Watson machine learning deployment takes too much time

Question

I trained a model using watson machine learning service. The training process has completed so I ran these command lines to deploy it:

bx ml store training-runs model-XXXXXXX

I get the output with the model ID

Starting to store the training-run 'model-XXXXXX' ...
OK
Model store successful. Model-ID is '93sdsdsf05-3ea4-4d9e-a751-5bcfbsdsd3391'.

Then I use the following to deploy it :

bx ml deploy 93sdsdsf05-3ea4-4d9e-a751-5bcfbsdsd3391 "my-test-model"

The problem is that I'm getting an endless message saying:

Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...

When I check in COS result bucket the model size is ~25MB so it shouldn't be that long to deploy. Am I missing something here ?

Answer 1

Deploying the same model using Python Client API :

from watson_machine_learning_client import WatsonMachineLearningAPIClient

client = WatsonMachineLearningAPIClient(wml_credentials)
deployment_details = client.deployments.create( model_id, "model_name")

This showed me very quickly that there is an error with the deployment. The strange thing is that the error doesn't pop up when deploying with command line interface (CLI) .