how create azure machine learning scoring image using local package

Question

I have pkl package saved in my azure devops repository

using below code it searches for package in workspace. How to provide package saved in repository

 ws = Workspace.get(
         name=workspace_name,
         subscription_id=subscription_id,
        resource_group=resource_group,
        auth=cli_auth)

image_config = ContainerImage.image_configuration(
    execution_script="score.py",
    runtime="python-slim",
    conda_file="conda.yml",
    description="Image with ridge regression model",
    tags={"area": "ml", "type": "dev"},
)

image = Image.create(
    name=image_name,  models=[model], image_config=image_config, workspace=ws
)

image.wait_for_creation(show_output=True)

if image.creation_state != "Succeeded":
    raise Exception("Image creation status: {image.creation_state}")

print(
    "{}(v.{} [{}]) stored at {} with build log {}".format(
        image.name,
        image.version,
        image.creation_state,
        image.image_location,
        image.image_build_log_uri,
    )
)

# Writing the image details to /aml_config/image.json
image_json = {}
image_json["image_name"] = image.name
image_json["image_version"] = image.version
image_json["image_location"] = image.image_location
with open("aml_config/image.json", "w") as outfile:
    json.dump(image_json, outfile)

I tried to provide path to models but its fails saying package not found

models = $(System.DefaultWorkingDirectory)/package_model.pkl

Answer 1

Register model: Register a file or folder as a model by calling Model.register().

In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace.

model = Model.register(workspace=ws,
                       model_name='',                # Name of the registered model in your workspace.
                       model_path='',  # Local file to upload and register as a model.
                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.
                       model_framework_version=sklearn.__version__,  # Version of scikit-learn used to create the model.
                       sample_input_dataset=input_dataset,
                       sample_output_dataset=output_dataset,
                       resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),
                       description='Ridge regression model to predict diabetes progression.',
                       tags={'area': 'diabetes', 'type': 'regression'})

print('Name:', model.name)
print('Version:', model.version)

Deploy machine learning models to Azure: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where?tabs=python

To Troubleshooting remote model deployment Please follow the document.