在ML引擎上进行训练后，获取save_model.pb的路径

Question

I have been using the python client API of ML engine to create training jobs of some canned estimators. What I'm not able to do is get the path of the saved_model.pb on GCS because the path it is stored in has a timestamp as a dir name. Is there anyway I can get this using a regular expression or something on python client, so that I'll be able to deploy the model with correct path.

The path seems to be in this format right now -

gs://bucket_name/outputs/export/serv/ timestamp /saved_model.pb

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UPDATE

Thanks shahin for the answer. So I wrote this, which gives me the exact path that I can pass to the deploy_uri for ml engine.

from google.cloud import storage

def getGCSPath(prefix):
    storage_client = storage.Client()
    bucket = storage_client.get_bucket(bucket_name)
    mlist = bucket.list_blobs(prefix=prefix)
    for line in mlist:
        if 'saved_model.pb' in line.name:
            return line.name[:-14]

# print getGCSPath('output/export/serv/')

Answer 1

Use gsutil and tail:

MODEL_LOCATION=$(gsutil ls gs://${BUCKET}/outputs/export/serv | tail -1)
gcloud ml-engine models create ${MODEL_NAME} --regions $REGION
gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version $TFVERSION

Answer 2

import os
import cloudstorage as gcs
bucket = os.environ.get('BUCKET')
page_size = 1
stats = gcs.listbucket(bucket + '/outputs/export/serv', max_keys=page_size)