Tensorflow Cloud ML对象检测-分布式训练中的错误

Question

我正在尝试遵循Tensorflow的对象检测教程进行分布式训练，以开发自己的模型，但是使用的代码与存储库中的代码完全相同。

我对本教程进行了一些更改，特别是使用运行时1.5代替了本教程中所说的1.2。 尝试在Google Cloud ML上运行时，没有任何明显的错误（我可以看到），但是该任务无需培训即可迅速退出。

这是我用来开始训练工作的命令：

gcloud ml-engine jobs submit training object_detection_`date +%s`
    --job-dir=gs://test-bucket/training/
    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz
    --module-name object_detection.train
    --region us-central1
    --config ./config.yaml
    --
    --train_dir=gs://test-bucket/data/
    --pipeline_config_path=gs://test-bucket/configs/ssd_inception_v2_coco.config

这是我的config.yaml：

trainingInput:
  runtimeVersion: "1.5"
  scaleTier: CUSTOM
  masterType: complex_model_l
  workerCount: 9
  workerType: standard_gpu
  parameterServerCount: 3
  parameterServerType: large_model

最后，我的工作日志完成了：

I  worker-replica-6 Clean up finished.  worker-replica-6
I  worker-replica-7 Signal 15 (SIGTERM) was caught. Terminated by service. This is normal behavior.  worker-replica-7
I  worker-replica-7 Module completed; cleaning up.  worker-replica-7
I  worker-replica-7 Clean up finished.  worker-replica-7
I  worker-replica-8 Signal 15 (SIGTERM) was caught. Terminated by service. This is normal behavior.  worker-replica-8
I  worker-replica-8 Module completed; cleaning up.  worker-replica-8
I  worker-replica-8 Clean up finished.  worker-replica-8
I  worker-replica-1 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-1
I  worker-replica-1 Signal 15 (SIGTERM) was caught. Terminated by service. This is normal behavior.  worker-replica-1
I  worker-replica-1 Module completed; cleaning up.  worker-replica-1
I  worker-replica-1 Clean up finished.  worker-replica-1
I  worker-replica-7 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-7
I  worker-replica-8 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-8
I  worker-replica-6 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-6
I  worker-replica-3 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-3
I  worker-replica-0 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-0
I  worker-replica-2 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-2
I  worker-replica-5 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-5
I  worker-replica-1 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-1
I  worker-replica-7 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-7
I  worker-replica-8 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-8
I  worker-replica-6 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-6
I  worker-replica-3 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-3
I  worker-replica-0 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-0
I  worker-replica-2 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-2
I  worker-replica-5 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-5
I  worker-replica-1 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-1
I  worker-replica-7 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-7
I  worker-replica-8 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-8
I  worker-replica-6 CreateSession still waiting for response from worker: /job:master/replica:0/task:0  worker-replica-6
I  Finished tearing down TensorFlow. 
I  Job failed.

正如我提到的那样，我无法从日志中获得有用的信息。 再往上走，我会收到此错误： Master init: Unavailable: Stream removed但不确定如何处理。 感谢您朝正确方向的推动！

Answer 1

我转载了您的问题。 我按照以下步骤修复了它：

roysheffi在3个月前对此问题发表了评论。 嗨@pkulzc，我想我可能有领先优势：

在357行上，object_detection / trainer.py调用tf.contrib.slim.learning.train（），它使用了已弃用的 tf.train.Supervisor，并且应迁移到 tf.train.MonitoredTrainingSession中，如tf.train.Supervisor中所述

这已在tensorflow / tensorflow＃15793中请求，并在yahoo / TensorFlowOnSpark＃245的最后评论中被报告为tensorflow / tensorflow＃17852的解决方案。 [ 1 ]

因此，最后，我在trainer.py中完成了此操作：

• 将tf.train.MonitoredTrainingSession(而不是slim.learning.train(