> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mathfi.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Start the run once inputs are uploaded

> Begin the run once the inputs are uploaded, then poll `/progress` for state and the top-performance
metrics as refinement proceeds.

Validates that both the train and labelled-test CSVs have been uploaded first. If either is missing,
or the training file has more columns than the run accepts, it returns 400 and does NOT start.




## OpenAPI

````yaml api-reference/openapi.yaml POST /api/v1/feature-refinery/{featureRefinementKey}/start
openapi: 3.0.1
info:
  title: MathFi.ai API
  description: >
    The MathFi.ai REST API runs the two products end to end.


    **Feature Refinery** takes your labelled data and works out which columns

    actually carry the signal. It returns a reduced train and test pair, a

    per-column keep/remove decision, and the performance target the refined data

    reached.


    **Model Crucible** takes a dataset, trains every algorithm family against it

    at once, ranks the results on held-out data by fewest wrong decisions, and

    keeps the best three as versions of one model. You pick which one
    predictions

    run against.


    Everything is asynchronous: you create a thing, upload to a signed URL,
    start

    it, then poll until the state is terminal. Nothing streams and nothing
    blocks.
  contact:
    name: MathFi.ai
    url: https://mathfi.ai
    email: support@mathfi.ai
  license:
    name: MathFi.ai
    url: https://mathfi.ai
  version: 1.0.0
servers:
  - url: https://{tenant}-api.mathfi.ai
    description: >-
      Your tenant's API. Each customer has their own, so the host varies.
      Replace {tenant} with the name issued when your tenant was created.
    variables:
      tenant:
        default: your-tenant
        description: The tenant name issued to you.
security:
  - BearerAuth: []
tags:
  - name: Authentication
    description: Exchange credentials for a bearer token
  - name: Feature Refinery
    description: Reduce a dataset to the columns that earn their place
  - name: Datasets
    description: >-
      Prepare labelled data for training, from uploads or from a finished
      refinement
  - name: Training
    description: Run the Crucible against a dataset and choose the champion model
  - name: Models
    description: Trained models and their versions
  - name: Predictions
    description: Score unlabelled data against a champion model
paths:
  /api/v1/feature-refinery/{featureRefinementKey}/start:
    post:
      tags:
        - Feature Refinery
      summary: Start the run once inputs are uploaded
      description: >
        Begin the run once the inputs are uploaded, then poll `/progress` for
        state and the top-performance

        metrics as refinement proceeds.


        Validates that both the train and labelled-test CSVs have been uploaded
        first. If either is missing,

        or the training file has more columns than the run accepts, it returns
        400 and does NOT start.
      operationId: startFeatureRefinement
      parameters:
        - name: featureRefinementKey
          in: path
          required: true
          schema:
            type: string
            format: uuid
      responses:
        '200':
          description: All inputs present; run started
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/FeatureRefineryStartOutput'
        '400':
          description: >-
            One or more expected input files were not uploaded — PUT them before
            starting
        '404':
          description: Unknown feature refinement key
        '409':
          description: The run was already started — /start can only be called once
        '429':
          description: >-
            Too many of your runs are already in flight — wait for one to finish
            before starting another
        '500':
          description: Internal server error
components:
  schemas:
    FeatureRefineryStartOutput:
      description: >-
        Returned by /start when all inputs are present and the run has been
        dispatched.
      required:
        - featureRefinementKey
        - state
      type: object
      properties:
        featureRefinementKey:
          type: string
          format: uuid
        state:
          type: string
  securitySchemes:
    BearerAuth:
      type: http
      scheme: bearer
      bearerFormat: JWT

````