--- id: model-archiver version: "1.0.3" license: Apache License Version 2.0 license_treatment: permissive maintenance: abandoned --- # model-archiver — Model Archiver is used for creating archives of trained neural net models that can be consumed by MXNet-Model-Server inference License: permissive · Maintenance: abandoned · Downloads: 390.3K/mo ## What it is and what it does Model Archiver is a command-line tool that packages trained neural network models into `.mar` archive files suitable for inference with MXNet-Model-Server. It accepts a model file, a handler script, and metadata, then bundles them into a standardized archive format that MMS can consume and serve. The tool is lightweight, depending only on future and enum-compat for compatibility. The package is part of the broader MXNet-Model-Server (MMS) ecosystem but can be installed standalone. It was designed to streamline the workflow of taking a trained model from development into a serving-ready format. However, the project is now archived with no active maintenance since mid-2024, and the latest release is from 2019, making it a legacy tool that may not align with current deep-learning deployment practices or MXNet versions. Use it for: - Package a trained PyTorch or MXNet model into a `.mar` file for deployment with MXNet-Model-Server. - Automate model archiving in a CI/CD pipeline to prepare models for inference serving. - Bundle a model with a custom inference handler to define preprocessing and postprocessing logic. - Create reproducible model archives for sharing trained models across teams or environments. ## Worth the install? AI-flagged interpretation of the facts above — verify before relying. Model Archiver creates `.mar` archive files from trained neural network models for use with MXNet-Model-Server inference. No. Although install friction is low and the license is permissive, the project is abandoned (archived repository, no releases since 2019, last commit 2024-05-20). MXNet-Model-Server itself is no longer actively maintained, and this tool is unlikely to work reliably with modern Python versions or current model-serving frameworks. Consider modern alternatives like TorchServe, TensorFlow Serving, or other actively maintained model-packaging tools. ## Install pip install model-archiver uv add model-archiver poetry add model-archiver ## Installing model-archiver Before you install: Install friction is low; the package is a pure-Python wheel with only two lightweight runtime dependencies (future and enum-compat). However, the repository is archived and the latest release dates to 2019-05-16, with no commits since 2024-05-20—this is an abandoned project with no active maintenance. License in practice: Licensed under Apache License Version 2.0 (permissive), so you may use, modify, and distribute it freely in commercial and private projects, provided you include a copy of the license and state any changes made. Quickstart: pip install model-archiver model-archiver archive -m model_name -s model.pt -e handler.py -o model.mar ONNX support requires manual installation of protobuf compiler, onnx, and mxnet; the tool itself does not declare these as dependencies. Verify before relying: - Whether the tool remains compatible with current MXNet versions or modern Python releases. - Whether `.mar` files created by this version remain compatible with current MXNet-Model-Server deployments. - Specific Python version compatibility (requires_python is unspecified in the fact sheet). ## Package facts - License: Apache License Version 2.0 (permissive) - Python support: unspecified - Install friction: low - Maintenance: abandoned - Downloads: 390.3K/month (top 15,000 on PyPI) - Known vulnerabilities: none known ## Tags neural network model packaging, create model archives, mxnet model server preparation, model archiving tool, package trained models, mar file creation, deep learning model deployment, model-packaging, mxnet-legacy, archived [View on SkillFed](https://skillfed.io/packages/model-archiver) · [View on PyPI](https://pypi.org/project/model-archiver/)