torch-model-archiver
Torch Model Archiver is used for creating archives of trained neural net models that can be consumed by TorchServe inference
Decision gist · record as of 2026-08-14
Yes, if you are already using TorchServe and need to package models for serving—it is the standard tool for that task and has low install friction. However, approach with caution for new projects: the repository is archived and maintenance is abandoned, so security patches and compatibility updates with future PyTorch versions are unlikely. Evaluate whether TorchServe itself remains actively maintained and suitable for your deployment strategy.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch and a trained model file; TorchServe is needed to serve the resulting .mar archive.
- Low install friction with a single lightweight runtime dependency.
- However, the repository is archived and maintenance is abandoned as of the latest release on 2024-09-30, with no commits since 2025-08-06.
License · maintenance · safety
Apache License Version 2.0 (permissive) — Apache License Version 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
last release 2024-09-30 (683 days) · last repo commit 2025-08-06 · 4,347 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 103,470 downloads/mo, #12,803 on PyPI
Alternatives
Verify before relying
pip install torch-model-archiver
from torch_model_archiver.model_packaging import package_model
package_model(model_name, model_path, handler, export_path)- Whether the archived repository will receive security patches or bug fixes if issues are discovered.
- Compatibility with recent PyTorch and TorchServe versions given the abandoned maintenance status.
- Whether enum-compat dependency has known issues or is actively maintained.
What it is and what it does
Torch Model Archiver is a command-line tool that packages trained PyTorch neural network models into .mar (model archive) files suitable for inference serving with TorchServe. It bundles the model weights, architecture, and a handler script into a single deployable artifact that TorchServe can load and use to serve predictions. The tool is part of the TorchServe ecosystem but can be installed standalone.
The package has low install friction—it depends only on enum-compat—and is distributed as a pure Python wheel. However, the underlying repository is archived and marked as abandoned, meaning no active development or maintenance is occurring. The last release was in September 2024, and while no known vulnerabilities are recorded, the lack of ongoing maintenance may be a concern for long-term production use or compatibility with future PyTorch versions.
Use it for
- Package a trained PyTorch model into a .mar file for deployment on a TorchServe inference server.
- Automate model archiving in a CI/CD pipeline to prepare models for production serving.
- Bundle custom inference handlers with model weights for standardized deployment across teams.
- Create reproducible model archives for sharing trained models with consistent serving configuration.
- Prepare models for containerized inference services that consume TorchServe-compatible archives.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using TorchServe and need to package models for serving—it is the standard tool for that task and has low install friction.
However, approach with caution for new projects: the repository is archived and maintenance is abandoned, so security patches and compatibility updates with future PyTorch versions are unlikely. Evaluate whether TorchServe itself remains actively maintained and suitable for your deployment strategy.
Install
torch-model-archiver on PyPI
Before you install
Low install friction with a single lightweight runtime dependency. However, the repository is archived and maintenance is abandoned as of the latest release on 2024-09-30, with no commits since 2025-08-06.
Requires PyTorch and a trained model file; TorchServe is needed to serve the resulting .mar archive.
License in practice
Apache License Version 2.0 is permissive, allowing commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
Quickstart
pip install torch-model-archiver
from torch_model_archiver.model_packaging import package_model
package_model(model_name, model_path, handler, export_path)Verify before relying
- Whether the archived repository will receive security patches or bug fixes if issues are discovered.
- Compatibility with recent PyTorch and TorchServe versions given the abandoned maintenance status.
- Whether enum-compat dependency has known issues or is actively maintained.
Package facts
| License | Apache License Version 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageenum-compat |
| Maintenance | Abandoned 683 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 103,470 / month, #12,803 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: torch_model_archiver-0.12.0-py3-none-any.whl
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See also model-archiver · multi-model-server · rotary-embedding-torch · pte-adapter-model-explorer · ema-pytorch · segmentation-models-pytorch · torch · pytorch · pytorchcv · tlparse