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.


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

to run a training module LOCALLY, such as, 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


Point In Time vs. Though The Cycle

Point In Time (PIT):  1 year period (short-term)

Through The Cycle (TTC): 5 year period observation (long-term)

The following article well explains why there are two terms used.

In case the link broke in the original website, a copy is kept here.



Run Cobo with internal pulser

After decompressing the tarball:

- open a terminal and, from the config subdirectory, run getEccServer
- open a terminal and, from the data directory, run dataRouter (GetController must not be running as dataRouter uses the same port)
- open a terminal and, from the config subdirectory, run getEccClient
- from the prompt of getEccClient, execute "exec init_and_start.ecc"

Installing GET software

For Ubuntu 16.04 LTS, the DAQ software prerequisites can be fulfilled by simply typing
The software package can be found in
but the installation path needs to be changed to PREFIX=/usr, as /usr/local/bin is not the default path Ubuntu will look through.


The 20170928 is the release date, which you can find in