> For the complete documentation index, see [llms.txt](https://docs.dbnl.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dbnl.com/v0.21.x/using-distributional/python-sdk/sdk-functions/run-config/create_run_config.md). # 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](/v0.21.x/using-distributional/python-sdk/sdk-objects/project.md) [^2]: [RunConfig](/v0.21.x/using-distributional/python-sdk/sdk-objects/runconfig.md)