if args.only_cpu: ONLY_CPU = True if args.minimize_mode: OPTIMIZE_MODE = "min" if __name__ == "__main__": parser = argparse.ArgumentParser("Start bi model tuning with hyperspace and resiliency testing, " "specify output csv file name.") parser.add_argument("-o", "--out", required=True) parser.add_argument("-m", "--model") parser.add_argument("-t", "--trials") parser.add_argument("-j", "--json") parser.add_argument('-d', "--max_diff", action="store_true") parser.add_argument('-r', '--minimize_resiliency', action="store_true") parser.add_argument('-l', '--on_lambda', action="store_true") parser.add_argument('-n', '--start_space') parser.add_argument('-c', '--only_cpu', action='store_true') parser.add_argument('-p', '--project_name', default="hyper_sensitive") parser.add_argument('--minimize_mode', action="store_true") args = parser.parse_args() bitune_parse_arguments(args) # print(PT_MODEL) print(OPTIMIZE_MODE) if args.on_lambda: spaceray.run_experiment(args, multi_train, ray_dir="~/raylogs", cpu=8, start_space=int(args.start_space), mode=OPTIMIZE_MODE) else: spaceray.run_experiment(args, multi_train, ray_dir="/lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/raylogs", cpu=8, start_space=int(args.start_space), mode=OPTIMIZE_MODE, project_name=args.project_name, group_name='bi_tune')
from hyper_resilient_experiments.alexnet_cifar import pytorch_alexnet, tensorflow_alexnet import spaceray from ray import tune class Namespace: def __init__(self, **kwargs): self.__dict__.update(kwargs) def objective(config): acc, model = tensorflow_alexnet.cifar100_tf_objective(config) search_results = {'average_res': acc} tune.report(**search_results) return search_results if __name__ == "__main__": args = Namespace(json='../standard.json', trials=10, out='tensorflow_benchmark.csv') spaceray.run_experiment( args, objective, ray_dir="/lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/raylogs", cpu=8, start_space=0, mode="max")
if __name__ == "__main__": parser = argparse.ArgumentParser( "Start bi model tuning with hyperspace and resiliency testing, " "specify output csv file name.") parser.add_argument("-o", "--out", required=True) parser.add_argument("-m", "--model") parser.add_argument("-t", "--trials") parser.add_argument("-j", "--json") parser.add_argument('-d', "--max_diff", action="store_true") parser.add_argument('-r', '--minimize_resiliency', action="store_true") parser.add_argument('-l', '--on_lambda', action="store_true") parser.add_argument('-f', '--framework', required=True) parser.add_argument('-n', '--start_space') args = parser.parse_args() bitune_parse_arguments(args) # print(PT_MODEL if args.on_lambda: spaceray.run_experiment(args, double_train, ray_dir="~/raylogs", cpu=8, start_space=int(args.start_space)) else: spaceray.run_experiment( args, double_train, ray_dir="/lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/raylogs", cpu=8, start_space=int(args.start_space))
import numpy as np import argparse import torch import spaceray from ray import tune def objective(config): search_results = {} average_res = np.random.rand() search_results['average_res'] = average_res tune.report(**search_results) return search_results if __name__ == "__main__": parser = argparse.ArgumentParser( "Start bi model tuning with hyperspace and resiliency testing, " "specify output csv file name.") parser.add_argument("-o", "--out", required=True) parser.add_argument("-m", "--model") parser.add_argument("-t", "--trials") parser.add_argument("-j", "--json") # print(NUM_CLASSES) args = parser.parse_args() spaceray.run_experiment( args, objective, cpu=8, ray_dir="/lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/raylogs")
print("NOTE: Training using Max Diff approach") if args.minimize_resiliency: MIN_RESILIENCY = True print("NOTE: Training using Min Resiliency approach") if __name__ == "__main__": parser = argparse.ArgumentParser( "Start bi model tuning with hyperspace and resiliency testing, " "specify output csv file name.") parser.add_argument("-o", "--out", required=True) parser.add_argument("-t", "--trials") parser.add_argument("-j", "--json") parser.add_argument('-d', "--max_diff", action="store_true") parser.add_argument('-r', '--minimize_resiliency', action="store_true") parser.add_argument('-l', '--on_lambda', action="store_true") args = parser.parse_args() bitune_parse_arguments(args) # print(PT_MODEL) if args.on_lambda: spaceray.run_experiment(args, segmentation_multi_train, ray_dir="~/raylogs", cpu=8) else: spaceray.run_experiment( args, segmentation_multi_train, ray_dir="/lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/raylogs", cpu=8)