Submit an AutoML training job
curl --request POST \
--url https://api.example.com/api/automl/train \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"name": "<string>",
"domain": "<string>",
"owner": "<string>",
"source_table": "<string>",
"preset": "balanced",
"label_column": "<string>",
"positive_class_value": "<unknown>",
"feature_columns": [
"<string>"
],
"exclude_columns": [
"<string>"
],
"group_column": "<string>",
"action_column": "<string>",
"reward_column": "<string>",
"probability_column": "<string>",
"context_columns": [
"<string>"
],
"train_split_cutoff": "<string>",
"time_column": "<string>",
"hyperparameter_overrides": {},
"resources": {
"executor_instances": 2,
"executor_cores": 2,
"executor_memory": "8g",
"executor_memory_overhead": "1g",
"driver_memory": "4g",
"driver_cores": 2
},
"enable_shap": true,
"experiment_name": "<string>",
"registered_model_name": "<string>",
"feature_engineering_job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trigger_now": true
}
'import requests
url = "https://api.example.com/api/automl/train"
payload = {
"name": "<string>",
"domain": "<string>",
"owner": "<string>",
"source_table": "<string>",
"preset": "balanced",
"label_column": "<string>",
"positive_class_value": "<unknown>",
"feature_columns": ["<string>"],
"exclude_columns": ["<string>"],
"group_column": "<string>",
"action_column": "<string>",
"reward_column": "<string>",
"probability_column": "<string>",
"context_columns": ["<string>"],
"train_split_cutoff": "<string>",
"time_column": "<string>",
"hyperparameter_overrides": {},
"resources": {
"executor_instances": 2,
"executor_cores": 2,
"executor_memory": "8g",
"executor_memory_overhead": "1g",
"driver_memory": "4g",
"driver_cores": 2
},
"enable_shap": True,
"experiment_name": "<string>",
"registered_model_name": "<string>",
"feature_engineering_job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trigger_now": True
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
name: '<string>',
domain: '<string>',
owner: '<string>',
source_table: '<string>',
preset: 'balanced',
label_column: '<string>',
positive_class_value: '<unknown>',
feature_columns: ['<string>'],
exclude_columns: ['<string>'],
group_column: '<string>',
action_column: '<string>',
reward_column: '<string>',
probability_column: '<string>',
context_columns: ['<string>'],
train_split_cutoff: '<string>',
time_column: '<string>',
hyperparameter_overrides: {},
resources: {
executor_instances: 2,
executor_cores: 2,
executor_memory: '8g',
executor_memory_overhead: '1g',
driver_memory: '4g',
driver_cores: 2
},
enable_shap: true,
experiment_name: '<string>',
registered_model_name: '<string>',
feature_engineering_job_id: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
trigger_now: true
})
};
fetch('https://api.example.com/api/automl/train', 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",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'name' => '<string>',
'domain' => '<string>',
'owner' => '<string>',
'source_table' => '<string>',
'preset' => 'balanced',
'label_column' => '<string>',
'positive_class_value' => '<unknown>',
'feature_columns' => [
'<string>'
],
'exclude_columns' => [
'<string>'
],
'group_column' => '<string>',
'action_column' => '<string>',
'reward_column' => '<string>',
'probability_column' => '<string>',
'context_columns' => [
'<string>'
],
'train_split_cutoff' => '<string>',
'time_column' => '<string>',
'hyperparameter_overrides' => [
],
'resources' => [
'executor_instances' => 2,
'executor_cores' => 2,
'executor_memory' => '8g',
'executor_memory_overhead' => '1g',
'driver_memory' => '4g',
'driver_cores' => 2
],
'enable_shap' => true,
'experiment_name' => '<string>',
'registered_model_name' => '<string>',
'feature_engineering_job_id' => '3c90c3cc-0d44-4b50-8888-8dd25736052a',
'trigger_now' => true
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"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"
payload := strings.NewReader("{\n \"name\": \"<string>\",\n \"domain\": \"<string>\",\n \"owner\": \"<string>\",\n \"source_table\": \"<string>\",\n \"preset\": \"balanced\",\n \"label_column\": \"<string>\",\n \"positive_class_value\": \"<unknown>\",\n \"feature_columns\": [\n \"<string>\"\n ],\n \"exclude_columns\": [\n \"<string>\"\n ],\n \"group_column\": \"<string>\",\n \"action_column\": \"<string>\",\n \"reward_column\": \"<string>\",\n \"probability_column\": \"<string>\",\n \"context_columns\": [\n \"<string>\"\n ],\n \"train_split_cutoff\": \"<string>\",\n \"time_column\": \"<string>\",\n \"hyperparameter_overrides\": {},\n \"resources\": {\n \"executor_instances\": 2,\n \"executor_cores\": 2,\n \"executor_memory\": \"8g\",\n \"executor_memory_overhead\": \"1g\",\n \"driver_memory\": \"4g\",\n \"driver_cores\": 2\n },\n \"enable_shap\": true,\n \"experiment_name\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"feature_engineering_job_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trigger_now\": true\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
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.post("https://api.example.com/api/automl/train")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"name\": \"<string>\",\n \"domain\": \"<string>\",\n \"owner\": \"<string>\",\n \"source_table\": \"<string>\",\n \"preset\": \"balanced\",\n \"label_column\": \"<string>\",\n \"positive_class_value\": \"<unknown>\",\n \"feature_columns\": [\n \"<string>\"\n ],\n \"exclude_columns\": [\n \"<string>\"\n ],\n \"group_column\": \"<string>\",\n \"action_column\": \"<string>\",\n \"reward_column\": \"<string>\",\n \"probability_column\": \"<string>\",\n \"context_columns\": [\n \"<string>\"\n ],\n \"train_split_cutoff\": \"<string>\",\n \"time_column\": \"<string>\",\n \"hyperparameter_overrides\": {},\n \"resources\": {\n \"executor_instances\": 2,\n \"executor_cores\": 2,\n \"executor_memory\": \"8g\",\n \"executor_memory_overhead\": \"1g\",\n \"driver_memory\": \"4g\",\n \"driver_cores\": 2\n },\n \"enable_shap\": true,\n \"experiment_name\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"feature_engineering_job_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trigger_now\": true\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/train")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"name\": \"<string>\",\n \"domain\": \"<string>\",\n \"owner\": \"<string>\",\n \"source_table\": \"<string>\",\n \"preset\": \"balanced\",\n \"label_column\": \"<string>\",\n \"positive_class_value\": \"<unknown>\",\n \"feature_columns\": [\n \"<string>\"\n ],\n \"exclude_columns\": [\n \"<string>\"\n ],\n \"group_column\": \"<string>\",\n \"action_column\": \"<string>\",\n \"reward_column\": \"<string>\",\n \"probability_column\": \"<string>\",\n \"context_columns\": [\n \"<string>\"\n ],\n \"train_split_cutoff\": \"<string>\",\n \"time_column\": \"<string>\",\n \"hyperparameter_overrides\": {},\n \"resources\": {\n \"executor_instances\": 2,\n \"executor_cores\": 2,\n \"executor_memory\": \"8g\",\n \"executor_memory_overhead\": \"1g\",\n \"driver_memory\": \"4g\",\n \"driver_cores\": 2\n },\n \"enable_shap\": true,\n \"experiment_name\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"feature_engineering_job_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trigger_now\": true\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": {}
}
]
}{
"error": "<string>",
"code": 500
}Training
Submit an AutoML training job
Validate the request, render the DAG, persist a queued job.
POST
/
api
/
automl
/
train
Submit an AutoML training job
curl --request POST \
--url https://api.example.com/api/automl/train \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"name": "<string>",
"domain": "<string>",
"owner": "<string>",
"source_table": "<string>",
"preset": "balanced",
"label_column": "<string>",
"positive_class_value": "<unknown>",
"feature_columns": [
"<string>"
],
"exclude_columns": [
"<string>"
],
"group_column": "<string>",
"action_column": "<string>",
"reward_column": "<string>",
"probability_column": "<string>",
"context_columns": [
"<string>"
],
"train_split_cutoff": "<string>",
"time_column": "<string>",
"hyperparameter_overrides": {},
"resources": {
"executor_instances": 2,
"executor_cores": 2,
"executor_memory": "8g",
"executor_memory_overhead": "1g",
"driver_memory": "4g",
"driver_cores": 2
},
"enable_shap": true,
"experiment_name": "<string>",
"registered_model_name": "<string>",
"feature_engineering_job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trigger_now": true
}
'import requests
url = "https://api.example.com/api/automl/train"
payload = {
"name": "<string>",
"domain": "<string>",
"owner": "<string>",
"source_table": "<string>",
"preset": "balanced",
"label_column": "<string>",
"positive_class_value": "<unknown>",
"feature_columns": ["<string>"],
"exclude_columns": ["<string>"],
"group_column": "<string>",
"action_column": "<string>",
"reward_column": "<string>",
"probability_column": "<string>",
"context_columns": ["<string>"],
"train_split_cutoff": "<string>",
"time_column": "<string>",
"hyperparameter_overrides": {},
"resources": {
"executor_instances": 2,
"executor_cores": 2,
"executor_memory": "8g",
"executor_memory_overhead": "1g",
"driver_memory": "4g",
"driver_cores": 2
},
"enable_shap": True,
"experiment_name": "<string>",
"registered_model_name": "<string>",
"feature_engineering_job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trigger_now": True
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
name: '<string>',
domain: '<string>',
owner: '<string>',
source_table: '<string>',
preset: 'balanced',
label_column: '<string>',
positive_class_value: '<unknown>',
feature_columns: ['<string>'],
exclude_columns: ['<string>'],
group_column: '<string>',
action_column: '<string>',
reward_column: '<string>',
probability_column: '<string>',
context_columns: ['<string>'],
train_split_cutoff: '<string>',
time_column: '<string>',
hyperparameter_overrides: {},
resources: {
executor_instances: 2,
executor_cores: 2,
executor_memory: '8g',
executor_memory_overhead: '1g',
driver_memory: '4g',
driver_cores: 2
},
enable_shap: true,
experiment_name: '<string>',
registered_model_name: '<string>',
feature_engineering_job_id: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
trigger_now: true
})
};
fetch('https://api.example.com/api/automl/train', 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",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'name' => '<string>',
'domain' => '<string>',
'owner' => '<string>',
'source_table' => '<string>',
'preset' => 'balanced',
'label_column' => '<string>',
'positive_class_value' => '<unknown>',
'feature_columns' => [
'<string>'
],
'exclude_columns' => [
'<string>'
],
'group_column' => '<string>',
'action_column' => '<string>',
'reward_column' => '<string>',
'probability_column' => '<string>',
'context_columns' => [
'<string>'
],
'train_split_cutoff' => '<string>',
'time_column' => '<string>',
'hyperparameter_overrides' => [
],
'resources' => [
'executor_instances' => 2,
'executor_cores' => 2,
'executor_memory' => '8g',
'executor_memory_overhead' => '1g',
'driver_memory' => '4g',
'driver_cores' => 2
],
'enable_shap' => true,
'experiment_name' => '<string>',
'registered_model_name' => '<string>',
'feature_engineering_job_id' => '3c90c3cc-0d44-4b50-8888-8dd25736052a',
'trigger_now' => true
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"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"
payload := strings.NewReader("{\n \"name\": \"<string>\",\n \"domain\": \"<string>\",\n \"owner\": \"<string>\",\n \"source_table\": \"<string>\",\n \"preset\": \"balanced\",\n \"label_column\": \"<string>\",\n \"positive_class_value\": \"<unknown>\",\n \"feature_columns\": [\n \"<string>\"\n ],\n \"exclude_columns\": [\n \"<string>\"\n ],\n \"group_column\": \"<string>\",\n \"action_column\": \"<string>\",\n \"reward_column\": \"<string>\",\n \"probability_column\": \"<string>\",\n \"context_columns\": [\n \"<string>\"\n ],\n \"train_split_cutoff\": \"<string>\",\n \"time_column\": \"<string>\",\n \"hyperparameter_overrides\": {},\n \"resources\": {\n \"executor_instances\": 2,\n \"executor_cores\": 2,\n \"executor_memory\": \"8g\",\n \"executor_memory_overhead\": \"1g\",\n \"driver_memory\": \"4g\",\n \"driver_cores\": 2\n },\n \"enable_shap\": true,\n \"experiment_name\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"feature_engineering_job_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trigger_now\": true\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
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.post("https://api.example.com/api/automl/train")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"name\": \"<string>\",\n \"domain\": \"<string>\",\n \"owner\": \"<string>\",\n \"source_table\": \"<string>\",\n \"preset\": \"balanced\",\n \"label_column\": \"<string>\",\n \"positive_class_value\": \"<unknown>\",\n \"feature_columns\": [\n \"<string>\"\n ],\n \"exclude_columns\": [\n \"<string>\"\n ],\n \"group_column\": \"<string>\",\n \"action_column\": \"<string>\",\n \"reward_column\": \"<string>\",\n \"probability_column\": \"<string>\",\n \"context_columns\": [\n \"<string>\"\n ],\n \"train_split_cutoff\": \"<string>\",\n \"time_column\": \"<string>\",\n \"hyperparameter_overrides\": {},\n \"resources\": {\n \"executor_instances\": 2,\n \"executor_cores\": 2,\n \"executor_memory\": \"8g\",\n \"executor_memory_overhead\": \"1g\",\n \"driver_memory\": \"4g\",\n \"driver_cores\": 2\n },\n \"enable_shap\": true,\n \"experiment_name\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"feature_engineering_job_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trigger_now\": true\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/train")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"name\": \"<string>\",\n \"domain\": \"<string>\",\n \"owner\": \"<string>\",\n \"source_table\": \"<string>\",\n \"preset\": \"balanced\",\n \"label_column\": \"<string>\",\n \"positive_class_value\": \"<unknown>\",\n \"feature_columns\": [\n \"<string>\"\n ],\n \"exclude_columns\": [\n \"<string>\"\n ],\n \"group_column\": \"<string>\",\n \"action_column\": \"<string>\",\n \"reward_column\": \"<string>\",\n \"probability_column\": \"<string>\",\n \"context_columns\": [\n \"<string>\"\n ],\n \"train_split_cutoff\": \"<string>\",\n \"time_column\": \"<string>\",\n \"hyperparameter_overrides\": {},\n \"resources\": {\n \"executor_instances\": 2,\n \"executor_cores\": 2,\n \"executor_memory\": \"8g\",\n \"executor_memory_overhead\": \"1g\",\n \"driver_memory\": \"4g\",\n \"driver_cores\": 2\n },\n \"enable_shap\": true,\n \"experiment_name\": \"<string>\",\n \"registered_model_name\": \"<string>\",\n \"feature_engineering_job_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trigger_now\": true\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": {}
}
]
}{
"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
Body
application/json
Request to train an AutoML model.
Required fields depend on problem_type. See each field's description
for details. Invalid combinations return a 422 validation error.
Human-readable training run name.
Business domain for governance.
Username who owns this run.
Fully qualified Iceberg training table in catalog.schema.table format.
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 Value treated as the positive class for binary classification.
Override feature columns. If omitted, the job infers features from the table schema, excluding the label, time/group/bandit role columns, and any exclude_columns.
Columns to drop from inferred features. For example, ID columns, entity-grain columns, or columns like trans_num. Ignored when the caller sets feature_columns.
Group/query identifier for ranking.
Date cutoff string in International Organization for Standardization (ISO) 8601 format. Rows with the time column before the cutoff go to train, those at or after go to test. Defaults to an 80/20 random split when omitted.
Column used by train_split_cutoff. Required when you configure train_split_cutoff.
Optional Spark resource overrides for the training DAG.
Any field you omit auto-sizes from the dataset's row count,
byte size, and feature width via automl_resource_sizing. A field
you provide always wins over the derived value. Memory follows
Spark's <n>g / <n>m format. For example, "8g".
Show child attributes
Show child attributes
Response
Job created with rendered DAG.
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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