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Cloud Integration

When using the module users can store, retrieve and delete models from the following cloud provider storage solutions:

  1. AWS S3 Buckets
  2. Azure Blob Storage

This interaction is facilitated through interactions with these vendors using REST calls.

From a users perspective this is intended to be a lightweight API with users requiring minimal configuration to publish to a registry on-prem or within a cloud storage solution.

Prior to running any of the function calls to publish/retrieve from the cloud, a user must generate a storage bucket to which artefacts will be published. At present this must be done external to the functionality provided by this interface i.e. if the bucket does not exist we will not generate it.

Once the user has a storage bucket available they can run the following examples from any application running this library.

The following sections outline how users can initialise this functionality and provides examples of function calls that are supported when using the registry in the context of a cloud vendor solution.

Required environment variables

Interactions with the various cloud vendors require the definition of environment variables to authenticate REST calls to the vendors, the following outlines the required arguments for each of the supported vendors within the docker image used.

Vendor Environment variable

Interacting with cloud vendors

Users can change the registry they wish to use within their Python process. This is facilitated through modification of the folder_path variable within the various functions. For one off calls to a different registry, users can pass in a dictionary containing the vendor type and location to their desired function as the folder_path argument.

For example of creating a registry within various cloud vendors:

# Import and initialise the library
>>> from kxpy import q
>>> from kxi import ml
>>> ml.init()

# Add a new registry to the local "/tmp" directory

# Add a new registry to a defined AWS s3 Bucket
>>>[{'aws': 's3://my-test-bucket'})


Once initialised following the instructions presented above a user can run the following examples

  • Add items to cloud storage

    >>> py_dict = {'aws': 's3://my-test-bucket'}
    >>>, 'test');
    >>> ml.registry.set.model(q('{x}'), 'mymodel', 'q', folder_path=py_dict, experiment_name='test')

  • Log ancillary information associated with a model

    >>> ml.registry.log.metric('metric', 2.0, py_dict)
    >>> ml.registry.set.parameters('paramfile', {'param1': 1, 'param2': 2}, folder_path=pydict, model_name='my_model', version=[1, 0])

  • Retrieve items from the registry

    >>> ml.registry.get.model(py_dict, None, 'mymodel', [1, 0])
    modelInfo| `registry`model`monitoring!(`description`modelInformation`experime..
    model    | {x}
    >>> ml.registry.get.metric(py_dict, param='metric')
    timestamp                     metricName metricValue
    2021.04.29D12:24:23.117795000 metric     2
    >>> ml.registry.get.parameters('paramFile', py_dict)
    param1| 1
    param2| 2

  • Delete the registry