pg_onnx

• ONNX: Open Neural Network Exchange
• ONNX Runtime integrated with PostgreSQL. Perform ML inference with data in your database.
• PostgreSQL creates a process every new client connects. If every process ran ONNX inference, the system and GPU would run out of memory. pg_onnx runs onnxruntime-server as a background worker and creates and recycles one onnx runtime session per ONNX file.

graph LR
    subgraph P[PostgreSQL]
        direction LR
        PM((postmaster))
        PS1[postgres]
        PS2[postgres]
        PS3[postgres]
        TABLE[(onnx model data)]
        subgraph ONNX[pg_onnx Background Worker]
            direction TB
            OS[onnxruntime-server]
            ONNX1([onnxruntime\nsession:\nmodel1])
            ONNX2([onnxruntime\nsession:\nmodel2])
            OS -. " create/execute session " .-o ONNX1 & ONNX2
        end
        PM -. " fork " .-o PS1 & PS2 & PS3
        PM -. " dynamic background worker " .-o ONNX
        PS3 <-- " import model " --> TABLE
        PS1 & PS2 & PS3 <-- " ONNX operation " --> ONNX
    end
    C[Client trying to use pg_onnx] ==> PS3

---

• Build pg_onnx
  • Requirements
  • Install ONNX Runtime
  • Install dependencies
  • Compile and Install
• How to use
  • Install extension
  • Simple Usage
  • Using inference results with triggers
• Functions
  • ONNX Model Functions
    • pg_onnx_import_model(TEXT, TEXT, BYTEA, JSONB, TEXT)
    • pg_onnx_drop_model(TEXT, TEXT)
    • pg_onnx_list_model()
    • pg_onnx_inspect_model_bin(BYTEA)
  • ONNX Session Functions
    • pg_onnx_create_session(TEXT, TEXT)
    • pg_onnx_describe_session(TEXT, TEXT)
    • pg_onnx_execute_session(TEXT, TEXT, JSONB)
    • pg_onnx_destroy_session(TEXT, TEXT, JSONB)
    • pg_onnx_list_session()

---

Build pg_onnx

Requirements

• PostgreSQL
• ONNX Runtime
• Boost
• CMake
• CUDA(optional, for Nvidia GPU support)

---

Install ONNX Runtime

Linux

• Use download-onnxruntime-linux.sh script
  • This script downloads the latest version of the binary and install to /usr/local/onnxruntime.
  • Also, add /usr/local/onnxruntime/lib to /etc/ld.so.conf.d/onnxruntime.conf and run ldconfig.
• Or manually download binary from ONNX Runtime Releases.

Mac OS

brew install onnxruntime

---

Install dependencies

Ubuntu/Debian

sudo apt install cmake libboost-all-dev libpq-dev postgresql-server-dev-all
# optional, for Nvidia GPU support
sudo apt install nvidia-cuda-toolkit nvidia-cudnn

Mac OS

brew install cmake boost postgresql

---

Compile and Install

Git clone

• Clone the repository with submodules.
  • ONNX Runtime Server is included as a submodule.
  • If you already cloned the repository, run git submodule update --init --recursive to update submodules.

git clone --recursive https://github.com/kibae/pg_onnx.git

cmake -B build -S . -DCMAKE_BUILD_TYPE=Release
cmake --build build --target pg_onnx --parallel
sudo cmake --install build/pg_onnx

---

How to use

Install extension

CREATE EXTENSION IF NOT EXISTS pg_onnx;

Simple Usage

• Import an ONNX file and get the inference results.

SELECT pg_onnx_import_model(
               'sample_model', --------------- model name
               'v20230101', ------------------ model version 
               PG_READ_BINARY_FILE('/your_model_path/model.onnx')::bytea, -- model binary data
               '{"cuda": true}'::jsonb, ------ options
               'sample model' ---------------- description
       );

SELECT pg_onnx_execute_session(
               'sample_model', -- model name
               'v20230101', ----- model version
               '{
                 "x": [[1], [2], [3]],
                 "y": [[3], [4], [5]],
                 "z": [[5], [6], [7]]
               }' --------------- inputs
       );

• Depending on the type and shape of the inputs and outputs of the ML model, you can see different results. Below is an example of the result.

                            pg_onnx_execute                             
--------------------------------------------------------------------------------
 {"output": [[0.7488641738891602], [0.8607008457183838], [0.9725375175476074]]}

Using inference results with triggers

• When data is added, use the BEFORE INSERT trigger to update some columns with ML inference results.
• Depending on your ML model, this can have a significant performance impact, so be careful when using it.
• Example

-- Create a test table
CREATE TABLE trigger_test
(
    id         SERIAL PRIMARY KEY,
    value1     INT,
    value2     INT,
    value3     INT,
    prediction FLOAT
);

-- Create a trigger function
CREATE OR REPLACE FUNCTION trigger_test_insert()
    RETURNS TRIGGER AS
$$
DECLARE
    result jsonb;
BEGIN
    result := pg_onnx_execute_session(
            'sample_model', 'v20230101',
            JSONB_BUILD_OBJECT(
                    'x', ARRAY [[NEW.value1]],
                    'y', ARRAY [[NEW.value2]],
                    'z', ARRAY [[NEW.value3]]));

    -- output shape: float[-1,1]
    -- eg: {"output": [[0.6492120623588562]]}
    NEW.prediction := result -> 'output' -> 0 -> 0;
    RETURN NEW;
END;
$$
    LANGUAGE plpgsql;

-- Create a trigger
CREATE TRIGGER trigger_test_insert
    BEFORE INSERT
    ON trigger_test
    FOR EACH ROW
EXECUTE PROCEDURE trigger_test_insert();

---

Functions

• Provides several functions for importing ONNX file and executing and managing it.
• ONNX Model Functions
  • pg_onnx_import_model(TEXT, TEXT, BYTEA, JSONB, TEXT)
  • pg_onnx_drop_model(TEXT, TEXT)
  • pg_onnx_list_model()
  • pg_onnx_inspect_model_bin(BYTEA)
• ONNX Session Functions
  • pg_onnx_create_session(TEXT, TEXT)
  • pg_onnx_describe_session(TEXT, TEXT)
  • pg_onnx_execute_session(TEXT, TEXT, JSONB)
  • pg_onnx_destroy_session(TEXT, TEXT, JSONB)
  • pg_onnx_list_session()