我想出了解决该问题的方法。在初始化多处理实例之前，将Numpy与BLAS一起使用时会发生此问题。我的解决方法只是将Numpy代码（运行BLAS）放入单个进程中，然后再运行多处理实例。这不是一种很好的编码样式，但是可以。请参见下面的示例：
以下操作将失败-Python将退出：
import numpy as np
from multiprocessing import Pool, Process
def test(x):
arr = np.dot(x.T,x) # On large matrices, this calc will use BLAS.
if __name__ == '__main__':
x = np.random.random(size=((2000,500))) # Random matrix
test(x)
evaluations = [x for _ in range(5)]
p = Pool()
p.map_async(test,evaluations) # This is where Python will quit, because of the prior use of BLAS.
p.close()
p.join()
以下将成功：
import numpy as np
from multiprocessing import Pool, Process
def test(x):
arr = np.dot(x.T,x) # On large matrices, this calc will use BLAS.
if __name__ == '__main__':
x = np.random.random(size=((2000,500))) # Random matrix
p = Process(target = test,args = (x,))
p.start()
p.join()
evaluations = [x for _ in range(5)]
p = Pool()
p.map_async(test,evaluations)
p.close()
p.join()
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