Azure ML Workbench Kubernetes部署失败

我正在尝试使用本教程中使用群集模式的ML Workbench流程将预测Web服务部署到Azure（https://docs.microsoft.com/en-us/azure/machine-learning/preview/tutorial-classifying-iris-part-3#prepare-to-operationalize-locally）

模型将被发送到清单，评分脚本和架构

创建服务............................................... ...........发生错误：{'错误'：{'代码'：'KubernetesDeploymentFailed'，'详情'：[{'消息'：'退避40s重启失败的容器= ... pod = ...'，'Code'：'CrashLoopBackOff'}]，'StatusCode'：400，'Message'：'Kubernetes Deployment failed'}，'OperationType'：'Service'，'State'：'Failed'， 'Id'：'...'，'ResourceLocation'：'/ api / subscriptions / ...'，'CreatedTime'：'2017-10-26T20：30：49.77362Z'，'EndTime'：'2017-10 -26T20：36：40.186369Z'}

以下是检查ml服务实时日志的结果

C:\Users\userguy\Documents\azure_ml_workbench\projecto>az ml service logs realtime -i projecto
2017-10-26 20:47:16,118 CRIT Supervisor running as root (no user in config file)
2017-10-26 20:47:16,120 INFO supervisord started with pid 1
2017-10-26 20:47:17,123 INFO spawned: 'rsyslog' with pid 9
2017-10-26 20:47:17,124 INFO spawned: 'program_exit' with pid 10
2017-10-26 20:47:17,124 INFO spawned: 'nginx' with pid 11
2017-10-26 20:47:17,125 INFO spawned: 'gunicorn' with pid 12
2017-10-26 20:47:18,160 INFO success: rsyslog entered RUNNING state, process has stayed up for > than 1 seconds (startsecs)
2017-10-26 20:47:18,160 INFO success: program_exit entered RUNNING state, process has stayed up for > than 1 seconds (startsecs)
2017-10-26 20:47:22,164 INFO success: nginx entered RUNNING state, process has stayed up for > than 5 seconds (startsecs)
2017-10-26T20:47:22.519159Z, INFO, 00000000-0000-0000-0000-000000000000, , Starting gunicorn 19.6.0
2017-10-26T20:47:22.520097Z, INFO, 00000000-0000-0000-0000-000000000000, , Listening at: http://127.0.0.1:9090 (12)
2017-10-26T20:47:22.520375Z, INFO, 00000000-0000-0000-0000-000000000000, , Using worker: sync
2017-10-26T20:47:22.521757Z, INFO, 00000000-0000-0000-0000-000000000000, , worker timeout is set to 300
2017-10-26T20:47:22.522646Z, INFO, 00000000-0000-0000-0000-000000000000, , Booting worker with pid: 22
2017-10-26 20:47:27,669 WARN received SIGTERM indicating exit request
2017-10-26 20:47:27,669 INFO waiting for nginx, gunicorn, rsyslog, program_exit to die
2017-10-26T20:47:27.669556Z, INFO, 00000000-0000-0000-0000-000000000000, , Handling signal: term
2017-10-26 20:47:30,673 INFO waiting for nginx, gunicorn, rsyslog, program_exit to die
2017-10-26 20:47:33,675 INFO waiting for nginx, gunicorn, rsyslog, program_exit to die
Initializing logger
2017-10-26T20:47:36.564469Z, INFO, 00000000-0000-0000-0000-000000000000, , Starting up app insights client
2017-10-26T20:47:36.564991Z, INFO, 00000000-0000-0000-0000-000000000000, , Starting up request id generator
2017-10-26T20:47:36.565316Z, INFO, 00000000-0000-0000-0000-000000000000, , Starting up app insight hooks
2017-10-26T20:47:36.565642Z, INFO, 00000000-0000-0000-0000-000000000000, , Invoking user's init function
2017-10-26 20:47:36.715933: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instruc
tions, but these are available on your machine and could speed up CPU computations.
2017-10-26 20:47:36,716 INFO waiting for nginx, gunicorn, rsyslog, program_exit to die
2017-10-26 20:47:36.716376: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instruc
tions, but these are available on your machine and could speed up CPU computations.
2017-10-26 20:47:36.716542: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructio
ns, but these are available on your machine and could speed up CPU computations.
2017-10-26 20:47:36.716703: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructi
ons, but these are available on your machine and could speed up CPU computations.
2017-10-26 20:47:36.716860: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructio
ns, but these are available on your machine and could speed up CPU computations.
this is the init
2017-10-26T20:47:37.551940Z, INFO, 00000000-0000-0000-0000-000000000000, , Users's init has completed successfully
Using TensorFlow backend.
2017-10-26T20:47:37.553751Z, INFO, 00000000-0000-0000-0000-000000000000, , Worker exiting (pid: 22)
2017-10-26T20:47:37.885303Z, INFO, 00000000-0000-0000-0000-000000000000, , Shutting down: Master
2017-10-26 20:47:37,885 WARN killing 'gunicorn' (12) with SIGKILL
2017-10-26 20:47:37,886 INFO stopped: gunicorn (terminated by SIGKILL)
2017-10-26 20:47:37,889 INFO stopped: nginx (exit status 0)
2017-10-26 20:47:37,890 INFO stopped: program_exit (terminated by SIGTERM)
2017-10-26 20:47:37,891 INFO stopped: rsyslog (exit status 0)

Received 41 lines of log

我最好的猜测是发生了一些无声的事情导致“WARN收到SIGTERM指示退出请求” . scoring.py脚本的其余部分似乎开始了 - 请参阅tensorflow get init和“this is the init”print语句 .

http://127.0.0.1:63437可从我的本地计算机访问，但ui endpoints 为空 .

有关如何在Azure群集中启动并运行的任何想法？我不太熟悉Kubernetes的工作原理，所以任何基本的调试指导都会受到赞赏 .