multi-model-server
Multi Model Server is a tool for serving neural net models for inference
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Java 8 or later must be installed and available in $PATH before installing MMS; MXNet must be installed separately if using MXNet models.
- 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.
License · maintenance · safety
Apache License Version 2.0 (permissive) — 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.
last release 2023-06-20 (1151 days) · last repo commit 2024-05-20 · 1,024 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 393,634 downloads/mo, #6,997 on PyPI
Alternatives
Verify before relying
pip install multi-model-server
from multi_model_server.server import start_server
# Requires Java 8+ in $PATH and model-archiver for packaging models- 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.
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 on this page. Verify before relying on it.
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
multi-model-server on PyPI
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.
Java 8 or later must be installed and available in $PATH before installing MMS; MXNet must be installed separately if using MXNet models.
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 modelsVerify 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 | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesPillowpsutilfuturemodel-archiver |
| Maintenance | Abandoned 1,151 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 393,634 / month, #6,997 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: multi_model_server-1.1.11-py2.py3-none-any.whl
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See also model-archiver · mxnet · sagemaker-inference · torch-model-archiver · mlserver · sagemaker-serve · onnx · tensorflow-serving-api · mnn · onnxruntime