This tutorial focuses looks at building convolutional neural networks for supervised classification problems. 

Computer Setup/Graphics

1. Have a laptop
2. get a terminal on a psana machine, ideally with graphics
   1. use nomachine - probably best Remote Visualization
   2. mac - can also install XQuartz, and use the terminal program
   3. windows - possible Xming , other heavyweight options: cygwin, setting up a virtual box running linux

...

The script clears environment variables like PYTHONPATH, LD_LIBRARY_PATH, and sets PATH to the rhel7 default, and puts a miniconda bin dir at the frount. Then it activates the 'mlearntut' environment in the conda install.

We will use Keras and tensorflow

There appears to be a new Keras like interface just for tensorflow: http://tflearn.org, don't have any experience with this yet

Data

Presently the data files are at /reg/d/ana01/temp/davidsch/ImgMLearnSmall
you can't see the data from pslogin, you have to be on the psana nodes.

Some notes on the hdf5 files: final-h5-output 

Code

1. from pslogin (a machine with outside internet), do git clone https://github.com/davidslac/mlearntut.git
2. you may need to go back and forth to the outside internet connection -  leave pslogin terminal up
3. start new terminal, ssh to pslogin, then ssh to psana
4. cd to the mlearntut directory you made, from the pslogin terminal

...