Pylearn2 doesn’t have any features explicitly designed to run large batches of experiments on computer clusters, but other related tools exist that enable you to run your Pylearn2 experiments in that way.
Script that allows you to submit jobs in an standard interface to multiple cluster that use different scheduler.
Allow to make a db of all wanted jobs to run and manage there executions(ensure all ended correctly, ...). It support grid search and random search out of the box. The experiment descriptions are compatible with pylearn2’s yaml format.
There is the Hyperplot project that allow to produce graphs and/or tables with jobman jobs: https://github.com/simlmx/hyperplot
Use postgres as a back-end database.
James project that allow to do “smart” hyper-parameter search. It also allow to manage jobs similar to jobman, but use mangodb as a back-end batabase.
It support atomic reservation of jobs.
For distributed optimization there are a few commandline utilities of interest: * hyperopt-mongo-search controls an optimization experiment * hyperopt-mongo-worker runs on worker nodes and polls a mongodb for experiments that need to be run. * hyperopt-mongo-show wraps a couple of visualization strategies of running experiments.
There is documentation coming along here: https://github.com/jaberg/hyperopt/wiki
Fred doesn’t have time to work on a switch from jobman to hyperopt to manage jobs. You can do try it if you want, but Fred won’t be able to help much.