Laboratory to bluemix, a cloud solution exercise to etl scheduler

Archive for the month “June, 2016”



1. Preparation

Before start tutorial please check out Tensorflow install@Windows for environment preparation.

2. Git pull tutorial

There are tutorials on gitHub.

3. Mount tutorial to docker

docker -v /c/Users/jesse/Desktop/py/workspace/Tensorflow/tutorial:/notebooks/tutorial

Notice: /c/Users is the Docker default shared folder on Windows





Jupyter setup

Jupyter  is is a web-based interactive computational environment for creating IPython notebooks. Easy to shared tutorial or exercise information and snippet. It supported over 30 engine, include R, Java script, Java and  other Engine list.

My environment is Python 3.5.1:: Anaconda 4.0.0 (32-bit).

It is more convenient to install a Python distribution such as Anaconda; see here. Anaconda not only installs IPython and its requirements, but also a selection of frequently-used Python packages.


Jupyter shortcut

After install Anaconda, you need to setup Jupyter short cut as

C:\Anaconda3\python.exe C:\Anaconda3\ C:\Anaconda3 "C:/Anaconda3/python.exe" "C:/Anaconda3/Scripts/"


Create a Python 2 Engine

Or using conda, create a Python 2 environment:

conda create -n ipykernel_py2 python=2 ipykernel
activate ipykernel_py2    # On Windows, remove the word 'source'
python -m ipykernel install --user

You can find the engine “Python 2” is  added to your menu now:

Home - Google Chrome_2016-06-05_07-13-22

If you’re running Jupyter on Python 2 and want to set up a Python 3 kernel, follow the same steps, replacing step 2,3.


Post Navigation