Training of neural networks using variations of 'evolutionary' methods including the 'Evolutionary Strategy' presented by OpenAI and Variational Optimization.

To create a new environment with the required packages, run

conda env create -f environment.yml

or to update an existing environment to include the required packages, run

conda env update --file environment.yml

Any of these two commands will create an Anaconda virtual environment called ml

To run the code on the High Performance Computing Cluster at the Technical University of Denmark first of all requires a user login.

The easiest way to create the environment on the HPC is using pip.

The script hpc_python_setup.sh will setup up the environment. The environment is called mlenv in this case.

Anaconda can be installed on the HPC. Get the latest 64 bit x86 version from https://www.anaconda.com/download/#linux.

1. Move the downloaded .sh file to the root of the HPC.
2. Install Anaconda by calling bash Anaconda3-5.0.1-Linux-x86_64.sh at the root.
3. Follow the installation instructions.

My personal root directory is /zhome/c2/b/86488/

A connection to the HPC can be established by SSH by ssh [email protected]

A local mirror of the user folder on the HPC can be created by sshfs sshfs [email protected]:/zhome/c2/b/86488 ~/mnt -o defer_permissions -o volname=hpc

A single job can be run (not submitted) by executing the run_hpc.sh script.

An entire batch of jobs can be submitted using the submit_batch_hpc.sh script. The specific inputs to each of the jobs must be specified in this script in the INPUTS array. An example call to submit_batch_hpc.sh which is

bash submit_batch_hpc.sh -i SM-experiment -t 10:00 -c 24 -q hpc

This will submit a series of jobs named "SM-experiment-[id]" with wall clock time limit of 10 hours, requesting a 24 core machine on the hpc queue

The data-analysis/monitor.py script allows for monitoring of multiple jobs running in parallel, e.g. on the HPC. The script takes a directory of checkpoints as input and uses the saved stats.pkl file.

It saves summarizing plots in the source checkpoint folder and displays statistics in the console.