ExpirationError(code=StatusCode.DEADLINE_EXCEEDED, details=“Deadline Exceeded") [英] ExpirationError(code=StatusCode.DEADLINE_EXCEEDED, details="Deadline Exceeded")

问题描述

我正在关注 教程 以使用 tensorflow 服务部署初始模型.我是使用 ubuntu 16.04 和 bazel 13.0.服务器正在运行，可以 ping 服务器.但是当我上传图片时，它显示以下错误

I am following tutorial for deploying the inception model using tensorflow serving.I am using ubuntu 16.04 and bazel 13.0.The server is running am able to ping the server.But when I upload a pic ,It shows the following error

jennings@Jennings:~/serving$ bazel-bin/tensorflow_serving/example/inception_clie                                nt --server=localhost:9000 --image=./Xiang_Xiang_panda.jpg

Traceback (most recent call last):
  File "/home/jennings/serving/bazel-bin/tensorflow_serving/example/inception_client.runfiles/tf_serving/tensorflow_serving/example/inception_client.py", line 56, in <module>
    tf.app.run()
  File "/home/jennings/serving/bazel-bin/tensorflow_serving/example/inception_client.runfiles/org_tensorflow/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "/home/jennings/serving/bazel-bin/tensorflow_serving/example/inception_client.runfiles/tf_serving/tensorflow_serving/example/inception_client.py", line 51, in main
    result = stub.Predict(request, 10.0)  # 10 secs timeout
  File "/home/jennings/.local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py", line 309, in __call__
    self._request_serializer, self._response_deserializer)
  File "/home/jennings/.local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py", line 195, in _blocking_unary_unary
    raise _abortion_error(rpc_error_call)
grpc.framework.interfaces.face.face.ExpirationError: ExpirationError(code=StatusCode.DEADLINE_EXCEEDED, details="Deadline Exceeded")

推荐答案

当 tensorflow 服务客户端无法与服务器进行通信时会发生这种情况.或者这也可能由于网络错误而发生.如果你使用 docker 来托管你的 tensorflow 模型服务器，你需要打开容器中的端口，如下所述，

This happens when the tensorflow serving client is not able to make communication with server. Or this might also occur due to network error. If you are using a docker to host your tensorflow model server, you need to open the port in the container as mentioned below,

docker run --name=tensorflow_container -p 9020:9020 -it $USER/tensorflow-serving-devel

让我知道这是否有效.祝你好运.

Let me know if this works.Have a good one.

这篇关于ExpirationError(code=StatusCode.DEADLINE_EXCEEDED, details=“Deadline Exceeded")的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！