Which Google Cloud Platform service is the easiest

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 try Google Cloud Platform (GCP). I will need at least 8GB memory. I know how to use docker locally but never tried it on the cloud.

1. Is there any ready-made solution for running Tensorflow on GCP?
2. If not, which service (Compute Engine or Container Engine) would make it easier to get started?
3. Any other tip is also appreciated!

Answer 1

Summing up the answers:

• Datalab
• Cloud ML
• Manual installation on Compute Engine. See instructions below.

Step by step instructions to 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. and @CJCullen.

Answer 2

Google Cloud Machine Learning is open to the world in Beta form today. It provides TensorFlow as a Service so you don't have to manage machines and other raw resources. As part of the Beta release, Datalab has been updated to provide commands and utilities for machine learning. Check it out at: http://cloud.google.com/ml.

Answer 3

Im not sure there if there is a need for you to stay on the Google Cloud platform. If you are able to use other products you might save a lot of time, and some money.

If you are using TensorFLow I would recommend a platform called TensorPort. It is exclusively for TesnorFlow and is the easy platform I am aware of. Code and data are loaded with git and they provide a python module for automatic toggling of paths between remote and your local machine. They also provide some boiler plate code for setting up distributed computing if you need it. Hope this helps.

Answer 4

Google has a Cloud ML platform in a limited Alpha.

Here is a blog post and a tutorial about running TensorFlow on Kubernetes/Google Container Engine.

If those aren't what you want, the TensorFlow tutorials should all be able to run on either AWS EC2 or Google Compute Engine.

Answer 5

You now can also use pre-configured DeepLearning images. They have everything that is required for the TensorFlow.