INNER CODE UNIT · Python

exp_duration

NVIDIA/DeepLearningExamples · PyTorch/Classification/ConvNets/main.py:647

    exp_duration = time.time() - exp_start_time
    if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
        logger.end()
    print("Experiment ended")


if __name__ == "__main__":
    epilog = [
        "Based on the architecture picked by --arch flag, you may use the following options:\n"
    ]
    for model, ep in available_models().items():
        model_help = "\n".join(ep.parser().format_help().split("\n")[2:])
        epilog.append(model_help)
    parser = argparse.ArgumentParser(
        description="PyTorch ImageNet Training",
        epilog="\n".join(epilog),
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )

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