  memory measurement it is recommended to run each memory benchmark in a separate process by making sure
  no_multi_processing is set to True.
One should always state the environment information when sharing the results of a model benchmark. Results can vary
  heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
  useful for the community.
Sharing your benchmark
Previously all available core models (10 at the time) have been benchmarked for inference time, across many different
settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
done across CPUs (except for TensorFlow XLA) and GPUs.
The approach is detailed in the following blogpost and the results are
available here.
