2-dl-container/Container-Root/job/bert/direct_benchmark-gpu.py [34:47]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
model_dict = dict()
model_dict['return_dict'] = False
# Get tokenizer and create encoded inputs
tokenizer = AutoTokenizer.from_pretrained(model_name)

gen = DocumentGenerator()
sequence_list = []
encoded_input_list = []
for _ in np.arange(num_request_samples):
    sequence = gen.sentence()
    encoded_inputs = tokenizer.encode_plus(sequence, max_length=max_length, padding='max_length', truncation=True,
                                           return_tensors='pt')
    sequence_list.append(sequence)
    encoded_input_list.append(encoded_inputs)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



2-dl-container/Container-Root/job/bert/direct_benchmark-inf.py [44:57]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
model_dict = dict()
model_dict['return_dict'] = False
# Get tokenizer and create encoded inputs
tokenizer = AutoTokenizer.from_pretrained(model_name)

gen = DocumentGenerator()
sequence_list = []
encoded_input_list = []
for _ in np.arange(num_request_samples):
    sequence = gen.sentence()
    encoded_inputs = tokenizer.encode_plus(sequence, max_length=max_length, padding='max_length', truncation=True,
                                           return_tensors='pt')
    sequence_list.append(sequence)
    encoded_input_list.append(encoded_inputs)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -