thetaspaceray 0.0.4

Batched SpaceRay tuning.

Theta Integration for SpaceRay

Theta batching for SpaceRay package in order to submit Cobalt jobs and run spaces on different GPU nodes.

Installation

In order to use:

• In order to use this package on ThetaGPU, you need two things:
  1. Definition of objective function
  2. argparse parsed argument space with the following required components:
  • --out: outfile
  • --json: json file of hyperparameter bounds
  • --trials: number of trials per space, not total
  • --mode: mode to apply during tune.run, defaults to "max" (optional)
  • --metric: metric used to guide tune.run search, defaults to "average_res" (optional)
  • --ray_dir: directory used to store Ray Tune logging files, defaults to /lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/ray_results (optional)

Example Usage

from argparse import ArgumentParser

### see ray tune docs for more info on how to define objective function and report metrics to ray tune
def objective_func(config):
    ### function training and testing using config from tune.run, then report results
    model.train()
    res = model.test()
    res_dict = {}
    res_dict['res'] = res
    tune.report(**res_dict)
    return res

if __name__ == "__main__":
   print("WARNING: default file locations are used to pickle arguments and hyperspaces. "
         "DO NOT RUN MORE THAN ONE EXPERIMENT AT A TIME.")
   print("Creating spaces.")
   parser = ArgumentParser("Run spaceray hyperparameter search on .")
   startTime = time.time()
   ray.init()
   parser.add_argument("-o", "--out")
   parser.add_argument("-m", "--model")
   parser.add_argument("-t", "--trials")
   parser.add_argument("-n", "--nodes", help="Number of GPU nodes to submit on.")
   parser.add_argument("-j", "--json", help="JSON file defining hyperparameter search space")
   arguments = parser.parse_args()
   theta_spaceray.run(objective_func, arguments)