GraphCanon updated Sep 30, 2026 · GitHub synced Sep 30, 2026
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Decision brief
ORMB encapsulates machine learning and deep-learning models via OCI artifacts within Docker containers for streamlined model management.
Good fit when
- If you need sophisticated version control for your ML/DL models through an image registry, ORMB provides this functionality.
- For operations where integration with Harbor or other OCI-compatible registries is preferred over traditional storage methods.
Avoid when
- Should you seek simple models deployment without extensive version management features, ORMB may introduce unnecessary complexity.
- If your project strictly avoids using Docker and OCI artifacts for model handling, then this tool would not be suitable.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (978d since push)
- As of Sep 30, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 30, 2026
- Security (OSV)
- 201 low (201 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
go get github.com/kleveross/ormbSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A tool to manage machine learning and deep learning models using Docker and OCI artifacts for model and version management.
Capability facts
- Languages
- go
Source: github.language · Sep 30, 2026
Categories
Tags
README
Getting Started You can watch our sample usage video or read the text version below. First, open a model folder that meets the specification of . (See our documentation for more information about ormbfile.yaml) $ git clone https://github.com/kleveross/ormb $ cd ormb $ go mod tidy...
For agents
This page has a .md twin and JSON over the API.