question: training on gpu becomes non-deterministic
context: I usually set seed_everything() at the beginning of my script but this does not always solves the problem.
  When I train models on cpu, it is determinstic; but when I switch to gpu, it becomes non-deterministic.
  When I am training simple model, like one-layer LSTM, it is deterministic both on cpu and gpu.
  But when I train a more completed model like LSTM-FCN, it is deterministic on cpu but not on gpu
  Can I get any help on debugging?
  I got my LSTM-FCN model from here (https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN_FCN.py) and the LSTM model I tested was simply a nn.LSTM
answer: maybe setting Trainer(deterministic=True) might help?