在云计算机学习引擎上运行时，Tensorflow对象检测train.py失败

Question

我有一个在本地工作的tensorflow对象检测API的小实例。一切看起来不错。我的目标是使用他们的脚本在Google机器学习引擎中运行，我之前已经广泛使用它。我正在关注这些docs。

声明一些相关的变量

declare PROJECT=$(gcloud config list project --format "value(core.project)") 
declare BUCKET="gs://${PROJECT}-ml" 
declare MODEL_NAME="DeepMeerkatDetection" 
declare FOLDER="${BUCKET}/${MODEL_NAME}" 
declare JOB_ID="${MODEL_NAME}_$(date +%Y%m%d_%H%M%S)" 
declare TRAIN_DIR="${FOLDER}/${JOB_ID}" 
declare EVAL_DIR="${BUCKET}/${MODEL_NAME}/${JOB_ID}_eval" 
declare PIPELINE_CONFIG_PATH="${FOLDER}/faster_rcnn_inception_resnet_v2_atrous_coco_cloud.config" 
declare PIPELINE_YAML="/Users/Ben/Documents/DeepMeerkat/training/Detection/cloud.yml" 

我YAML看起来像

trainingInput: 
    runtimeVersion: "1.0" 
    scaleTier: CUSTOM 
    masterType: standard_gpu 
    workerCount: 5 
    workerType: standard_gpu 
    parameterServerCount: 3 
    parameterServerType: standard 

相关的路径在配置设置，如

fine_tune_checkpoint: "gs://api-project-773889352370-ml/DeepMeerkatDetection/checkpoint/faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017/model.ckpt" 

我已经打包的对象检测和苗条使用setup.py

运行

gcloud ml-engine jobs submit training "${JOB_ID}_train" \ 
    --job-dir=${TRAIN_DIR} \ 
    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \ 
    --module-name object_detection.train \ 
    --region us-central1 \ 
    --config ${PIPELINE_YAML} \ 
    -- \ 
    --train_dir=${TRAIN_DIR} \ 
    --pipeline_config_path= ${PIPELINE_CONFIG_PATH} 

产生一个tensorflow（进口？）错误。它有点神秘

insertId: "1inuq6gg27fxnkc" 
logName: "projects/api-project-773889352370/logs/ml.googleapis.com%2FDeepMeerkatDetection_20171017_141321_train" 
receiveTimestamp: "2017-10-17T21:38:34.435293164Z" 
resource: {…} 
severity: "ERROR" 
textPayload: "The replica ps 0 exited with a non-zero status of 1. Termination reason: Error. 
Traceback (most recent call last): 
    File "/usr/lib/python2.7/runpy.py", line 162, in _run_module_as_main 
    "__main__", fname, loader, pkg_name) 
    File "/usr/lib/python2.7/runpy.py", line 72, in _run_code 
    exec code in run_globals 
    File "/root/.local/lib/python2.7/site-packages/object_detection/train.py", line 198, in <module> 
    tf.app.run() 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 44, in run 
    _sys.exit(main(_sys.argv[:1] + flags_passthrough)) 
    File "/root/.local/lib/python2.7/site-packages/object_detection/train.py", line 145, in main 
    model_config, train_config, input_config = get_configs_from_multiple_files() 
    File "/root/.local/lib/python2.7/site-packages/object_detection/train.py", line 127, in get_configs_from_multiple_files 
    text_format.Merge(f.read(), train_config) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/lib/io/file_io.py", line 112, in read 
    return pywrap_tensorflow.ReadFromStream(self._read_buf, length, status) 
    File "/usr/lib/python2.7/contextlib.py", line 24, in __exit__ 
    self.gen.next() 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status 
    pywrap_tensorflow.TF_GetCode(status)) 
FailedPreconditionError: . 

我已经看到了有关预测的机器学习引擎等questions这个错误，可能提示这个错误（？）不直接与对象相关的检测代码，但感觉像它没有正确打包，缺少依赖关系？我已将我的gcloud更新到最新版本。

Bens-MacBook-Pro:research ben$ gcloud --version 
Google Cloud SDK 175.0.0 
bq 2.0.27 
core 2017.10.09 
gcloud 
gsutil 4.27 

很难看到其相关的在这里

FailedPreconditionError when running TF Object Detection API with own model

这个问题为什么代码需要在云初始化不同？

更新＃1。

好奇的是，eval.py工作正常，所以它不能成为配置文件的路径，或train.py和eval.py共享的任何路径。 Eval.py耐心地坐在并等待创建模型检查点。

另一个想法可能是，检查点莫名其妙地被上传过程中被损坏。我们可以从头开始测试绕过和训练。

在的.config

from_detection_checkpoint: false 

其产生的相同的前提条件的错误，所以它不能被该模型。

Answer 1

根本原因是轻微的错字：

--pipeline_config_path= ${PIPELINE_CONFIG_PATH} 

有一个额外的空间。试试这个：

gcloud ml-engine jobs submit training "${JOB_ID}_train" \ 
    --job-dir=${TRAIN_DIR} \ 
    --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz \ 
    --module-name object_detection.train \ 
    --region us-central1 \ 
    --config ${PIPELINE_YAML} \ 
    -- \ 
    --train_dir=${TRAIN_DIR} \ 
    --pipeline_config_path=${PIPELINE_CONFIG_PATH}