# Define custom model runtime parameters

> Define custom model runtime parameters - Add runtime parameters to a custom model through the model
> metadata, making your custom model code easier to reuse.

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Companion generated at `2026-04-24T16:03:56.256941+00:00` (UTC).

## Primary page

- [Define custom model runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html): Full documentation for this topic (HTML).

## Sections on this page

- [DataRobot reserved runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html#datarobot-reserved-runtime-parameters): In-page section heading.
- [Define custom model metadata](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html#define-custom-model-metadata): In-page section heading.
- [Provide credentials through runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html#provide-credentials-through-runtime-parameters): In-page section heading.
- [Reference existing credentials](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html#reference-existing-credentials): In-page section heading.
- [Provide credential values directly](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html#provide-credential-values-directly): In-page section heading.
- [Provide override values during local development](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html#provide-override-values-during-local-development): In-page section heading.
- [Import and use runtime parameters in custom code](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html#import-and-use-runtime-parameters-in-custom-code): In-page section heading.

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Code-first tools](https://docs.datarobot.com/en/docs/api/code-first-tools/index.html): Linked from this page.
- [DataRobot User Models](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html): Linked from this page.
- [define the custom model metadata](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html): Linked from this page.
- [Credentials management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html): Linked from this page.
- [API reference documentation for credentials](https://docs.datarobot.com/en/docs/api/reference/public-api/credentials.html#schemacredentialsbody): Linked from this page.

## Documentation content

# Define custom model runtime parameters

Add runtime parameters to a custom model through the model metadata, making your custom model code easier to reuse. 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, deployment. |
| defaultValue | (Optional) Set the default value for the runtime parameter. For credential type parameters, use defaultValue to reference an existing credential by its credential ID. For other types, set the default string, boolean, or numeric value. If you define a runtime parameter without specifying a defaultValue, the default value is None. |
| 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. |
| credentialType | (Optional) For credential runtime parameters, set the type of credentials the parameter must contain. |
| allowEmpty | (Optional) Set the empty field policy for the runtime parameter.True: (Default) Allows an empty runtime parameter.False: Enforces providing a value for the runtime parameter before deployment. |
| description | (Optional) Provide a description of the purpose or contents of the runtime parameter. |

## DataRobot reserved runtime parameters

The following runtime parameter is reserved by DataRobot for custom model configuration:

| Runtime parameter | Type | Description |
| --- | --- | --- |
| CUSTOM_MODEL_WORKERS | numeric | Allows each replica to handle a set number of concurrent processes. This option is intended for process-safe custom models, primarily in generative AI use cases (for more information on process-safe models, see the note below). To determine the appropriate number of concurrent processes to allow per replica, monitor the number of requests and the median response time for the custom model. The median response time for the custom model should be close to the median response time from the LLM. If the response time of the custom model exceeds the LLM's response time, stop increasing the number of concurrent processes and instead increase the number of replicas.Default value: 1 Max value: 40 |

> [!WARNING] Custom model process safety
> When enabling and configuring `CUSTOM_MODEL_WORKERS`, ensure that your model is process-safe, allowing multiple independent processes to safely interact with shared resources without causing conflicts. This configuration is not intended for general use with custom models to make them more resource efficient. Only process-safe custom models with I/O-bound tasks (like proxy models) benefit from utilizing CPU resources this way.

## Define custom model metadata

Before you define `runtimeParameterDefinitions` in `model-metadata.yaml`, [define the custom model metadata](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html) required for the target type. For binary and multiclass models, that includes an [inferenceModel](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html#inference-model-metadata) block ( `targetName` and class labels; for multiclass, `classLabels`).

**Binary classification:**
```
name: binary-example
targetType: binary
type: inference
inferenceModel:
  targetName: target
  positiveClassLabel: "1"
  negativeClassLabel: "0"
```

**Regression:**
```
name: regression-example
targetType: regression
type: inference
```

**Text generation:**
```
name: textgeneration-example
targetType: textgeneration
type: inference
```

**Anomaly detection:**
```
name: anomaly-example
targetType: anomaly
type: inference
```

**Unstructured:**
```
name: unstructured-example
targetType: unstructured
type: inference
```

**Multiclass:**
```
name: multiclass-example
targetType: multiclass
type: inference
inferenceModel:
  targetName: class
  classLabels:
    - class_a
    - class_b
    - class_c
```


Then, below the model information, you can provide the `runtimeParameterDefinitions`:

```
# Example: runtimeParameterDefinitions in model-metadata.yaml
name: runtime-parameter-example
targetType: regression
type: inference

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
  credentialType: basic
  allowEmpty: false
  description: A runtime parameter containing a dictionary of credentials; credentials must be provided before registering the custom model.

- fieldName: runtime_parameter_for_connected_deployment
  type: deployment
  description: A runtime parameter defined to accept the deployment ID of another deployment to connect to the deployed custom model.
```

## Provide credentials through runtime parameters

The `credential` runtime parameter type supports any `credentialType` value available in the DataRobot REST API. You can provide credentials in two ways:

- Reference existing credentials: Use the credential ID as thedefaultValueto reference credentials defined in the DataRobotCredentials managementsection.
- Provide credential values directly: Include the full credential structure when defining the runtime parameter (typically used during local development with DRUM).

> [!NOTE] Credential types
> For more information on the supported credential types, see the [API reference documentation for credentials](https://docs.datarobot.com/en/docs/api/reference/public-api/credentials.html#schemacredentialsbody).

### Reference existing credentials

To reference an existing credential, set the `defaultValue` to the credential ID:

```
# Example: Reference an existing credential
- fieldName: my_api_token
  type: credential
  credentialType: api_token
  allowEmpty: false
  defaultValue: <credential-id>
  description: A runtime parameter referencing an existing API token credential.
```

> [!WARNING] Credential requirements
> When you reference an existing credential, the credential must exist in the credential management section before registering the custom model, must match the `credentialType` specified in the runtime parameter definition, and must match the credential ID used as the `defaultValue`.

### Provide credential values directly

The credential information required depends on the `credentialType`, as shown in the examples below:

| 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`:

```
# 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 `--runtime-params-file` option specifies the file containing the runtime parameter values:

```
# 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`:

```
# 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
```