# create\_run\_config

dbnl.create_run_config(
    *,
    project: Project,
    columns: list[dict[str, Any]],
    scalars: Optional[list[dict[str, Any]]] = None,
    description: Optional[str] = None,
    display_name: Optional[str] = None,
    row_id: Optional[list[str]] = None,
    components_dag: Optional[dict[str, list[str]]] = None,
) -> RunConfig:

## Parameters

Arguments	Description
`project`	The Project this RunConfig is associated with.
`columns`	A list of column schema specs for the uploaded data, required keys `name` and `type`, optional key `component`, `description` and `greater_is_better`. `type` can be `int`, `float`, `category`, `boolean`, or `string`. `component` is a string that indicates the source of the data. e.g. "component" : "sentiment-classifier" or "component" : "fraud-predictor". Specified components must be present in the `components_dag` dictionary. `greater_is_better` is a boolean that indicates if larger values are better than smaller ones. False indicates smaller values are better. None indicates no preference. Example: `columns=[{"name": "pred_proba", "type": "float", "component": "fraud-predictor"}, {"name": "decision", "type": "boolean", "component": "threshold-decision"}, {"name": "requests", "type": "string", "description": "curl request response msg"}]` See the column schema section below for more information.
`scalars`	NOTE: `scalars` is available in SDK v0.0.15 and above. A list of scalar schema specs for the uploaded data, required keys `name` and `type`, optional key `component`, `description` and `greater_is_better`. `type` can be `int`, `float`, `category`, `boolean`, or `string`. `component` is a string that indicates the source of the data. e.g. "component" : "sentiment-classifier" or "component" : "fraud-predictor". Specified components must be present in the `components_dag` dictionary. `greater_is_better` is a boolean that indicates if larger values are better than smaller ones. False indicates smaller values are better. None indicates no preference. An example RunConfig scalars: `scalars=[{"name": "accuracy", "type": "float", "component": "fraud-predictor"}, {"name": "error_type", "type": "category"}]` Scalar schema is identical to column schema.
`description`	An optional description of the RunConfig, defaults to `None`. Descriptions are limited to 255 characters.
`display_name`	An optional display name of the RunConfig, defaults to `None`. Display names do not have to be unique.
`row_id`	An optional list of the column names that can be used as unique identifiers, defaults to `None`.
`components_dag`	An optional dictionary representing the direct acyclic graph (DAG) of the specified components, defaults to `None`. Every `component` listed in the `columns` schema must be present in the `components_dag`. Example: `components_dag={"fraud-predictor": ["threshold-decision"], 'threshold-decision': []}` See the components DAG section below for more information.

### Column Schema #### Column Names Column names can only be alphanumeric characters and underscores. #### Supported Types The following type supported as `type` in column schema

type	Notes
`float`
`int`
`boolean`
`string`	Any arbitrary string values. Raw string type columns do not produce any histogram or scatterplot on the web UI.
`category`	Equivalent of pandas categorical data type. Currently only supports category of string values.
`list`	Currently only supports list of string values. List type columns do not produce any histogram or scatterplot on the web UI.

#### Components The optional `component` key is for specifying the source of the data column in relationship to the AI/ML app subcomponents. Components are used in visualizing the [components DAG](#components-dag). ### Components DAG

The `components_dag` dictionary specifies the topological layout of the AI/ML app. For each key-value pair, the key represents the source component, and the value is a list of the leaf components. The following code snippet describes the DAG shown above. ```python components_dags={ "TweetSource": ["EntityExtractor", "SentimentClassifier"], "EntityExtractor": ["TradeRecommender"], "SentimentClassifier": ["TradeRecommender"], "TradeRecommender": [], "Global": [], } ``` ## Returns

Type	Description
RunConfig	A new dbnl RunConfig

## Examples ### Basic Usage ```python import dbnl dbnl.login() proj = dbnl.get_or_create_project(name="test_p1") # create a new RunConfig runcfg1 = dbnl.create_run_config( project=proj, columns=[ {"name": "error_type", "type": "category"}, {"name": "email", "type": "string", "description": "raw email text content from source"}, {"name": "spam-pred", "type": "boolean"}, ], display_name="Basic RunConfig for spam prediction", ) ``` ### RunConfig with DAG ```python import dbnl dbnl.login() proj = dbnl.get_or_create_project(name="test_p1") # create a new RunConfig with DAG runcfg1 = dbnl.create_run_config( project=proj, columns=[ {"name": "error_type", "type": "category"}, {"name": "email", "type": "string", "component": "data_source", "description": "raw email text content from source"}, {"name": "spam-pred", "type": "boolean", "component": "spam_classifier"}, ], display_name="Basic RunConfig for spam prediction", components_dag={ "data_source": ["spam_classifier"] "spam_classifier": [] ) ``` ### RunConfig with row\_id ```python import dbnl dbnl.login() proj = dbnl.get_or_create_project(name="test_p1") # create a new RunConfig runcfg1 = dbnl.create_run_config( project=proj, columns=[ {"name": "error_type", "type": "category"}, {"name": "email", "type": "string", "description": "raw email text content from source"}, {"name": "spam-pred", "type": "boolean"}, {"name": "email_id", "type": "string", "description": "unique id for each email"}, ], display_name="Basic RunConfig for spam prediction", row_id=["email_id"], ) ``` RunConfig with scalars ```python import dbnl dbnl.login() proj = dbnl.get_or_create_project(name="test_p1") # create a new RunConfig runcfg1 = dbnl.create_run_config( project=proj, columns=[ {"name": "error_type", "type": "category"}, {"name": "email", "type": "string", "description": "raw email text content from source"}, {"name": "spam-pred", "type": "boolean"}, {"name": "email_id", "type": "string", "description": "unique id for each email"}, ], scalars=[ {"name": "model_F1", "type": "float"}, {"name": "model_recall", "type": "float"}, ], display_name="Basic RunConfig for spam prediction", ) ``` [^1]: [Project](/v0.20.x/using-distributional/python-sdk/sdk-objects/project.md) [^2]: [RunConfig](/v0.20.x/using-distributional/python-sdk/sdk-objects/runconfig.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.dbnl.com/v0.20.x/using-distributional/python-sdk/sdk-functions/run-config/create_run_config.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.