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.
In your Python environment, use:
pip install omlmd
!!! question "Why do I need a Python environment?"
This SDK follows the same prerequisites as [InstructLab](https://github.com/instructlab/instructlab?tab=readme-ov-file#-installing-ilab) and is intented to offer Pythonic way to create OCI Artifact for ML model and metadata.
For general CLI tools for containers, we invite you to checkout [Podman](https://podman.io) and all the [Containers](https://github.com/containers/#%EF%B8%8F-tools) toolings.
Store ML model file model.joblib and its metadata in the OCI repository at localhost:8080:
=== "Python"
```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)
```
=== "CLI"
```sh
omlmd push localhost:8080/mmortari/mlartifact:v1 model.joblib --metadata md.json
```
Fetch everything in a single pull:
=== "Python"
```py
omlmd.pull(target="localhost:8080/matteo/ml-artifact:latest", outdir="tmp/b")
```
=== "CLI"
```sh
omlmd pull localhost:8080/mmortari/mlartifact:v1 -o tmp/a
```
Or fetch only the ML model assets:
=== "Python"
```py
omlmd.pull(target="localhost:8080/matteo/ml-artifact:latest", outdir="tmp/b", media_types=["application/x-mlmodel"])
```
=== "CLI"
```sh
omlmd pull localhost:8080/mmortari/mlartifact:v1 -o tmp/b --media-types "application/x-mlmodel"
```
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
=== "Python"
```py
md = omlmd.get_config(target="localhost:8080/matteo/ml-artifact:latest")
print(md)
```
=== "CLI"
```sh
omlmd get config localhost:8080/mmortari/mlartifact:v1
```
Client-side crawling of metadata.
Note: Server-side analogous coming soon/reference in blueprints.
=== "Python"
```py
crawl_result = omlmd.crawl([
"localhost:8080/matteo/ml-artifact:v1",
"localhost:8080/matteo/ml-artifact:v2",
"localhost:8080/matteo/ml-artifact:v3"
])
```
=== "CLI"
```sh
omlmd crawl localhost:8080/mmortari/mlartifact:v1 localhost:8080/mmortari/mlartifact:v2 localhost:8080/mmortari/mlartifact:v3
```
Demonstrate integration of crawling results with querying (in this case using jQ)
Of the crawled ML OCI artifacts, which one exhibit the max accuracy?
=== "Python"
```py
import jq
jq.compile( "max_by(.config.customProperties.accuracy).reference" ).input_text(crawl_result).first()
```
=== "CLI"
```sh
omlmd crawl \
localhost:8080/mmortari/mlartifact:v1 \
localhost:8080/mmortari/mlartifact:v2 \
localhost:8080/mmortari/mlartifact:v3 \
| jq "max_by(.config.customProperties.accuracy).reference"
```