Error when doing the Tensorflow tutorial

Question

I follow this tutorial to do the training on Google cloud ml-engine. I follow it step by step but I am facing error when submit the ml job to cloud. I ran this command.

sam@sam-VirtualBox:~/models/research$ gcloud ml-engine jobs submit training whoami_object_detection_date +%s --job-dir=gs://tf_testing/train --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz --module-name object_detection.train --region us-central1 --config object_detection/samples/cloud/cloud.yml -- --train_dir=gs://tf_testing/train --pipeline_config_path=gs://tf_testing/data/faster_rcnn_resnet101_pets.config

and I got this error.

ERROR: (gcloud.ml-engine.jobs.submit.training) FAILED_PRECONDITION: Field: package_uris Error: The provided GCS paths [gs://tf_testing/train/packages/8ec87a281aadb58d3d82462bbffafa9d7e521cc03025209704bc643eb9f3bc37/slim-0.1.tar.gz, gs://tf_testing/train/packages/8ec87a281aadb58d3d82462bbffafa9d7e521cc03025209704bc643eb9f3bc37/object_detection-0.1.tar.gz] cannot be read by service account [email protected]. - '@type': type.googleapis.com/google.rpc.BadRequest fieldViolations: - description: The provided GCS paths [gs://tf_testing/train/packages/8ec87a281aadb58d3d82462bbffafa9d7e521cc03025209704bc643eb9f3bc37/slim-0.1.tar.gz, gs://tf_testing/train/packages/8ec87a281aadb58d3d82462bbffafa9d7e521cc03025209704bc643eb9f3bc37/object_detection-0.1.tar.gz] cannot be read by service account [email protected]. field: package_uris

I saw this post and this post and tried the solution but it did not help. FYI, I did not change PATH_TO_BE_CONFIGURED when ran this command. Could it be the reason?

sed -i "s|PATH_TO_BE_CONFIGURED|"gs://${YOUR_GCS_BUCKET}"/data|g" \ object_detection/samples/configs/faster_rcnn_resnet101_pets.config

Answer 1

You need to to allow the service account to read/write to your bucket:

gsutil acl ch -u $SVCACCT:WRITE gs://$BUCKET/

gsutil defacl ch -u $SVCACCT:O gs://$BUCKET/

Alternately:

gcloud ml-engine init-project

Will add the service account as an editor on the project. Make sure to do this in the project that owns the bucket