

By default, the time and the required memory for inference are benchmarked. In the example output above the first
two sections show the result corresponding to inference time and inference memory. In addition, all relevant
information about the computing environment, e.g. the GPU type, the system, the library versions, etc are printed
out in the third section under ENVIRONMENT INFORMATION. This information can optionally be saved in a .csv file
when adding the argument save_to_csv=True to [PyTorchBenchmarkArguments] and
[TensorFlowBenchmarkArguments] respectively. In this case, every section is saved in a separate
.csv file. The path to each .csv file can optionally be defined via the argument data classes.
Instead of benchmarking pre-trained models via their model identifier, e.g. google-bert/bert-base-uncased, the user can
alternatively benchmark an arbitrary configuration of any available model class. In this case, a list of
configurations must be inserted with the benchmark args as follows.