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When getting explanations for a Vertex AI Model I get the following error message

{
  "error": {
    "code": 400,
    "message": "Endpoint projects/sascha-playground-doit/locations/us-central1/endpoints/519294943752093696 doesn't have traffic_split.",
    "status": "FAILED_PRECONDITION"
  }
}

Model: Vertex AI AutoML Image Classification
Method: XRAY

Code to reproduce

import explainable_ai_sdk
from base64 import b64encode
import tensorflow as tf

instances = []

img_bytes = tf.io.read_file('/content/cast_def_0_1104.jpeg')
b64str = b64encode(img_bytes.numpy()).decode('utf-8')
instances.append({"content": {'b64': b64str}})

remote_model = explainable_ai_sdk.load_model_from_vertex(PROJECT, REGION, ENDPOINT_ID)
#predictions = remote_ig_model.predict(instances)
response = remote_model.explain(instances)

This precondition is limiting us in multiple ways

1. Sometimes there is only one version deployed so enabling splitting is not an option.
2. And even if there are multiple versions we usually enable traffic splitting for a short time and as soon as we are sure the model performs in production well we switch 100% of the traffic to the new model

Looking forward to your feedback

Best regards
Sascha

Following the notebook (just using different data) at GoogleCloudPlatform/ai-platform-samples/blob/master/notebooks/samples/explanations/tf2/ai-explanations-tabular.ipynb
using TF2.2 and 1.0.5 release of explainable_ai_sdk python 3.7. AI Platform Notebook instance.

When calling load_model_from_ai_platform(proj,model,version) the error is raised.
AttributeError: module 'google.auth.credentials' has no attribute 'CredentialsWithQuotaProject'

Not sure if this is due to a mistake I have made or a package dependency.
thanks,
jim

Here is the full error message:

AttributeError Traceback (most recent call last)
in
2 PROJECT_ID = 'jwtfxpipe'
3 print('model {} version {}'.format(MODEL,VERSION))
----> 4 remote_ig_model = explainable_ai_sdk.load_model_from_ai_platform(PROJECT_ID, MODEL, VERSION)
5 ig_response = remote_ig_model.explain([prediction_json])
6 print('OK@',time.asctime())

/opt/conda/lib/python3.7/site-packages/explainable_ai_sdk/model/model_factory.py in load_model_from_ai_platform(project, model, version, credentials)
56 if version:
57 endpoint = os.path.join(endpoint, 'versions', version)
---> 58 return _MODEL_REGISTRY[_REMOTE_MODEL_KEY](endpoint, credentials)
59
60

/opt/conda/lib/python3.7/site-packages/explainable_ai_sdk/model/ai_platform_model.py in init(self, endpoint, credentials)
48 self._credentials = credentials
49 self._endpoint = endpoint
---> 50 self._explanation_metadata = self._get_explanation_metadata()
51 self._modality_input_list_map = utils.get_modality_input_list_map(
52 self._explanation_metadata)

/opt/conda/lib/python3.7/site-packages/explainable_ai_sdk/model/ai_platform_model.py in _get_explanation_metadata(self)
103
104 """
--> 105 explanation_md_uri = self._get_explanation_metadata_uri()
106
107 md = explain_metadata.ExplainMetadata.from_file(explanation_md_uri)

/opt/conda/lib/python3.7/site-packages/explainable_ai_sdk/model/ai_platform_model.py in _get_explanation_metadata_uri(self)
83 gcs bucket uri.
84 """
---> 85 gcs_uri = self._get_deployment_uri()
86 match = re.search('gs://(?P<bucket_name>[^/])[/](?P.*)',
87 gcs_uri)

/opt/conda/lib/python3.7/site-packages/explainable_ai_sdk/model/ai_platform_model.py in _get_deployment_uri(self)
63 """
64 response = http_utils.make_get_request_to_ai_platform(
---> 65 self._endpoint, self._credentials)
66
67 if 'deploymentUri' not in response:

/opt/conda/lib/python3.7/site-packages/explainable_ai_sdk/model/http_utils.py in make_get_request_to_ai_platform(uri_params_str, credentials, timeout_ms)
93 Request results in json format.
94 """
---> 95 headers = _get_request_header(credentials)
96 ai_platform_endpoint = (
97 os.getenv('CLOUDSDK_API_ENDPOINT_OVERRIDES_ML') or

/opt/conda/lib/python3.7/site-packages/explainable_ai_sdk/model/http_utils.py in _get_request_header(credentials)
40 # If credentials is not given, use the default credentials
41 if credentials is None:
---> 42 credentials, _ = google.auth.default()
43
44 # Refresh credentials in case it has been expired.

/opt/conda/lib/python3.7/site-packages/google/auth/_default.py in default(scopes, request)
306 scopes (Sequence[str]): The list of scopes for the credentials. If
307 specified, the credentials will automatically be scoped if
--> 308 necessary.
309 request (google.auth.transport.Request): An object used to make
310 HTTP requests. This is used to detect whether the application

/opt/conda/lib/python3.7/site-packages/google/auth/_default.py in _get_gae_credentials()
178 explicit_file = os.environ.get(environment_vars.CREDENTIALS)
179
--> 180 _LOGGER.debug(
181 "Checking %s for explicit credentials as part of auth process...", explicit_file
182 )

/opt/conda/lib/python3.7/site-packages/google/auth/app_engine.py in
79
80 class Credentials(
---> 81 credentials.Scoped, credentials.Signing, credentials.CredentialsWithQuotaProject
82 ):
83 """App Engine standard environment credentials.

AttributeError: module 'google.auth.credentials' has no attribute 'CredentialsWithQuotaProject'

Can we use this service for pytorch models ?

Hello Google Team,

explanations[0].visualize_attributions() is not rendering the visualizations see

Model is a AutoML Image Classification
Method configured as XRAI

Code for reproduction
https://colab.research.google.com/drive/1tpaX9GieVznkEUUMbFYIMI4UiM7AhzgE#scrollTo=jnI17hENqyxO&uniqifier=1

Best regards
Sascha