Google Cloud Deep Learning Kit

This repository aims to create a GPU instance with Jupyter, Tensorflow and Keras on google cloud platform in an instant.

Requirements

• make
• Google Cloud SDK
• Docker

Life Cycle to Use this Kit

1. Create an instance with make create-instance
2. Run jupyter on the instance with make run-jupyter

• It may takes 5 minutes or so.

3. Install python libraries with make pip-install

• Put libraries you want to install in ./requirements.txt

4. Upload files to the instance with make upload-files.
5. Make ssh tunnel to the instance with make ssh-tunnel
6. Access jupyter via your web browser

• Default: http://localhost:18888

7. Download outputs with make download-outputs
8. Delete the instance with make delete-instance

Commands Reference

Create a GCP instance with GPU

It may take 5 minutes or so to finish to execute the startup script to install the require environment in your instance.

make create-instance \
  INSTANCE_NAME="test-gpu-instance" \
  GCP_PROJECT_ID=xxx-xxx-xxx

Run Jupyter as a docker container

make run-jupyter \
  INSTANCE_NAME="test-gpu-instance" \
  GCP_PROJECT_ID=xxx-xxx-xxx

SSH tunnel

In order to access the jupyter which you launched, you have to have a SSH tunnel. When you got it, you can access http://localhost:18888 via a web browser on your local machine. When you don't set any value with make ssh-tunnel, the port is the default value. The default of JUPYTER_PORT is 18888.

make ssh-tunnel \
  INSTANCE_NAME="test-gpu-instance" \
  GCP_PROJECT_ID=xxx-xxx-xxx \
  JUPYTER_PORT=18888

Delete a instance you created

make delete-instance \
  INSTANCE_NAME="test-gpu-instance" \
  GCP_PROJECT_ID=xxx-xxx-xxx

Install python libraries

make pip-install \
  INSTANCE_NAME="test-gpu-instance" \
  GCP_PROJECT_ID=xxx-xxx-xxx

Upload your files

make upload-files \
  INSTANCE_NAME="test-gpu-instance" \
  GCP_PROJECT_ID=xxx-xxx-xxx \
  FROM=/path/to/your/files

Download ouputs

make download-outputs \
  INSTANCE_NAME="test-gpu-instance" \
  GCP_PROJECT_ID=xxx-xxx-xxx \
  TO=/path/to/your/destination

Links

• yuiskw/google-cloud-deep-learning-kit - Docker Hub