def create_spaces_and_args_pickles(args):
f = open(
"/lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/tmp/hyperres_pickled_args",
"wb")
pickle.dump(args, f)
f.close()
print(
"Dumped arguments to /lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/tmp/hyperres_pickled_args, "
"now creating hyperspaces.")
# Defining the hyperspace
if args.model == "segmentation_cityscapes":
hyperparameters = [
(0.00001, 0.1), # learning_rate
(10, 100), # epochs
(8, 24), # batch size
(1, .00000001)
] # epsilon for Adam optimizer
elif args.model == "segmentation_gis":
hyperparameters = [
(0.00001, 0.1), # learning_rate
(10, 100), # epochs
(100, 1000), # batch size
(1, .00000001)
] # epsilon for Adam optimizer
else:
hyperparameters = [
(0.00001, 0.1), # learning_rate
(0.2, 0.9), # dropout
(10, 100), # epochs
(10, 500), # batch size
(.00000001, 1)
] # epsilon for Adam optimizer
# create pickled space
space = create_hyperspace(hyperparameters)
f = open(
"/lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/tmp/hyperres_pickled_spaces",
"wb")
pickle.dump(space, f)
f.close()
print(
"Dumped scikit opt spaces to /lus/theta-fs0/projects/CVD-Mol-AI/mzvyagin/tmp/hyperres_pickled_spaces.... "
"Submitting batch jobs to Cobalt now.")
return space
if __name__ == "__main__":
parser = ArgumentParser(
"Run AlexNet cross framework tuning on PyTorch and AlexNet.")
parser.add_argument("--out",
"-o",
help="Specify the out csv filename.",
required=True)
args = parser.parse_args()
# Defining the hyperspace
hyperparameters = [
(0.00001, 0.1), # learning_rate
(0.2, 0.9), # dropout
(10, 100), # epochs
(10, 1000)
] # batch size
space = create_hyperspace(hyperparameters)
# Perform runs and aggregate results
results = []
for section in tqdm(space):
# create a skopt gp minimize object
optimizer = Optimizer(section)
search_algo = SkOptSearch(
optimizer, ['learning_rate', 'dropout', 'epochs', 'batch_size'],
metric='average_res',
mode='max')
analysis = tune.run(multi_train,
search_alg=search_algo,
num_samples=50,
resources_per_trial={'gpu': 1})
results.append(analysis)