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					 2 changed files with 11 additions and 9 deletions
				
			
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					@ -14,26 +14,28 @@ online_endpoint_name = ("srv-" + AZURE_WORKSPACE_NAME).lower()
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def main():
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					def main():
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    # 1. Create or Load a ML client
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					    # 1. Create or Load a ML client
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    ml_client = XXXX()
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					    ml_client = create_or_load_ml_client()
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    # 2. Create a endpoint
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					    # 2. Create a endpoint
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    print(f"Creating endpoint {online_endpoint_name}")
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					    print(f"Creating endpoint {online_endpoint_name}")
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    endpoint = XXXX(
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					    endpoint = ManagedOnlineEndpoint(
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        name=online_endpoint_name,
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					        name=online_endpoint_name,
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        auth_mode="key",
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					        auth_mode="key",
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    )
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					    )
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    # Method `result()` should be added to wait until completion
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					    # Method `result()` should be added to wait until completion
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    ml_client.online_endpoints.XXXX(endpoint).result()
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					    ml_client.online_endpoints.begin_create_or_update(endpoint).result()
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    # 3. Create a deployment
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					    # 3. Create a deployment
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    best_model_latest_version = XXXX
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					    best_model_latest_version = max(
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					        [int(m.version) for m in ml_client.models.list(name=best_model_name)]
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					    )
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    blue_deployment = XXXX(
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					    blue_deployment = ManagedOnlineDeployment(
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        name=online_endpoint_name,
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					        name=online_endpoint_name,
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        endpoint_name=online_endpoint_name,
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					        endpoint_name=online_endpoint_name,
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        # @latest doesn't work with model paths
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					        # @latest doesn't work with model paths
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        model=XXXX,
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					        model=f"azureml:{best_model_name}:{best_model_latest_version}",
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        instance_type=VM_SIZE,
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					        instance_type=VM_SIZE,
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        instance_count=1,
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					        instance_count=1,
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    )
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					    )
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					@ -7,11 +7,11 @@ from ml_client import create_or_load_ml_client
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def main():
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					def main():
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    # 1. Load a Workspace
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					    # 1. Load a Workspace
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    ml_client = XXXX()
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					    ml_client = create_or_load_ml_client()
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    # 2. Get predictions
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					    # 2. Get predictions
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    output = ml_client.online_endpoints.XXXX(
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					    output = ml_client.online_endpoints.invoke(
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        endpoint_name=XXXX,
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					        endpoint_name=online_endpoint_name,
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        deployment_name=online_endpoint_name,
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					        deployment_name=online_endpoint_name,
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        request_file="./diabetes_test_inference/request.json",
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					        request_file="./diabetes_test_inference/request.json",
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    )
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					    )
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