GS_OPT_MODEL

GS_OPT_MODEL is a data table used when the AI engine is enabled to predict the planned time. It records the configurations, training results, features, corresponding system functions, and training history of machine learning models.

**Table 1** GS_OPT_MODEL columns
Name	Type	Description
template_name	name	Template name of the machine learning model, which determines the APIs called for training and prediction. Currently, only rlstm is implemented.
model_name	name	Model name. Each model corresponds to a set of parameters, training logs, and model coefficients in the AI engine online learning process. The name must be unique.
datname	name	Name of the database served by the model. Each model is specific to a single database. This parameter determines data used for training.
ip	name	IP address of the host where the AI engine is deployed
port	integer	Listening port number of the AI engine
max_epoch	integer	Maximum number of iterations in an epoch
learning_rate	real	Learning rate of model training. The default value 1 is recommended.
dim_red	real	Number of model feature dimensions whose retention is reduced
hidden_units	integer	Number of neurons in the model's hidden layer. If the model cannot be converged for a long time, increase the value of this parameter.
batch_size	integer	Size of a batch in each iteration. It is recommended that the size be greater than or equal to the total training data volume to accelerate model convergence.
feature_size	integer	Length of the model feature, which is used to trigger retraining. This parameter is automatically updated after model training and does not need to be specified.
available	Boolean	Whether the model is converged. This parameter does not need to be specified.
Is_training	Boolean	Whether the model is being trained. This parameter does not need to be specified.
label	"char"[]	Target task of the model. S: startup time T: total time R: rows M: peak memory Currently, {S, T} or {R} is recommended due to model performance restrictions.
max	bigint[]	Maximum value of each task label of the model, which is used to trigger retraining. This parameter does not need to be specified.
acc	real[]	Accuracy of each model task. This parameter does not need to be specified.
description	text	Model comment