Built-in Models

This document introduces SQLRec built-in model types and their usage.

Built-in Model Types

SQLRec has two built-in model types:

1. External Model

External models are used to interface with existing external model services and do not support training and export operations.

Model Name: external

Features:

• Connect to existing external model inference services
• Does not support training (TRAIN MODEL)
• Does not support export (EXPORT MODEL)
• Access services directly via URL

Configuration Parameters:

Parameter	Type	Description
url	String	External model service URL address
output_columns	String	Output column definition, format: name1:type1,name2:type2

Usage Example:

CREATE MODEL external_model WITH (
    model = 'external',
    url = 'http://external-service:8080/predict',
    output_columns = 'score:FLOAT,label:VARCHAR'
);

CREATE SERVICE external_service
    ON MODEL external_model;

2. Wide & Deep Model

Wide & Deep model is a recommendation model implemented based on the tzrec framework, supporting complete training, export, and service deployment workflow.

Model Name: tzrec.wide_and_deep

Features:

• Supports Wide & Deep architecture recommendation models
• Supports distributed training (PyTorch Distributed)
• Supports Parquet format training data
• Automatically generates Kubernetes training and service YAML
• Supports sparse and dense features

Output Fields:

Field Name	Type	Description
probs	FLOAT	Predicted probability value

Training Configuration Parameters:

Parameter	Type	Default	Description
sparse_lr	Double	0.001	Sparse feature learning rate
dense_lr	Double	0.001	Dense feature learning rate
num_epochs	Integer	1	Number of training epochs
batch_size	Integer	8192	Batch size
num_workers	Integer	8	Data loader worker process count
embedding_dim	Integer	16	Embedding dimension
num_buckets	Integer	1000000	Integer feature bucket count
hidden_units	String	"512,256,128"	Deep network hidden layer unit count
label_columns	String	-	Label column name

Distributed Training Parameters:

Parameter	Type	Default	Description
nnodes	Integer	1	Training node count
nproc_per_node	Integer	1	Processes per node
master_port	Integer	29500	Distributed training master port

Resource Configuration Parameters:

Parameter	Type	Default	Description
image	String	"sqlrec/tzrec"	Docker image name
version	String	"0.1.0-cpu"	Docker image version
pod_cpu_cores	Integer	2	Pod CPU core count
pod_memory	String	"8Gi"	Pod memory
replicas	Integer	1	Service replica count

Column-level Configuration Parameters:

Can configure parameters separately for each feature column:

Parameter Format	Description
column.{feature_name}.bucket_size	Feature bucket count
column.{feature_name}.embedding_dim	Feature embedding dimension

Usage Example:

CREATE MODEL rec_model (
    user_id VARCHAR,
    item_id VARCHAR,
    category VARCHAR,
    price DOUBLE,
    label INT
) WITH (
    model = 'tzrec.wide_and_deep',
    label_columns = 'label',
    embedding_dim = 32,
    hidden_units = '512,256,128',
    column.user_id.embedding_dim = 64,
    column.item_id.embedding_dim = 64
);

TRAIN MODEL rec_model CHECKPOINT = 'v1.0'
    ON training_data
    WITH (
        num_epochs = 10,
        batch_size = 4096,
        sparse_lr = 0.01,
        nnodes = 2,
        nproc_per_node = 4
    );

CREATE SERVICE rec_service
    ON MODEL rec_model
    CHECKPOINT = 'v1.0'
    WITH (
        replicas = 3,
        pod_cpu_cores = 4,
        pod_memory = '16Gi'
    );