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Discover top open-source software, updated regularly with real-world adoption signals.
Version, share, and serve ML models via OCI image registry
ORMB lets you store, version, and distribute machine learning models as OCI artifacts, using Docker or Harbor registries, with simple CLI commands for save, push, pull, and export.
ORMB is a lightweight Go‑based tool that treats machine‑learning models as OCI artifacts. By leveraging existing container registries such as Harbor or Docker Registry, it provides a unified place to store, version, and retrieve models without requiring a separate database.
Developers place a model directory alongside an ormbfile.yaml specification, then use the CLI to save the model to a local cache, push it to a remote registry, it back when needed, and it to a local filesystem. All operations report digests, sizes, and format metadata, making reproducibility straightforward. ORMB integrates with CI/CD pipelines and serving frameworks like Seldon Core, enabling automated model promotion from training to production.
pullexportInstall the pre‑compiled binary or build from source, configure your preferred OCI‑compatible registry, and start managing models with a single command line. No additional UI is required, and the tool works across Linux, macOS, and Windows environments.
When teams consider ORMB, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
CI/CD pipeline integration
Automatically push trained models to a Harbor registry after each training run.
Model serving with Seldon Core
Pull versioned models from the registry for seamless deployment in production.
Cross‑team model sharing
Provide a single source of truth for model versions accessible to multiple data science teams.
Backup and archival
Store immutable snapshots of models in an OCI registry for compliance and disaster recovery.
Any OCI‑compatible registry, such as Harbor, Docker Registry, or other OCI artifact stores.
Each model is tagged like a Docker image (e.g., `my_model:v1`), and the registry tracks digests for immutability.
Yes, any model format that can be packaged and described in `ormbfile.yaml` is supported.
Metadata is displayed in the registry UI (e.g., Harbor) and also printed by the CLI during push/pull.
ORMB relies on the underlying registry’s authentication mechanisms; configure credentials as you would for Docker.
Project at a glance
DormantLast synced 4 days ago
