小编Sri*_*hat的帖子

我有一个暴露类型= LoadBalancer的服务,当我做一个

kubectl describe services servicename,

我得到这个输出:

Name:           ser1
Namespace:      default
Labels:         app=online1
Selector:       app=online1
Type:           LoadBalancer
IP:         10.0.0.32
External IPs:       192.168.99.100
Port:           <unset> 8080/TCP
NodePort:       <unset> 30545/TCP
Endpoints:      172.17.0.10:8080,172.17.0.11:8080,172.17.0.8:8080 + 1 more...
Session Affinity:   None

有人可以指导以下疑问:

1.)我无法理解<unset>Port和NodePort中的含义.另外,它如何影响我的服务？

2.)当我想要服务时,我使用了<external-ip:NodePort>正确的服务？那么Port的用途是什么？

我试过很多次搜索这个。静态数组通常存储在堆栈中，它们的大小在编译时确定。前任：

int main()
{
    int n;
    scanf("%d", &n);
    int array[n];
    printf("%u", sizeof(array));
    return 0;
}

数组的大小随 n 的不同值而变化。因此，这里的数组不应该存储在堆中，因为大小是在运行时确定的吗？一直对此感到困惑。请帮忙。谢谢你！

运行tensorflow代码时出现以下错误:

Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_shape.py", line 579, in merge_with
    new_dims.append(dim.merge_with(other[i]))
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_shape.py", line 138, in merge_with
    self.assert_is_compatible_with(other)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_shape.py", line 111, in assert_is_compatible_with
    other))
ValueError: Dimensions 5 and 4 are not compatible

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 602, in gradients
    in_grad.set_shape(t_in.get_shape())
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 407, in set_shape
    self._shape = self._shape.merge_with(shape)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_shape.py", line 582, in merge_with
    raise ValueError("Shapes %s and %s are not compatible" % …