如何在Google合作中使用GPU的Scipy最小化？

Question

我不得不将运行时类型更改为GPU，否则我将RAM崩溃。但是，当我使用GPU时，我在执行Scipy最小化时会遇到错误。错误如下： -

------Start--------
Traceback (most recent call last):
  File "<ipython-input-8-4ca37ba86fbb>", line 119, in train
    result=minimize(objective,val,constraints=cons,options={"disp":True})
  File "/usr/local/lib/python3.7/dist-packages/scipy/optimize/_minimize.py", line 618, in minimize
    constraints, callback=callback, **options)
  File "/usr/local/lib/python3.7/dist-packages/scipy/optimize/slsqp.py", line 315, in _minimize_slsqp
    for c in cons['ineq']]))
  File "/usr/local/lib/python3.7/dist-packages/scipy/optimize/slsqp.py", line 315, in <listcomp>
    for c in cons['ineq']]))
  File "<ipython-input-8-4ca37ba86fbb>", line 64, in constraint
    return -(A @ v)+alpha   # scipy proves >= for constraints
  File "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py", line 678, in __array__
    return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

------End--------

如何摆脱此问题？我需要将哪个张量复制到主机内存？我的目标是最小化和约束，如下所示： -

#Declaring the minimization equation here

def objective(x):
    alpha = x[0]
    v=x[1:len(x)]
    vnorm=torch.linalg.vector_norm(v) * torch.linalg.vector_norm(v)
    return alpha+(vnorm/2)

#Declaring the constraint here

def constraint(x):
    alpha=x[0]
    v=x[1:len(x)]
    return -(A @ v)+alpha


cons={'type':'ineq','fun':constraint}
result=minimize(objective,val,constraints=cons,options={"disp":True})

Answer 1

val变量是torch.tensor或matrix a，该在约束函数中使用。因此，如果val是torch.tensor计算 result 带有以下行：

result = minimize(objective, val.cpu().numpy(), constraints=cons, options={"disp" : True})

该方式vals在主机上转移到nd.array，如最小化文档。将a转为nd.array（如果需要）可以做同样的方法。