Method set_server_def

tensorflow/python/eager/context.py:516–547

Allow setting a server_def on the context. When a server def is replaced, it effectively clears a bunch of caches within the context. If you attempt to use a tensor object that was pointing to a tensor on the remote device, it will raise an error. Args: server_def: A tensorfl

(self, server_def, keep_alive_secs=600)

    pywrap_tensorflow.TFE_ClearScalarCache()

  def set_server_def(self, server_def, keep_alive_secs=600):
    """Allow setting a server_def on the context.

    When a server def is replaced, it effectively clears a bunch of caches
    within the context. If you attempt to use a tensor object that was pointing
    to a tensor on the remote device, it will raise an error.

    Args:
      server_def: A tensorflow::ServerDef proto.
        Enables execution on remote devices.
      keep_alive_secs: Num. seconds after which the remote end will hang up.
        As long as the client is still alive, the server state for the context
        will be kept alive. If the client is killed (or there is some failure),
        the server will clean up its context keep_alive_secs after the final RPC
        it receives.

    Raises:
      ValueError: if server_def is None.
    """
    if not server_def:
      raise ValueError("server_def is None.")

    self._server_def = server_def

    if self._context_handle:
      server_def_str = server_def.SerializeToString()
      pywrap_tensorflow.TFE_ContextSetServerDef(self._context_handle,
                                                keep_alive_secs, server_def_str)
      self._initialize_logical_devices()

    # Clear all the caches in case there are remote tensors in them.
    self._clear_caches()

  def enable_collective_ops(self, server_def):
    """Enable distributed collective ops with an appropriate server_def.