more about:

• Google Cloud Shell
• Container-Optimized Google Compute Engine Images
• Google Cloud SDK for a more responsive shell and more.

Good to know

• "The contents of your Cloud Shell home directory persist across projects between all Cloud Shell sessions, even after the virtual machine terminates and is restarted"
• To list all available image versions: gcloud compute images list --project google-containers

Thanks to @user728291, @MattW, @CJCullen, and @zain-rizvi