Configuration
init()
lume.init()
Initializes the Lume client with a specific API key and other configurations. This is only necessary if you are not setting the
LUME_API_KEY environment variable.Parametersapi_key(str): Your Lume API key.request_timeout(int, optional): The timeout in seconds for API requests. Defaults to60.
Pipeline Execution
run()
lume.run()
Triggers a new, end-to-end Lume pipeline execution. This is an asynchronous operation that returns immediately.Parameters
flow_version(str): The name and version of the flow to execute (e.g.,"my_flow:v2").source_path(str): A unique identifier pointing to the source data for this run.run_metadata(dict, optional): A dictionary of custom key-value pairs to associate with the run, retrievable via webhook orrun_status().force_rerun(bool, optional): IfTrue, bypasses the idempotency check to re-process asource_paththat has already succeeded. Defaults toFalse.
LumeRun: An object representing the newly created run.
run_status()
lume.run_status()
Retrieves the current status and metadata of a specific run by its ID.Parameters
run_id(str): The unique identifier of the run (e.g.,"run_01HY...").
LumeRun: An object representing the run, with itsstatusandmetadataattributes populated.
Beta Functions
upload_file()
lume.upload_file()
Uploads a local file to the Lume-managed staging area for S3-based flows, returning a
source_path for use in lume.run().Parameterslocal_file_path(str): Path to the local file to upload.flow_version(str): The Flow Version this file is for, used to determine the correct upload location.
str: Thesource_pathcorresponding to the uploaded file.
get_results_dataframe()
lume.get_results_dataframe()
Downloads the mapped or rejected output from a completed run directly into a Pandas DataFrame. Requires
pandas.Parametersrun_id(str): The ID of the completed run.result_type(str, optional): The result set to download, either"mapped"or"rejects". Defaults to"mapped".
pandas.DataFrame: A DataFrame containing the requested result data.
