tensorforce/models/model.py
def start_server(self):
        """
        Creates and stores a tf server (and optionally joins it if we are a parameter-server).
        Only relevant, if we are running in distributed mode.
        """
        self.server = tf.train.Server(
            server_or_cluster_def=self.distributed_spec["cluster_spec"],
            job_name=self.distributed_spec["job"],
            task_index=self.distributed_spec["task_index"],
            protocol=self.distributed_spec.get("protocol"),
            config=self.distributed_spec.get("session_config"),
            start=True
        )
        if self.distributed_spec["job"] == "ps":
            self.server.join()
            # This is unreachable?
            quit()
def start_server(self):
        """
        Creates and stores a tf server (and optionally joins it if we are a parameter-server).
        Only relevant, if we are running in distributed mode.
        """
        self.server = tf.train.Server(
            server_or_cluster_def=self.distributed_spec["cluster_spec"],
            job_name=self.distributed_spec["job"],
            task_index=self.distributed_spec["task_index"],
            protocol=self.distributed_spec.get("protocol"),
            config=self.distributed_spec.get("session_config"),
            start=True
        )
        if self.distributed_spec["job"] == "ps":
            self.server.join()
            # This is unreachable?
            quit()
Creates and stores a tf server (and optionally joins it if we are a parameter-server).
        Only relevant, if we are running in distributed mode.