def init(config, _run):
args = SimpleNamespace(**config)
assertions.validate_hypers(args)
mlh.seed_all(args.seed)
args.data_path = assertions.validate_dataset_path(args)
if args.activation is not None:
if 'relu' in args.activation:
args.activation = torch.nn.ReLU()
elif 'elu' in args.activation:
args.activation = torch.nn.ELU()
else:
args.activation = torch.nn.ReLU()
args._run = _run
Path(args.artifact_dir).mkdir(exist_ok=True)
args.loss_name = args.loss
if args.cuda and torch.cuda.is_available():
args.device = torch.device('cuda')
args.cuda = True
else:
args.device = torch.device('cpu')
args.cuda = False
args.partition_scheduler = updates.get_partition_scheduler(args)
args.partition = util.get_partition(args)
args.data_path = Path(args.data_path)
return args
def init(config):
# This gives dot access to all paths, hyperparameters, etc
args = SimpleNamespace(**config)
assertions.validate_hypers(args)
args = mlh.detect_cuda(args)
mlh.seed_all(args.seed)
# get data
args.data = data_handler.get_dataset(args)
# get model
args.model = model_handler.get_model(args)
return args
def init(config, _run):
args = SimpleNamespace(**config)
assertions.validate_hypers(args)
mlh.seed_all(args.seed)
args.data_path = assertions.validate_dataset_path(args)
if args.activation is not None:
if 'relu' in args.activation:
args.activation = torch.nn.ReLU()
elif 'elu' in args.activation:
args.activation = torch.nn.ELU()
else:
args.activation = torch.nn.ReLU()
args._run = _run
args.model_dir = args.artifact_dir
if args.checkpoint or args.record:
unique_directory = Path(args.model_dir) / str(uuid.uuid4())
unique_directory.mkdir(parents=True)
args.unique_directory = unique_directory
# Save args json for grepability
with open(args.unique_directory / 'args.json', 'w') as outfile:
json.dump(dict(config), outfile, indent=4)
args.loss_name = args.loss
if args.cuda and torch.cuda.is_available():
args.device = torch.device('cuda')
args.cuda = True
else:
args.device = torch.device('cpu')
args.cuda = False
args.partition_scheduler = updates.get_partition_scheduler(args)
args.partition = util.get_partition(args)
args.per_batch = False if (args.per_batch
and args.per_sample) else args.per_batch
args.data_path = Path(args.data_path)
return args