python - Google Cloud Platform - AI Platform: why do I get different response body when calling API?

Question

I created 2 models on Google Cloud AI Platform and I am wondering why do I get different response body when calling REST API with Python?
To be more specific:

• In the first case, I get 2 dictionaries (keys: "predictions" and "dense_1", the latter is the output layer name of my tensorflow model)
  {'predictions': [{'dense_1': [9.130606807519459e-23, 4.872276949885089e-23, 0.002939987927675247, 0.957423210144043, 0.0, 7.103511528994133e-11, 6.0420668887672946e-05, 0.039576299488544464, 3.989315388447379e-12, 8.409963248741903e-32]}]}
• In the second case, I get 1 dictionary (key: "predictions").
  {'predictions': [[9.13060681e-23, 4.87227695e-23, 0.00293998793, 0.95742321, 0.0, 7.10351153e-11, 6.04206689e-05, 0.0395763, 3.98931539e-12, 8.40996325e-32]]}

This is weird because I am using the exact same model from GCS. The only difference between those 2 models is that the second one has a region endpoint in Europe and they don't run on same machine type (but I don't think there is a link with my issue).

EDIT : Here is my request method. I used regional_endpoint = None in case 1 and regional_endpoint = "europe-west1" in case 2

project_id = "my_project_id"
model_id = "my_model_id"
version_id = None # if None, default version is used
regional_endpoint = None # "europe-west1"

def predict(project, model, instances, version=None, regional_endpoint=None):
'''
Send JSON data to a deployed model for prediction.
Args:
    - project (str): Project ID where the AI Platform model is deployed
    - model (str): Model ID
    - instances (tensor): model's expected inputs
    - version (str): Optional. Version ID
    - regional_endpoint (str): Optional. See https://cloud.google.com/dataflow/docs/concepts/regional-endpoints
Returns:
    - dictionary of prediction results

'''
input_data_json = {"signature_name": "serving_default", "instances": instances.tolist()}

model_path = "projects/{}/models/{}".format(project_id, model_id)
if version is not None:
    model_path += "/versions/{}".format(version)

if regional_endpoint is not None:
    endpoint = 'https://{}-ml.googleapis.com'.format(regional_endpoint)
    regional_endpoint = ClientOptions(api_endpoint=endpoint)

ml_ressource = googleapiclient.discovery.build("ml", "v1", client_options=regional_endpoint).projects()

request = ml_ressource.predict(name=model_path, body=input_data_json)
response = request.execute()

if "error" in response:
    raise RuntimeError(response["error"])

return response["predictions"]

I get the same result using gcloud command:

$ gcloud ai-platform predict --model=my_model_id --json-request=data.json --region=europe-west1
Using endpoint [https://europe-west1-ml.googleapis.com/]
[[5.64439188e-06, 1.11136234e-09, 4.66703168e-05, 1.34729596e-08, 2.34136132e-05, 1.52856941e-07, 0.999924064, 3.328397e-10, 3.32789263e-08, 3.37864092e-09]]

$ gcloud ai-platform predict --model=my_model_id --json-request=data.json
Using endpoint [https://ml.googleapis.com/]
DENSE_1
[5.644391876558075e-06, 1.1113623354930269e-09, 4.6670316805830225e-05, 1.3472959636828818e-08, 2.341361323487945e-05, 1.528569413267178e-07, 0.9999240636825562, 3.328397002455574e-10, 3.327892628135487e-08, 3.378640922591103e-09]

question from:https://stackoverflow.com/questions/66060254/google-cloud-platform-ai-platform-why-do-i-get-different-response-body-when-c

Answer 1

I have reproduced the same behavior.
From the list of endpoints, I have already tested the following:

• europe-west1
• asia-east1
• us-east1
• australia-southeast1

And neither of them returns the output layer’s name like the global endpoint does.

I have already informed the AI Platform product team of this behavior and created a public issue on issuetracker to track their progress.
Therefore, I suggest that all future communication about it should be done on issuetracker.