本文整理汇总了Python中tensorflow.python.pywrap_tensorflow.PyServer_New方法的典型用法代码示例。如果您正苦于以下问题：Python pywrap_tensorflow.PyServer_New方法的具体用法？Python pywrap_tensorflow.PyServer_New怎么用？Python pywrap_tensorflow.PyServer_New使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.pywrap_tensorflow的用法示例。
在下文中一共展示了pywrap_tensorflow.PyServer_New方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tensorflow.python import pywrap_tensorflow [as 别名]
# 或者: from tensorflow.python.pywrap_tensorflow import PyServer_New [as 别名]
def __init__(self,
server_or_cluster_def,
job_name=None,
task_index=None,
protocol=None,
config=None,
start=True):
"""Creates a new server with the given definition.
The `job_name`, `task_index`, and `protocol` arguments are optional, and
override any information provided in `server_or_cluster_def`.
Args:
server_or_cluster_def: A `tf.train.ServerDef` or
`tf.train.ClusterDef` protocol buffer, or a
`tf.train.ClusterSpec` object, describing the server to be
created and/or the cluster of which it is a member.
job_name: (Optional.) Specifies the name of the job of which the server
is a member. Defaults to the value in `server_or_cluster_def`, if
specified.
task_index: (Optional.) Specifies the task index of the server in its
job. Defaults to the value in `server_or_cluster_def`, if specified.
Otherwise defaults to 0 if the server's job has only one task.
protocol: (Optional.) Specifies the protocol to be used by the server.
Acceptable values include `"grpc"`. Defaults to the value in
`server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`.
config: (Options.) A `tf.ConfigProto` that specifies default
configuration options for all sessions that run on this server.
start: (Optional.) Boolean, indicating whether to start the server
after creating it. Defaults to `True`.
Raises:
tf.errors.OpError: Or one of its subclasses if an error occurs while
creating the TensorFlow server.
"""
self._server_def = _make_server_def(server_or_cluster_def,
job_name, task_index, protocol, config)
with errors.raise_exception_on_not_ok_status() as status:
self._server = pywrap_tensorflow.PyServer_New(
self._server_def.SerializeToString(), status)
if start:
self.start()