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

Answer 2

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 3

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

Answer 4

This is an old question but there's are new, even easier options now:

If you want to run TensorFlow with Jupyter Lab

GCP AI Platform Notebooks, which gives you on-click access to a Jupyter Lab Notebook with Tensorflow pre-installed (you can also use Pytorch, R, or a few other libraries instead if you prefer).

If you just want to use a raw VM

If you don't care about Jupyer Lab and just want a raw VM with Tensorflow pre-installed, you can instead create a VM using GCP's Deep Learning VM Image. These DLVM images give you a VM with Tensorflow pre-installed and are all setup to use GPUs if you want. (The AI Platform Notebooks use these DLVM images under the hood)

If you'd like to run it on both your laptop and the cloud

Finally, if you want to be able to run tensorflow both on your personal laptop and in the cloud and are comfortable using Docker, you can use GCP's Deep Learning Container Images. It contains the exact same setup as the DLVM images, but packaged as a container instead, so you can launch these anywhere you like.

Extra benefit: If you're running this container image on your laptop, it's 100% free :D

Answer 5

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 6

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.