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POST
/
api
/
automl
/
train
/
jobs
/
{job_id}
/
trigger
Manually trigger an AutoML training DAG
curl --request POST \
  --url https://api.example.com/api/automl/train/jobs/{job_id}/trigger \
  --header 'Authorization: Bearer <token>'
{
  "job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "name": "<string>",
  "domain": "<string>",
  "owner": "<string>",
  "source_table": "<string>",
  "created_at": "2023-11-07T05:31:56Z",
  "updated_at": "2023-11-07T05:31:56Z",
  "rendered_dag": "<string>",
  "mlflow_run_id": "<string>",
  "mlflow_experiment_name": "<string>",
  "registered_model_name": "<string>",
  "airflow_dag_id": "<string>",
  "airflow_dag_url": "<string>",
  "error_message": "<string>",
  "feature_engineering_job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
}

Authorizations

​
Authorization
string
header
required

The access token received from the authorization server in the OAuth 2.0 flow.

Path Parameters

​
job_id
string<uuid>
required

Training job ID.

Response

Successful Response

A persisted training job.

​
job_id
string<uuid>
required
​
name
string
required
​
domain
string
required
​
owner
string
required
​
source_table
string
required
​
problem_type
enum<string>
required

Supervised / unsupervised problem categories.

Available options:
binary_classification,
multiclass_classification,
regression,
ranking,
anomaly_detection,
contextual_bandit
​
algorithm
enum<string>
required

Supported AutoML algorithms.

Available options:
lightgbm_classifier,
lightgbm_regressor,
lightgbm_ranker,
xgboost_classifier,
xgboost_regressor,
xgboost_ranker,
isolation_forest,
vw_classifier,
vw_regressor,
vw_contextual_bandit
​
preset
enum<string>
required

Tuning presets shared across algorithms.

Available options:
fast,
balanced,
best_quality
​
status
enum<string>
required

Lifecycle of an AutoML training job.

  • READY: DAG uploaded but not yet triggered. The user opted out of auto-trigger at create time. Manual trigger flips it to QUEUED.
  • QUEUED: DAG triggered, waiting for Airflow to pick it up.
  • RUNNING: Airflow has the run going.
  • COMPLETE/FAILED: terminal.
Available options:
ready,
queued,
running,
complete,
failed
​
created_at
string<date-time>
required
​
updated_at
string<date-time>
required
​
rendered_dag
string | null

The rendered DAG source. Populated on creation.

​
mlflow_run_id
string | null
​
mlflow_experiment_name
string | null
​
registered_model_name
string | null
​
airflow_dag_id
string | null
​
airflow_dag_url
string | null
​
error_message
string | null
​
feature_engineering_job_id
string<uuid> | null