How severe does this issue affect your experience of using Ray?
- High: It blocks me to complete my task.
Hi, I got stuck when I use ray to do multiprocessing inference. To be exactly, I have 8 GPU, and I want to do some inference with multiprocessing to speed up my work. On each process, a model will be put on a single GPU, which means there are 8 processes and 8 models. It is normally when I use
concurrent.futures.ProcessPoolExecutor, but when I use ray, only one GPU is activated. Here is a snippet of my code.
@ray.remote(num_gpus=8) def my_job(args): # do some jobs def main(): futures = [my_job.remote(j) for j in jobs] results = ray.get(futures) if __name__ == "__main__": main()