Runtime parameters for custom models¶
本機能の提供について
Runtime parameters for custom models are off by default. この機能を有効にする方法については、DataRobotの担当者または管理者にお問い合わせください。
Feature flag: Enable the Injection of Runtime Parameters for Custom Models
パブリックプレビュー版の機能です。モデルのメタデータを通じてカスタムモデルにランタイムパラメーターを追加でき、カスタムモデルのコードが再利用しやすくなりました
ランタイムパラメーターの定義¶
ランタイムパラメーターを定義するには、model-metadata.yamlに以下の runtimeParameterDefinitionsを追加します。
| キー | 値 |
|---|---|
fieldName |
ランタイムパラメーターの名前 |
type |
ランタイムパラメーターに含まれるデータタイプ：stringまたはcredential |
defaultValue |
（オプション）ランタイムパラメーターのデフォルト文字列値（credentialタイプはデフォルト値をサポートしません） |
description |
（オプション）ランタイムパラメーターの目的または内容の説明 |
備考
If you define a runtime parameter without specifying a defuaultValue, the default value is None.
yaml title="Example: model-metadata.yaml"
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:
備考
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:
yaml title="Example: .runtime-parameters.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:
sh title="Example: --runtime-params-file"
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:
py title="Example: 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
DataRobotでのランタイムパラメーターの表示と編集¶
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:
備考
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:
