required=False,
type=str,
default=None)
parser.add_argument("--dataset",
help="path to your dataset",
required=True,
type=str)
parser.add_argument("--configuration",
help="which configuration file to use",
required=True,
type=str)
parser.add_argument("--gpu",
help="which gpu to use, by deault all will be utilized",
type=int,
default=-1)
args = parser.parse_args()
factory = MachineFactory(config_file=args.configuration)
model = factory.load_model(name=args.model)
if args.gpu < 0:
factory.train(args.dataset, model=model, gpus=get_gpu_count())
else:
os.environ['CUDA_VISIBLE_DEVICES'] = "{}".format(args.gpu)
factory.train(args.dataset, model=model, gpus=1)
# uploading dataset to cloud storage for reuse by other software
share = S3Share()
share.submit(args.dataset)
def test_train_model():
machine = MachineFactory()
machine.train("datasets/clean_dirty", gpus=1)