Google Cloud Machine Learning

1. Create an Google Compute Engine Instance
2. Activate the Google Machine Learning API under this project
3. Create an Google Storage Bucket
4. Login to the Google Compute Engine, create a folder for the ML project, in my case I called it MLtest. Inside this folder, two basic configuration files are required.

a. config.yaml

It is important to set "runtimeVersion" to be the latest, otherwise, some functions may not be available.

b. setup.py

5. The file inside the folder shows the structure blow.

to run a training module LOCALLY, such as 8-output.py, you type

In order to submit the job to Google Cloud ML, the following command is required,

However, users will not be able to read and write directory to the cloud storage bucket. As this being said, below commands will NOT work.

File IO can to be handle through

Similarly, you can read a file through the same way.

6. To check the current status of jobs,

 

Anaconda virtual environment setup python 2, 3 and R

After install anaconda 3