Label Studio default timeout settings for ML server requests

Label studio has default timeouts for all types of requests to ML server.

There are several different requests to ML server:

1. Health - request to check ML backend health status when adding new ML backend (env variable ML_TIMEOUT_HEALTH)
2. Setup - request to setup ML backend, initialize ML model (env variable ML_TIMEOUT_SETUP)
3. Predict - prediction request when Label Studio gets predictions from ML backend (env variable ML_TIMEOUT_PREDICT)
4. Train - request to train ML backend (env variable ML_TIMEOUT_PREDICT)
5. Duplicate model - duplicate model request to ML backend (env variable ML_TIMEOUT_PREDICT)
6. Delete - send delete request to ML backend (env variable ML_TIMEOUT_PREDICT)
7. Train job status - request train job status from ML backend (env variable ML_TIMEOUT_PREDICT)

You can adjust the timeout by setting an environment variables for each request or modify in Label Studio variables. These are the variables section in Label Studio (in seconds):

CONNECTION_TIMEOUT = float(get_env('ML_CONNECTION_TIMEOUT', 1)) 
TIMEOUT_DEFAULT = float(get_env('ML_TIMEOUT_DEFAULT', 100)) 
TIMEOUT_TRAIN = float(get_env('ML_TIMEOUT_TRAIN', 30))
TIMEOUT_PREDICT = float(get_env('ML_TIMEOUT_PREDICT', 100))
TIMEOUT_HEALTH = float(get_env('ML_TIMEOUT_HEALTH', 1))
TIMEOUT_SETUP = float(get_env('ML_TIMEOUT_SETUP', 3))
TIMEOUT_DUPLICATE_MODEL = float(get_env('ML_TIMEOUT_DUPLICATE_MODEL', 1))
TIMEOUT_DELETE = float(get_env('ML_TIMEOUT_DELETE', 1))
TIMEOUT_TRAIN_JOB_STATUS = float(get_env('ML_TIMEOUT_TRAIN_JOB_STATUS', 1))

You can modify them in ml/api_connector.py.