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HttpResponseError when deploying a model to an endpoint #1890

Closed monajalal closed 1 year ago

monajalal commented 1 year ago

I am trying to deploy a model to the endpoint. Code is from https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/single-step/pytorch/train-hyperparameter-tune-deploy-with-pytorch

Here's the error I get:

from azure.ai.ml.entities import (
    ManagedOnlineDeployment,
    Model,
    Environment,
    CodeConfiguration,
)

online_deployment_name = "aci-blue"

# create an online deployment.
blue_deployment = ManagedOnlineDeployment(
    name=online_deployment_name,
    endpoint_name=online_endpoint_name,
    model=model,
    environment=curated_env_name,
    code_configuration=CodeConfiguration(code="./src/", scoring_script="score.py"),
    #instance_type="Standard_NC6s_v3",
    instance_type="STANDARD_NC6",
    instance_count=1,
)

blue_deployment = ml_client.begin_create_or_update(blue_deployment).result()
Check: endpoint aci-birds-endpoint-11c92082 exists
data_collector is not a known attribute of class <class 'azure.ai.ml._restclient.v2022_02_01_preview.models._models_py3.ManagedOnlineDeployment'> and will be ignored
Output exceeds the [size limit](command:workbench.action.openSettings?[). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?e85de1c2-7208-41f6-acd7-8048e230d6b8)
---------------------------------------------------------------------------
HttpResponseError                         Traceback (most recent call last)
/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb Cell 45 in <cell line: 22>()
     [10](vscode-notebook-cell://amlext%2B2f737562736372697074696f6e732f39626531333637612d626363392d343237352d386233642d6130343639663431313966612f7265736f7572636547726f7570732f6172642d6d6c2f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f4d4c6f70732d546573742f636f6d70757465732f6d6f6e612d7375626d69742d6e6f6465/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb#X60sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9) # create an online deployment.
     [11](vscode-notebook-cell://amlext%2B2f737562736372697074696f6e732f39626531333637612d626363392d343237352d386233642d6130343639663431313966612f7265736f7572636547726f7570732f6172642d6d6c2f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f4d4c6f70732d546573742f636f6d70757465732f6d6f6e612d7375626d69742d6e6f6465/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb#X60sdnNjb2RlLXJlbW90ZQ%3D%3D?line=10) blue_deployment = ManagedOnlineDeployment(
     [12](vscode-notebook-cell://amlext%2B2f737562736372697074696f6e732f39626531333637612d626363392d343237352d386233642d6130343639663431313966612f7265736f7572636547726f7570732f6172642d6d6c2f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f4d4c6f70732d546573742f636f6d70757465732f6d6f6e612d7375626d69742d6e6f6465/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb#X60sdnNjb2RlLXJlbW90ZQ%3D%3D?line=11)     name=online_deployment_name,
     [13](vscode-notebook-cell://amlext%2B2f737562736372697074696f6e732f39626531333637612d626363392d343237352d386233642d6130343639663431313966612f7265736f7572636547726f7570732f6172642d6d6c2f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f4d4c6f70732d546573742f636f6d70757465732f6d6f6e612d7375626d69742d6e6f6465/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb#X60sdnNjb2RlLXJlbW90ZQ%3D%3D?line=12)     endpoint_name=online_endpoint_name,
   (...)
     [19](vscode-notebook-cell://amlext%2B2f737562736372697074696f6e732f39626531333637612d626363392d343237352d386233642d6130343639663431313966612f7265736f7572636547726f7570732f6172642d6d6c2f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f4d4c6f70732d546573742f636f6d70757465732f6d6f6e612d7375626d69742d6e6f6465/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb#X60sdnNjb2RlLXJlbW90ZQ%3D%3D?line=18)     instance_count=1,
     [20](vscode-notebook-cell://amlext%2B2f737562736372697074696f6e732f39626531333637612d626363392d343237352d386233642d6130343639663431313966612f7265736f7572636547726f7570732f6172642d6d6c2f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f4d4c6f70732d546573742f636f6d70757465732f6d6f6e612d7375626d69742d6e6f6465/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb#X60sdnNjb2RlLXJlbW90ZQ%3D%3D?line=19) )
---> [22](vscode-notebook-cell://amlext%2B2f737562736372697074696f6e732f39626531333637612d626363392d343237352d386233642d6130343639663431313966612f7265736f7572636547726f7570732f6172642d6d6c2f70726f7669646572732f4d6963726f736f66742e4d616368696e654c6561726e696e6753657276696365732f776f726b7370616365732f4d4c6f70732d546573742f636f6d70757465732f6d6f6e612d7375626d69742d6e6f6465/home/azureuser/cloudfiles/code/Users/Mona.Jalal/research/azure_hyperparamater_tuning/train-hyperparameter-tune-deploy-with-pytorch.ipynb#X60sdnNjb2RlLXJlbW90ZQ%3D%3D?line=21) blue_deployment = ml_client.begin_create_or_update(blue_deployment).result()

File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/azure/ai/ml/_ml_client.py:841, in MLClient.begin_create_or_update(self, entity, **kwargs)
    814 def begin_create_or_update(
    815     self,
    816     entity: R,
    817     **kwargs,
    818 ) -> LROPoller[R]:
    819     """Creates or updates an Azure ML resource asynchronously.
    820 
    821     :param entity: The resource to create or update.
   (...)
    838         azure.ai.ml.entities.JobSchedule]]
    839     """
--> 841     return _begin_create_or_update(entity, self._operation_container.all_operations, **kwargs)
...
    "value": "eastus2"
}Type: Time
Info: {
    "value": "2023-03-16T20:39:25.1734285+00:00"
}

I am not sure what is wrong and how it should be fixed since I am new to Azure Endpoint.

This is part of the code in train-hyperparameter-tune-deploy-with-pytorch.ipynb and the hyperparameter tuning experiment is already finished and best model is selected.