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 | Value |
|---|---|
fieldName |
The name of the runtime parameter. |
type |
The data type the runtime parameter contains:string or credential. |
defaultValue |
(Optional) The default string value for the runtime parameter (the credential type doesn't support default values). |
description |
(Optional) A descripton of the purpose or contents of the runtime parameter. |
Note
If you define a runtime parameter without specifying a defuaultValue, 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 runtime parameter with a default value.
- fieldName: runtime_parameter_for_credentials
type: credential
description: A runtime parameter containing a dictionary of credentials.
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
|
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:
