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>'import requests
url = "https://api.example.com/api/automl/train/jobs/{job_id}/trigger"
headers = {"Authorization": "Bearer <token>"}
response = requests.post(url, headers=headers)
print(response.text)const options = {method: 'POST', headers: {Authorization: 'Bearer <token>'}};
fetch('https://api.example.com/api/automl/train/jobs/{job_id}/trigger', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.example.com/api/automl/train/jobs/{job_id}/trigger",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/api/automl/train/jobs/{job_id}/trigger"
req, _ := http.NewRequest("POST", url, nil)
req.Header.Add("Authorization", "Bearer <token>")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.example.com/api/automl/train/jobs/{job_id}/trigger")
.header("Authorization", "Bearer <token>")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/train/jobs/{job_id}/trigger")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
response = http.request(request)
puts response.read_body{
"job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"name": "<string>",
"domain": "<string>",
"owner": "<string>",
"source_table": "<string>",
"problem_type": "binary_classification",
"algorithm": "lightgbm_classifier",
"preset": "fast",
"status": "ready",
"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"
}{
"error": "<string>",
"code": 500
}{
"error": "<string>",
"code": 500
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}{
"error": "<string>",
"code": 500
}Training
Manually trigger an AutoML training DAG
Kick off a fresh DAG run via Airflow’s REST API.
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>'import requests
url = "https://api.example.com/api/automl/train/jobs/{job_id}/trigger"
headers = {"Authorization": "Bearer <token>"}
response = requests.post(url, headers=headers)
print(response.text)const options = {method: 'POST', headers: {Authorization: 'Bearer <token>'}};
fetch('https://api.example.com/api/automl/train/jobs/{job_id}/trigger', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.example.com/api/automl/train/jobs/{job_id}/trigger",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/api/automl/train/jobs/{job_id}/trigger"
req, _ := http.NewRequest("POST", url, nil)
req.Header.Add("Authorization", "Bearer <token>")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.example.com/api/automl/train/jobs/{job_id}/trigger")
.header("Authorization", "Bearer <token>")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/train/jobs/{job_id}/trigger")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
response = http.request(request)
puts response.read_body{
"job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"name": "<string>",
"domain": "<string>",
"owner": "<string>",
"source_table": "<string>",
"problem_type": "binary_classification",
"algorithm": "lightgbm_classifier",
"preset": "fast",
"status": "ready",
"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"
}{
"error": "<string>",
"code": 500
}{
"error": "<string>",
"code": 500
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}{
"error": "<string>",
"code": 500
}Authorizations
OAuth2AuthorizationCodeBearerAPIKeyHeader
The access token received from the authorization server in the OAuth 2.0 flow.
FlowAuthorization Code
- Authorization URL
- https://sso-rapid.rapid.nx1cloud.com/realms/rapid/protocol/openid-connect/auth
- Token URL
- https://sso-rapid.rapid.nx1cloud.com/realms/rapid/protocol/openid-connect/token
Path Parameters
Training job ID.
Response
Successful Response
A persisted training job.
Supervised / unsupervised problem categories.
Available options:
binary_classification, multiclass_classification, regression, ranking, anomaly_detection, contextual_bandit 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 Tuning presets shared across algorithms.
Available options:
fast, balanced, best_quality 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 toQUEUED.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 The rendered DAG source. Populated on creation.
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