6.1 Choose an execution rule

A deployment is a way to run a Machine Learning pipeline in a repeatable and automated way.

For each deployment, you can configure an execution rule:

by endpoint (web API) : the pipeline will be executed by a call to a web API. In addition, this API will allow, if necessary, to retrieve data as input and deliver the result of the pipeline as output. Access to the API can be securely communicated to external users.
by periodic trigger (CRON) : rules can be configured to trigger the pipeline periodically.

Function name	Method	Return type	Description
create_deployment	create_deployment(name, pipeline_name, execution_rule, outputs_mapping=[], inputs_mapping=[])	Dict	Function that deploy a pipeline by creating a deployment which allows a user to trigger the pipeline execution

6.1.2 Deploy with execution rule: Endpoint

6.1.2.1 Definition function

To create an auto-mapping deployment where all inputs and outputs are based on API calls, you can use the create_deployment function. To create a deployment with manual mapping, you can use the create_deployment function with the additional parameters inputs_mapping to specify the precise mapping between input and source.

Warning

Outputs mapping always needs to be precised. Auto-mapping is only available for inputs.

Parameters

deployment_name (str) – Name of endpoint chosen by the user to refer to the endpoint
pipeline_name (str) – Name of pipeline that will be run by the deployment / endpoint
execution_rule (str) - Execution rule of the deployment. Must be “endpoint” or “periodic”. For convenience, members of the enumeration DEPLOYMENT_EXECUTION_RULES could be used too.
description (str, optional) – Text description of usage of pipeline for user only
outputs_mapping (List) - List of all OutputDestination objects with information for each input mapping.
inputs_mapping (List, optional) - List of input mappings, to map pipeline inputs to different sources (such as constant values, endpoint inputs, data store or environment variables). See InputSource for more details. For endpoint rules, if an input of the step in the pipeline is not explicitly mapped, it will be automatically mapped to an endpoint input with the same name.

Returns

Information about the deployment just create in a dict Python format. In this data, you will have :

id - Unique identifier of the deployment
name - Name of the deployment
endpoint_token - Token used for authentication when making requests to the endpoint
inputs_mapping - Information about the mapping between the sources of the deployment and the inputs of the pipeline
- step_input_name - Name of the input in the pipeline
- endpoint_input_name - Name of the source in the deployment
- constant_value - Default value for the input if the source is missing
- environment_variable_name - The name of an environment variable
- is_null - A flag indicating that the input should not be provided any value at execution time
outputs_mapping - Information about the mapping between the destinations of the deployment and the outputs of the pipeline
- step_output_name - Name of the output in the pipeline
- endpoint_output_name - Name of the destination in the deployment
- is_null - This flag indicates that the output will be deleted after execution.
pipeline - Information about the pipeline that will be run by the deployment
- name - Name of the pipeline

6.1.2.2 Example

Example auto mapping

sdk.create_deployment(
   deployment_name="my_deployment",
   pipeline_name="my_pipeline",
        execution_rule="endpoint",
   outputs_mapping=[prediction_endpoint_ouput],
   inputs_mapping=[],
)

Example manual mapping

sdk.create_deployment(
   deployment_name="my_deployment",
   pipeline_name="my_pipeline",
        execution_rule="endpoint",
   outputs_mapping=[prediction_endpoint_ouput],
    inputs_mapping=[
                seagull_endpoint_input,
                big_whale_input,
                salt_constant_input,
        ],
)

Example of return object

{
    'id': '29d0c371-49ec-4071-a879-10cdab00fda4',
    'name': 'my_deployment',
    'endpoint_token': 'EWwIii [...] kjdD5Q',
    'inputs_mapping':
    [{
        'step_input_name': 'seagull',
        'endpoint_input_name': 'sourceData'
    },
    {
        'step_input_name': 'big_whale',
        'constant_value': 5
    },],
    'outputs_mapping':
    [{
        'step_output_name': 'resultMulti',
        'endpoint_output_name': 'resultMulti'
    }],
    'pipeline': {
        'name': 'my_pipeline'
    }
}

6.1.3 Deploy with execution rule: Periodic

6.1.3.1 Definition function

To create an auto-mapping deployment where all inputs and outputs are based on periodicity, you can use the create_deployment function. To create a deployment with manual mapping, you can use the create_deployment function with the additional parameters inputs_mapping to specify the precise mapping between input and source.

Warning

Outputs mapping always needs to be precised. Auto-mapping is only available for inputs.

Parameters

deployment_name (str) – Name of endpoint chosen by the user to refer to the endpoint
pipeline_name (str) – Name of pipeline that will be run by the deployment / endpoint
execution_rule (str) - Execution rule of the deployment. Must be “endpoint” or “periodic”. For convenience, members of the enumeration DEPLOYMENT_EXECUTION_RULES could be used too.
description (str, optional) – Text description of usage of pipeline for user only.
outputs_mapping (List) - List of all OutputDestination objects with information for each input mapping.
inputs_mapping (List, optional) - List of input mappings, to map pipeline inputs to differentsources (such as constant values, data store, or environment variables). See InputSource for more details. For periodic rules, all inputs of the step in the pipeline must be explicitly mapped.

Returns

Information about the deployment just create in a dict Python format.