``pytest`` can't deal with distributed training directly. If this is attempted - the sub-processes don't do the right thing and end up thinking they are ``pytest`` and start running the test suite in loops. It works, however, if one spawns a normal process that then spawns off multiple workers and manages the IO pipes.
This is still under development but you can study 2 different tests that perform this successfully:
* `test_seq2seq_examples_multi_gpu.py <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/test_seq2seq_examples_multi_gpu.py>`__ - a ``pytorch-lightning``-running test (had to use PL's ``ddp`` spawning method which is the default)
* `test_finetune_trainer.py <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/test_finetune_trainer.py>`__ - a normal (non-PL) test
To jump right into the execution point, search for the ``execute_async_std`` function in those tests.
You will need at least 2 GPUs to see these tests in action: