Runtime parameters for custom models¶
Availability information
Runtime parameters for custom models are off by default. Contact your DataRobot representative or administrator for information on enabling this feature.
Feature flag: Enable the Injection of Runtime Parameters for Custom Models
Now available as a public preview feature, you can add runtime parameters to a custom model through the model metadata, making your custom model code easier to reuse.
Define runtime parameters¶
To define runtime parameters, you can add the following runtimeParameterDefinitions
in model-metadata.yaml
:
Key | Description |
---|---|
fieldName |
Define the name of the runtime parameter. |
type |
Define the data type the runtime parameter contains: string , boolean , numeric credential . |
defaultValue |
(Optional) Set the default string value for the runtime parameter (the credential type doesn't support default values). |
minValue |
(Optional) For numeric runtime parameters, set the minimum numeric value allowed in the runtime parameter. |
maxValue |
(Optional) For numeric runtime parameters, set the maximum numeric value allowed in the runtime parameter. |
allowEmpty |
(Optional) Set the empty field policy for the runtime parameter:
|
description |
(Optional) A description of the purpose or contents of the runtime parameter. |
Note
If you define a runtime parameter without specifying a defaultValue
, the default value is None
.
name: runtime-parameter-example
type: inference
targetType: regression
runtimeParameterDefinitions:
- fieldName: my_first_runtime_parameter
type: string
description: My first runtime parameter.
- fieldName: runtime_parameter_with_default_value
type: string
defaultValue: Default
description: A string-type runtime parameter with a default value.
- fieldName: runtime_parameter_boolean
type: boolean
defaultValue: true
description: A boolean-type runtime parameter with a default value of true.
- fieldname: runtime_parameter_numeric
type: numeric
defaultValue: 0
minValue: -100
maxValue: 100
description: A boolean-type runtime parameter with a default value of 0, a minimum value of -100, and a maximum value of 100.
- fieldName: runtime_parameter_for_credentials
type: credential
allowEmpty: false
description: A runtime parameter containing a dictionary of credentials; credentials must be provided before registering the custom model.
The credential
runtime parameter type supports any credentialType
value available in the DataRobot REST API. The credential information included depends on the credentialType
, as shown in the examples below:
Note
For more information on the supported credential types, see the API reference documentation for credentials.
Credential Type | Example |
---|---|
basic |
basic: credentialType: basic description: string name: string password: string user: string |
azure |
azure: credentialType: azure description: string name: string azureConnectionString: string |
gcp |
gcp: credentialType: gcp description: string name: string gcpKey: string |
s3 |
s3: credentialType: s3 description: string name: string awsAccessKeyId: string awsSecretAccessKey: string awsSessionToken: string |
api_token |
api_token: credentialType: api_token apiToken: string name: string |
Provide override values during local development¶
For local development with DRUM, you can specify a .yaml
file containing the values of the runtime parameters. The values defined here override the defaultValue
set in model-metadata.yaml
:
my_first_runtime_parameter: Hello, world.
runtime_parameter_with_default_value: Override the default value.
runtime_parameter_for_credentials:
credentialType: basic
name: credentials
password: password1
user: user1
When using DRUM, the new --runtime-params-file
option specifies the file containing the runtime parameter values:
drum score --runtime-params-file .runtime-parameters.yaml --code-dir model_templates/python3_sklearn --target-type regression --input tests/testdata/juniors_3_year_stats_regression.csv
Import and use runtime parameters in custom code¶
To import and access runtime parameters, you can import the RuntimeParameters
module in your code in custom.py
:
from datarobot_drum import RuntimeParameters
def mask(value, visible=3):
return value[:visible] + ("*" * len(value[visible:]))
def transform(data, model):
print("Loading the following Runtime Parameters:")
parameter1 = RuntimeParameters.get("my_first_runtime_parameter")
parameter2 = RuntimeParameters.get("runtime_parameter_with_default_value")
print(f"\tParameter 1: {parameter1}")
print(f"\tParameter 2: {parameter2}")
credentials = RuntimeParameters.get("runtime_parameter_for_credentials")
if credentials is not None:
credential_type = credentials.pop("credentialType")
print(
f"\tCredentials (type={credential_type}): "
+ str({k: mask(v) for k, v in credentials.items()})
)
else:
print("No credential data set")
return data
View and edit runtime parameters in DataRobot¶
When you add a model-metadata.yaml
file with runtimeParameterDefinitions
to DataRobot while creating a custom model, the Runtime Parameters section appears on the Assemble tab for that custom model. After you build the environment and create a new version, you can click View and Edit to configure the parameters:
Note
Each change to a runtime parameter creates a new minor version of the custom model.
After you test a model with runtime parameters in the Custom Model Workshop, you can navigate to the Test > Runtime Parameters to view the model's parameters:
If any runtime parameters have allowEmpty: false
in the definition without a defaultValue
, you must set a value before registering the custom model.