deviceStr = "cuda"
print("GPU is available")
else:
device = torch.device("cpu")
print("GPU not available, CPU used")
print("Using device {}.".format(device))
torch.manual_seed(args.seed)
dataset = Dataset(args.data_dir)
train_loader = dataset.getDatasetTrain(args.batch_size)
test_loader = dataset.getDatasetTest(args.batch_size)
val_loader = dataset.getDataValid(args.batch_size)
dictionary = dataset.getDictionaryFromS3(args.dictionary_file_name)
modelConfig = {
"embedding_dim" : args.embedding_dim,
"hidden_dim" : args.hidden_dim,
"vocab_size" : args.vocab_size,
"output_size" : args.output_size,
"n_layers" : args.n_layers,
"device" : deviceStr,
"batch_size" : args.batch_size,
}
model_cfg = os.path.join(args.model_dir, 'model.cfg')
with open(model_cfg, 'wb') as f:
torch.save(modelConfig, f)
