docs: initial proposal for OCI artifact registry

Partially addresses kubeflow/community#682

Signed-off-by: Ramkumar Chinchani <[email protected]>

docs/oci-registry.md

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+# OCI Registry as a Kubeflow Model Registry
+
+## Authors
+
+- Ramkumar Chinchani (Cisco)
+- _TBD_
+
+## Maintainers
+
+- Ramkumar Chinchani (Cisco)
+- _TBD_
+
+## Motivation
+
+According to the [Kubeflow 2023
+survey](https://blog.kubeflow.org/kubeflow-user-survey-2023/), 44% of users
+identified Model Registry as one of the big gaps in the user’s ML Lifecycle
+missing from the Kubeflow offering. 
+
+![Kubeflow survey](diagrams/model-registry-kubeflowsurvey.png "Kubeflow survey")
+
+## Solution Overview
+
+[Open Container Initiative](https://opencontainers.org/) is a sibling (to CNCF)
+organization under [The Linux Foundation](https://www.linuxfoundation.org/)
+which has the container
+[runtime](https://github.com/opencontainers/runtime-spec),
+[image](https://github.com/opencontainers/image-spec) and
+[distribution](https://github.com/opencontainers/distribution-spec)
+specifications under its purvey which are vendor-neutral contracts that the Kubernetes
+ecosystem relies on for running, filesystem layout, and pushing and pulling of
+container images.
+
+However, recent developments in the OCI, specifically
+[_image_](https://github.com/opencontainers/image-spec/releases/tag/v1.1.0) and
+[_distribution_](https://github.com/opencontainers/distribution-spec/releases/tag/v1.1.0)
+spec **v1.1.0**, have included support for pushing arbitrary artifacts along
+with support for relationships between artifacts.
+
+## OCI v1.1.0 Conformant Registries
+
+The following are the highlights about OCI artifact registries.
+
+- Container images: these represent workloads and have been the traditional use case for an OCI conformant registry.
+
+- Artifacts: these represent arbitrary data (ML model data or additional
+  metadata in this context) that can also be pushed and pulled from an OCI
+  conformant registry.
+
+- Content-addressable: all data is organized as a Merkle DAG with SHA256-hashed
+  blobs. This bodes well for reproducibility.
+
+- Versioning: apart from the SHA256 hash, all data can be tagged with a human-readable version.
+
+- Annotations: there is provision to append arbitrary annotations to any artifact.
+
+- References: an artifact can be pushed along with a reference to another
+  artifact (via the `Subject` field) which can be leveraged to address the data
+  lineage use case.
+
+- Provenance: each artifact can be cryptographically signed, with the signature
+  as its own separate artifact "referring" to the signed artifact.
+
+- Ecosystem tooling: there are OCI v1.1.0 conformant registries and clients
+  already available which can be leveraged.
+
+- Infrastructure reuse: a container image registry is already a critical piece of
+  infrastructure which can now be reused.
+
+
+## References
+
+_TBD_
+
+# Appendix
+
+The following section demonstrates the supported workflow.
+
+NOTE: This section is not an endorsement of all of the tools used but merely
+represents a demonstration. Readers are free to pick and choose any tool as
+they see fit with the requirement that the choice should be OCI v1.1.0
+conformant.
+
+[`zot`](https://zotregistry.dev) is chosen as the registry and
+[`regctl`](https://github.com/regclient/regclient) as the client.
+
+## Start a registry
+
+```bash
+podman run -p 5000:5000 ghcr.io/project-zot/zot-linux-amd64:latest
+```
+
+## Download model data
+
+```bash
+curl -v -L0 https://github.com/tarilabs/demo20231212/raw/main/v1.nb20231206162408/mnist.onnx -o mnist.onnx
+```
+
+## Upload model data with annotations
+
+```bash
+regctl artifact put \
+  --annotation description="used for demo purposes" \
+  --annotation model_format_name="onnx" \
+  --annotation model_format_version="1" \
+  --artifact-type "application/vnd.model.type" \
+  localhost:5000/models/my-model-from-gh:v1 \
+  -f mnist.onnx
+```
+
+## List all artifacts
+
+```bash
+regctl artifact list localhost:5000/models/my-model-from-gh:v1 --format '{{jsonPretty .}}
+```
+
+## Filter by artifact type
+
+```bash
+regctl artifact list --filter-artifact-type "application/vnd.model.type" localhost:5000/models/my-model-from-gh:v1 --format '{{jsonPretty .}}'
+```
+
+## Download model data
+
+```bash
+regctl artifact get localhost:5000/models/my-model-from-gh:v1 > mnist.onnx.copy
+```