2-dl-container/Container-Root/job/bert/direct_benchmark-inf.py [79:96]:
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    latency_time = time.time() - begin
    latency_list.append(latency_time)
    return


def benchmark(num_models, num_threads, num_requests, model_file):
    # Load a model into each NeuronCore
    print('Loading Models To Memory')
    models = [load_model(model_file) for _ in range(num_models)]
    print('Starting benchmark')
    output_list = []
    begin = time.time()
    futures = []
    # Submit all tasks and wait for them to finish
    # https://stackoverflow.com/questions/51601756/use-tqdm-with-concurrent-futures
    with tqdm(total=num_requests) as pbar:
        with ThreadPoolExecutor(num_threads) as pool:
            for i in range(num_requests):
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2-dl-container/Container-Root/job/resnet/direct_benchmark-inf.py [85:103]:
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    latency_time = time.time() - begin

    latency_list.append(latency_time)
    return


def benchmark(num_models, num_threads, num_requests, model_file):
    # Load a model into each NeuronCore
    print('Loading Models To Memory')
    models = [load_model(model_file) for _ in range(num_models)]
    print('Starting benchmark')
    output_list = []
    begin = time.time()
    futures = []
    # Submit all tasks and wait for them to finish
    # https://stackoverflow.com/questions/51601756/use-tqdm-with-concurrent-futures
    with tqdm(total=num_requests) as pbar:
        with ThreadPoolExecutor(num_threads) as pool:
            for i in range(num_requests):
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