DAG status writeback
curl --request PUT \
--url https://api.example.com/api/automl/train/jobs/{job_id}/status \
--header 'Authorization-PSK: <api-key>' \
--header 'Content-Type: application/json' \
--data '
{
"mlflow_run_id": "<string>",
"registered_model_name": "<string>",
"error_message": "<string>"
}
'import requests
url = "https://api.example.com/api/automl/train/jobs/{job_id}/status"
payload = {
"mlflow_run_id": "<string>",
"registered_model_name": "<string>",
"error_message": "<string>"
}
headers = {
"Authorization-PSK": "<api-key>",
"Content-Type": "application/json"
}
response = requests.put(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'PUT',
headers: {'Authorization-PSK': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
mlflow_run_id: '<string>',
registered_model_name: '<string>',
error_message: '<string>'
})
};
fetch('https://api.example.com/api/automl/train/jobs/{job_id}/status', 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}/status",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "PUT",
CURLOPT_POSTFIELDS => json_encode([
'mlflow_run_id' => '<string>',
'registered_model_name' => '<string>',
'error_message' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Authorization-PSK: <api-key>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/api/automl/train/jobs/{job_id}/status"
payload := strings.NewReader("{\n \"mlflow_run_id\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"error_message\": \"<string>\"\n}")
req, _ := http.NewRequest("PUT", url, payload)
req.Header.Add("Authorization-PSK", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.put("https://api.example.com/api/automl/train/jobs/{job_id}/status")
.header("Authorization-PSK", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"mlflow_run_id\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"error_message\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/train/jobs/{job_id}/status")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Put.new(url)
request["Authorization-PSK"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"mlflow_run_id\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"error_message\": \"<string>\"\n}"
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
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}Training
DAG status writeback
Called by the rendered DAG at task start, success, or failure.
PSK-authenticated to match the existing GET /api/jobs/{id}/status endpoint
used by data-engineering DAGs.
PUT
/
api
/
automl
/
train
/
jobs
/
{job_id}
/
status
DAG status writeback
curl --request PUT \
--url https://api.example.com/api/automl/train/jobs/{job_id}/status \
--header 'Authorization-PSK: <api-key>' \
--header 'Content-Type: application/json' \
--data '
{
"mlflow_run_id": "<string>",
"registered_model_name": "<string>",
"error_message": "<string>"
}
'import requests
url = "https://api.example.com/api/automl/train/jobs/{job_id}/status"
payload = {
"mlflow_run_id": "<string>",
"registered_model_name": "<string>",
"error_message": "<string>"
}
headers = {
"Authorization-PSK": "<api-key>",
"Content-Type": "application/json"
}
response = requests.put(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'PUT',
headers: {'Authorization-PSK': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
mlflow_run_id: '<string>',
registered_model_name: '<string>',
error_message: '<string>'
})
};
fetch('https://api.example.com/api/automl/train/jobs/{job_id}/status', 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}/status",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "PUT",
CURLOPT_POSTFIELDS => json_encode([
'mlflow_run_id' => '<string>',
'registered_model_name' => '<string>',
'error_message' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Authorization-PSK: <api-key>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/api/automl/train/jobs/{job_id}/status"
payload := strings.NewReader("{\n \"mlflow_run_id\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"error_message\": \"<string>\"\n}")
req, _ := http.NewRequest("PUT", url, payload)
req.Header.Add("Authorization-PSK", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.put("https://api.example.com/api/automl/train/jobs/{job_id}/status")
.header("Authorization-PSK", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"mlflow_run_id\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"error_message\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/train/jobs/{job_id}/status")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Put.new(url)
request["Authorization-PSK"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"mlflow_run_id\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"error_message\": \"<string>\"\n}"
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
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}Authorizations
Path Parameters
Training job ID.
Body
application/json
Status writeback payload sent by the Airflow DAG.
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 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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