问题描述
我有一个RL代理，我想同时使用我的gpu进行训练和推理。后来将cuda与torch.multiprocessing一起使用似乎效果不佳，并且在初始化其他过程时出现此错误：
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing,you must use the 'spawn' start method
但是使用'spawn'start方法给我带来了另一个问题，因为该方法尝试使用pickle序列化所有内容，这给我带来了optim.zero_grad（）上的另一个神秘错误。在optim.zero_grad（）之前和之后，我已经分离了重放缓冲区中的所有内容。
File "env.py",line 60,in <module>
main()
File "env.py",line 55,in main
process_two.start()
File "/home/`/anaconda3/lib/python3.7/multiprocessing/process.py",line 112,in start
self._popen = self._Popen(self)
File "/home/zero/anaconda3/lib/python3.7/multiprocessing/context.py",line 223,in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/home/zero/anaconda3/lib/python3.7/multiprocessing/context.py",line 284,in _Popen
return Popen(process_obj)
File "/home/zero/anaconda3/lib/python3.7/multiprocessing/popen_spawn_posix.py",line 32,in __init__
super().__init__(process_obj)
File "/home/zero/anaconda3/lib/python3.7/multiprocessing/popen_fork.py",line 20,in __init__
self._launch(process_obj)
File "/home/zero/anaconda3/lib/python3.7/multiprocessing/popen_spawn_posix.py",line 47,in _launch
reduction.dump(process_obj,fp)
File "/home/zero/anaconda3/lib/python3.7/multiprocessing/reduction.py",in dump
ForkingPickler(file,protocol).dump(obj)
File "/home/zero/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/reductions.py",line 136,in reduce_tensor
raise RuntimeError("Cowardly refusing to serialize non-leaf tensor which requires_grad,"
RuntimeError: Cowardly refusing to serialize non-leaf tensor which requires_grad,since autograd does not support crossing process boundaries. If you just want to transfer the data,call detach() on the tensor before serializing (e.g.,putting it on the queue).
所以，我的问题是，有没有办法在GPU上并行进行这种异步训练/推理设置？ torch.multiprocessing的默认启动方法似乎在我的多核cpu上运行良好。但我想使用我的GPU进行更优化的Matmul操作...
解决方法
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