---
id: multi-model-server
version: "1.1.11"
license: Apache License Version 2.0
license_treatment: permissive
maintenance: abandoned
---

# multi-model-server — Multi Model Server is a tool for serving neural net models for inference

License: permissive · Maintenance: abandoned · Downloads: 393.6K/mo

## What it is and what it does

Multi Model Server is a framework for deploying deep learning models as HTTP services. It accepts models exported from MXNet or ONNX, packages them using model-archiver, and runs them behind HTTP endpoints that handle inference requests. The tool provides both a command-line interface and pre-built Docker images to simplify setup and deployment.

The package depends on Pillow for image handling, psutil for system monitoring, future for Python 2/3 compatibility, and model-archiver for packaging models. However, the project is no longer maintained—the repository is archived, the last commit was in May 2024, and no releases have occurred since June 2023. This means no bug fixes, security patches, or feature updates are expected going forward.

Use it for:

- Deploy MXNet or ONNX models as REST APIs for batch or real-time inference without building a custom server.
- Package trained models into archives and serve them alongside other models on a single HTTP endpoint.
- Run model inference in containerized environments using the provided Docker images for consistent deployment.
- Monitor resource usage (CPU, memory) during model serving using built-in system monitoring capabilities.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Multi Model Server is a tool for serving deep learning models exported from MXNet or ONNX via HTTP endpoints for inference requests, using a CLI or Docker images to manage the service.

No—do not install for new projects. The package is abandoned (archived repository, no commits since May 2024, last release June 2023) and will receive no maintenance, security updates, or bug fixes. If you need model serving, use actively maintained alternatives like TorchServe, TensorFlow Serving, or Seldon Core. Install only if you are maintaining legacy code that already depends on it and cannot migrate.

## Install

pip install multi-model-server
uv add multi-model-server
poetry add multi-model-server

## Installing multi-model-server

Before you install: Installation is straightforward (low friction), but the project is archived and abandoned as of 2024, with no active maintenance since May 2024. The last release was over a year ago. Consider this a legacy tool no longer receiving updates or support.

License in practice: Licensed under Apache License Version 2.0 (permissive), which allows commercial use, modification, and distribution with minimal restrictions, though you must include a copy of the license and state significant changes.

Quickstart:

pip install multi-model-server

from multi_model_server.server import start_server
# Requires Java 8+ in $PATH and model-archiver for packaging models

Java 8 or later must be installed and available in $PATH before installing MMS; MXNet must be installed separately if using MXNet models.

Verify before relying:

- Current compatibility with modern Python versions and recent MXNet/ONNX releases is unclear given the abandoned status.
- Whether the archived repository will accept security patches or bug fixes if vulnerabilities are discovered.
- Performance and stability characteristics on current hardware and frameworks compared to actively maintained alternatives.

## Package facts

- License: Apache License Version 2.0 (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: abandoned
- Downloads: 393.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

serve deep learning models http, mxnet onnx model serving, neural network inference server, model serving framework, deep learning model deployment, inference endpoint management, model archiver server, model-serving, archived-project, deep-learning

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