Welcome to Model Management Service for AIML Framework

Model Management Service Overview

Model Management Service works with AIML Framework to manage the life cycle of trained AIML models, such as creating a model, storing the trained model, storing the trained model info. It exposes REST based API to work with models.

Release-Notes

This document provides the release notes for the I release of Model Management Service

Version history

Date

Ver.

Author

Comment

2023-12-14

1.0.0

Sandeep Jaisawal

I release

Summary

The I release of AIMLFW Model Management Service provides basic version of Model Management Service

Release Data

I Release

Project

AIMLFW Model Management Service

Repo/commit-ID

aiml-fw/awmf/modelmgmtservice /9310c67f6446c77ca9803db316a44046a36c5978

Release designation

I release

Release date

2023-12-29

Purpose of the delivery

Initial version of Model Management Service to manage life cycle of trained AIML models

User-Guide

Overview

Model Management Service works with AIML Framework to manage the life cycle of trained AIML models, such as creating a model, storing the trained model, storing the trained model info. It exposes REST based API to work with models.

Steps to build and run Model Management Service Standalone

Prerequisites

  1. Install go

Steps

 git clone "https://gerrit.o-ran-sc.org/r/aiml-fw/awmf/modelmgmtservice.git"
cd modelmgmtservice
Update ENV variables in config.env
Execute below commands
export $(< ./config.env)
go get
go build -o mme_bin .
./mme_bin

Steps to run Model Management Service using AIMLFW deployment scripts

Follow the steps in this link: AIMLFW installation guide

APIs and samples

  1. Registering a model in Model Management Service Sample model-name value is “qos_301”

    curl  -i  -H "Content-Type: application/json"  \
        -X POST \
        -d '{"model-name":"qos_301", "rapp-id": "rapp_1", "meta-info" : {"accuracy":"90","model-type":"timeseries","feature-list":["pdcpBytesDl","pdcpBytesUl"]}}' \
        http://127.0.0.1:32006/registerModel

  2. Fetch trained model information from Model Management Service

    curl -X GET  http://127.0.0.1:32006/getModelInfo/qos_301

  3. Upload a trained AIML Model to Model Management Service

    curl -F "file=@<MODEL_ZIP_FILE_NAME>" http://127.0.0.1:32006/uploadModel/qos_301

  4. Download a trained model from Model Management Service

    curl -X GET http://127.0.0.1:32006/downloadModel/qos_301/model.zip --output model.zip