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- Have a laptop
- get a terminal on a psana machine, ideally with graphics
- use nomachine - probably best Remote Visualization
- mac - can also install XQuartz, and use the terminal program
- windows - only free X connection is through cygwin, but that is heavyweight softwarepossible Xming , other heavyweight options: cygwin, setting up a virtual box running linux
Software
In bash, source ~davidsch/mlearntut.sh for environment.
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- from pslogin (a machine with outside internet), do git clone https://github.com/davidslac/mlearntut.git
- you may need to go back and forth to the outside internet connection - leave pslogin terminal up
- start new terminaterminal, ssh to pslogin, then ssh to psana
- cd to the mlearntut directory you made, from the pslogin terminal
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If resources are getting tight on the interactive nodes, you can launch jobs on the batch. You can launch the jobs in 'interactive' mode so you can see output (graphical output won, however you can't work from batch)do any graphics or plotting from jobs running on batch. Here is an example:
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bsub -q psanaq -I python ex01_keras_train.py
I'm getting a big MPI warning when I run these jobs. I think it is safe to ignore it. To do so, do
export OMPI_MCA_mpi_warn_on_fork=0
I am not sure why we need to set mpi_warn_on_fork=0, it quiets a noisy error message. Although we aren't using MPI, our hdf5 library is built with it, so some MPI aware code is running and complaining. Note, the code includes
The examples in the tutorial do
sys.stdout.flush()
calls so that we can see print output more immediately while running on batch interactively, without the flush calls, the batch system queues up program output.