https://github.com/containers/omlmd
OCI Artifact for ML model & metadata
https://github.com/containers/omlmd
hacktoberfest machine-learning oci oci-artifacts
Last synced: 9 months ago
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OCI Artifact for ML model & metadata
- Host: GitHub
- URL: https://github.com/containers/omlmd
- Owner: containers
- License: apache-2.0
- Created: 2024-07-20T10:39:51.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-09-22T01:34:27.000Z (9 months ago)
- Last Synced: 2025-09-28T22:07:08.335Z (9 months ago)
- Topics: hacktoberfest, machine-learning, oci, oci-artifacts
- Language: Python
- Homepage: https://containers.github.io/omlmd
- Size: 6.69 MB
- Stars: 77
- Watchers: 1
- Forks: 7
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# OCI Artifact for ML model & metadata
[](https://github.com/containers/omlmd)
[](LICENSE)
[](https://github.com/containers/omlmd/actions/workflows/build.yaml)
[](https://github.com/containers/omlmd/actions/workflows/e2e.yaml)
[](https://pypi.org/project/omlmd)
[](https://containers.github.io/omlmd)
[](https://github.com/containers/omlmd)
[](https://www.youtube.com/watch?v=W4GwIRPXE8E&list=PLdbdefeRIj9SRbg6Hkr15GeyPH0qpk_ww)
This project is a collection of blueprints, patterns and toolchain (in the form of python SDK and CLI) to leverage OCI Artifact and containers for ML model and metadata.
Documentation: https://containers.github.io/omlmd
GitHub repository: https://github.com/containers/omlmd
YouTube video playlist: https://www.youtube.com/watch?v=W4GwIRPXE8E&list=PLdbdefeRIj9SRbg6Hkr15GeyPH0qpk_ww
Pypi distribution: https://pypi.org/project/omlmd
## Installation
> [!TIP]
> We recommend checking out the [Getting Started tutorial](https://containers.github.io/omlmd) in the documentation; below instructions are provided for a quick overview.
In your Python environment, use:
```
pip install omlmd
```
## Push
Store ML model file `model.joblib` and its metadata in the OCI repository at `localhost:8080`:
```py
from omlmd.helpers import Helper
omlmd = Helper()
omlmd.push("localhost:8080/matteo/ml-artifact:latest", "model.joblib", name="Model Example", author="John Doe", license="Apache-2.0", accuracy=9.876543210)
```
## Pull
Fetch everything in a single pull:
```py
omlmd.pull(target="localhost:8080/matteo/ml-artifact:latest", outdir="tmp/b")
```
Or fetch only the ML model assets:
```py
omlmd.pull(target="localhost:8080/matteo/ml-artifact:latest", outdir="tmp/b", media_types=["application/x-mlmodel"])
```
### Custom Pull: just metadata
The features can be composed in order to expose higher lever capabilities, such as retrieving only the metadata informatio.
Implementation intends to follow OCI-Artifact convention
```py
md = omlmd.get_config(target="localhost:8080/matteo/ml-artifact:latest")
print(md)
```
## Crawl
Client-side crawling of metadata.
_Note: Server-side analogous coming soon/reference in blueprints._
```py
crawl_result = omlmd.crawl([
"localhost:8080/matteo/ml-artifact:v1",
"localhost:8080/matteo/ml-artifact:v2",
"localhost:8080/matteo/ml-artifact:v3"
])
```
### Example query
Demonstrate integration of crawling results with querying (in this case using [jQ](https://jqlang.github.io/jq))
> Of the crawled ML OCI artifacts, which one exhibit the max accuracy?
```py
import jq
jq.compile( "max_by(.config.customProperties.accuracy).reference" ).input_text(crawl_result).first()
```
## To be continued...
Don't forget to checkout the [documentation website](https://containers.github.io/omlmd) for more information!