snorkelflow.sdk.MLflowDeployment

class snorkelflow.sdk.MLflowDeployment(deployment_uid, **kwargs)

Bases: Deployment

MLflowDeployment class represents a deployment of a Snorkel Flow application as an MLflow model. This class inherits from the Deployment class and provides additional functionality specific to MLflow deployments. Since MLflow is the only supported deployment type at the moment, this class is the only implementation of the Deployment class. This class is intended to be an interface between Snorkel Flow and MLflow.

A typical workflow for deploying an application to production is as follows:

from snorkelflow.sdk import MLflowDeployment

deployment = MLflowDeployment.create("my-application", "my-deployment")
deployment.execute(df)
deployment.download('/path/to/download')

Follow with MLflow client calls to deploy your workflow to production.

If you want to get a deployment by its name, you can use the following code:

from snorkelflow.sdk import MLflowDeployment

deployments = MLflowDeployment.list(application="my-application")
matched_deployment = next((deployment for deployment in deployments if deployment.name == "my-deployment"), None)
if matched_deployment is None:
    print("Deployment not found")

\_\_init\_\_

__init__

__init__(deployment_uid, **kwargs)

Initializes a Deployment object. This constructor should not be called directly. Instead, use the Deployment.create method to create a deployment.

Parameters

Parameters

Name	Type	Default	Info
deployment_uid	int		The unique identifier of the deployment.

Methods

__init__(deployment_uid, **kwargs)	Initializes a Deployment object.
create(application[, name, registry_type, ...])	Create a new MLflow deployment from an application.
delete(deployment_uid)	Delete a deployment based on the provided identifier.
download(local_path[, download_as_zip, ...])	Download an MLflow deployment to the specified local path.
execute(df)	Execute a deployment on a pandas DataFrame.
get(deployment_uid)	Fetches a Deployment object based on the provided identifier.
list([application])	List all deployments for an application.
register(tracking_server_uri, *[, ...])	Register an MLflowModel Deployment with an external MLflow model registry.
update(name)	Update the metadata for the deployment.

create

create

classmethod create(application, name=None, registry_type='INTERNAL', model_registry_uid=None, signature=True, experiment_name=None)

Create a new MLflow deployment from an application.

Examples

>>> from snorkelflow.sdk import MLflowDeployment
>>> my_deployment = MLflowDeployment.create(application="my-app", name="my-deployment")
{'deployment_uid': <deployment_uid>}
>>> my_deployment.name
"my-deployment"

Parameters

Parameters

Name	Type	Default	Info
application	Union[str, int]		The name or UID of the application to be exported.
name	Optional[str]	None	The name of the deployment. If this is not provided, deployment name will be created using the application name. Deployment names are not unique and re-running this method with the same name will create a new deployment. To register a model to Databricks Unity Catalog, the name should be in the form of “<catalog>.<schema>.<model>”.
registry_type	str	'INTERNAL'	The type of the model registry, “INTERNAL” or “EXTERNAL”.
model_registry_uid	Optional[int]	None	The UID of the external model registry. If registry_type is “INTERNAL”, model_registry_uid is not required.
signature	bool	True	Whether to add a signature for the input_schema in the MLmodel yaml file. If True, a signature will be added. If False, no signature will be added.
experiment_name	Optional[str]	None	The name of the experiment to register the model under, used the deployment name if not provided. This is required for certain model registries such as Databricks, where this must an absolute path, e.g. “/Users/<username>/<experiment_name>”.

Returns

Returns

MLflowDeployment object containing information about the deployed application

Return type

Return type

MLflowDeployment

download

download

download(local_path, download_as_zip=False, overwrite=False)

Download an MLflow deployment to the specified local path. To see how to use this downloaded package, please go to https://mlflow.org/.

Examples

>>> deployment.download(local_path='./my-folder', download_as_zip=True)
Successfully downloaded deployment to ./my-folder/my-deployment-name.zip
>>> deployment.download(local_path='./my-folder', download_as_zip=False)
Successfully downloaded deployment to ./my-folder/my-deployment-name

Parameters

Parameters

Name	Type	Default	Info
local_path	str		The local path to download the deployment to.
download_as_zip	bool	False	Whether to download the deployment as a zip file. If False, the deployment will be downloaded as a directory.
overwrite	bool	False	Whether to overwrite the local path if it already exists. If False, an error will be thrown if the path already exists.

Return type

Return type

None

register

register

register(tracking_server_uri, *, experiment_name=None, model_name=None, mlflow_s3_endpoint_url=None, aws_access_key_id=None, aws_secret_access_key=None, basic_auth_username=None, basic_auth_password=None, bearer_auth_token=None, timeout_seconds=600)

Register an MLflowModel Deployment with an external MLflow model registry.

Examples

>>> from snorkelflow.sdk import MLflowDeployment
>>> deployment = MLflowDeployment.create(application="my-app")
>>> deployment.register(tracking_server_uri="http://localhost:5000")
<Response [200]>

Parameters

Parameters

Name	Type	Default	Info
tracking_server_uri	str		URI of the tracking server to export the deployment to.
experiment_name	Optional[str]	None	The name of the experiment to register the model under, used default if not provided.
model_name	Optional[str]	None	The name of the model to register the deployment under. Defaults to the parent application name.
mlflow_s3_endpoint_url	Optional[str]	None	The endpoint of the S3-compatible storage service used by the tracking server.
aws_access_key_id	Optional[str]	None	The access key for the s3-compatible storage service used by the tracking server.
aws_secret_access_key	Optional[str]	None	The secret key for the s3-compatible storage service used by the tracking server.
basic_auth_username	Optional[str]	None	Username needed for access to servers with basic authentication enabled.
basic_auth_password	Optional[str]	None	Password needed for access to servers with basic authentication enabled.
bearer_auth_token	Optional[str]	None	Bearer token needed for access to servers with bearer authentication enabled.
timeout_seconds	int	600	Maximum amount of time to wait for the job to complete before giving up.

Returns

Returns

A dictionary describing the model that has been exported to the external service. Resembles a mlflow.model.models.ModelInfo object with extra information on the active Experiment.

Return type

Return type

Dict