Model Registry logical model

This document provides a brief summary of the Model Registry logical model and its entities.

Registered Model

Represent logically a given ML model.

Examples:

a given Git repo
a given Container image registry repo/name
a given Hugging Face repo
“ChatGPT”, “Stable Diffusion”, “Phi”, “Mistral”

Note

Implemented as MLMD Context.

Model Version

Represent a specific version of a given Registered Model.

Examples:

A git tag
A container image tag
“v1.0”

Note

Implemented as a MLMD Context with parent-context to the Registered Model. To avoid name clash this is technically named with the prefix of the owned entity (Registered Model).

Model Artifact

Represent the ML model artifact in a given Model Version.

Examples:

An ONNX file
A Pickle file
A checkpoint .ckpt file

Provides several properties which can be used to locate and identify the model artifact/file (on storage). Some of the properties, such as storage_key and storage_path, are loosely modeled after KServe InferenceService’s storage spec for historical reasons (and integration blueprints), but we found it provides a flexible, general logical structure to capture “a storage pointer and resolver”, which can be applied to several technical solutions beyond KServe or Model Mesh.

uri can be used as a general mechanism to locate the model artifact/file on storage. We recommend using the URI protocol to identify the kind of storage, while the remainder to locate the artifact/file. This makes it possible for downstream use to leverage URI as the resolution mechanism, eventually augmented with credentials to be supplied by the platform's idiomatic choice for secret management and access control.

storage_key can be used to reference the access key to be used in conjunction with URI (above) in order to gain access to the artifact/file.

For example, this can be the name of a Kubernetes Secret or a given entry in a Vault.

storage_path might be used to further qualify the location of the model artifact/file within the URI.

Further attributes which might be needed or helpful to provide access to the Model artifact/file at the URI, shall be placed in the customProperties.

The combination of uri, storage_key, storage_path and additional customProperties might be used downstream to determine access strategy to the model artifact/file at the uri, for example:

use CustomStorageInitializer: https://kserve.github.io/website/master/modelserving/storage/storagecontainers/
use the python function from the point above in a non-Kubernetes context
reimplement the python function from the point above in a non-Kubernetes context and in the preferred language of choice

Note

Implemented as a MLMD Artifact with MLMD Association to the Model Version. To avoid name clashes this is technically named with the prefix of the owned entity (Model Version).

Examples of Model Artifact details

Please notice the method register_model() is a fluent way to do at-once upsertion of RegisteredModel, ModelVersion along with the ModelArtifact.

A ML model from GitHub:

model = registry.register_model(
    "my-model-from-gh",  
    "https://github.com/tarilabs/demo20231212/raw/main/v1.nb20231206162408/mnist.onnx",  
    version="v1",
    description="used for demo purposes",
    model_format_name="onnx",
    model_format_version="1"
)

resulting in:

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/1" \                     
  -H 'accept: application/json' | jq
{
  "createTimeSinceEpoch": "1707765353963",
  "customProperties": {},
  "id": "1",
  "lastUpdateTimeSinceEpoch": "1707765353963",
  "name": "my-model-from-gh",
  "state": "LIVE"
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/1/versions?pageSize=100&orderBy=ID&sortOrder=DESC&nextPageToken=" \
  -H 'accept: application/json' | jq 
{
  "items": [
    {
      "author": "mmortari",
      "createTimeSinceEpoch": "1707765354455",
      "customProperties": {},
      "description": "used for demo purposes",
      "id": "2",
      "lastUpdateTimeSinceEpoch": "1707765354455",
      "name": "v1",
      "state": "LIVE"
    }
  ],
  "nextPageToken": "",
  "pageSize": 100,
  "size": 1
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/model_versions/2/artifacts" \
  -H 'accept: application/json' | jq
{
  "items": [
    {
      "artifactType": "model-artifact",
      "createTimeSinceEpoch": "1707765355147",
      "customProperties": {},
      "id": "1",
      "lastUpdateTimeSinceEpoch": "1707765355147",
      "modelFormatName": "onnx",
      "modelFormatVersion": "1",
      "name": "my-model-from-gh",
      "state": "UNKNOWN",
      "uri": "https://github.com/tarilabs/demo20231212/raw/main/v1.nb20231206162408/mnist.onnx"
    }
  ],
  "nextPageToken": "",
  "pageSize": 0,
  "size": 1
}

A ML model from S3 bucket referencing a K8s Secret:

model = registry.register_model(
    "my-model-from-s3",  
    "s3://mybucket/v1.nb20231222141832/mnist.onnx",  
    version="v1.nb20231222141832",
    description="used for demo purposes",
    model_format_name="onnx",
    model_format_version="1",
    storage_key="aws-connection-mybucket",
    storage_path="v1.nb20231222141832"
)

please notice:

storage_key can be used to reference a Kubernetes Secret, containing AWS_S3_ENDPOINT, AWS_S3_BUCKET, etc.
storage_path can be used in place of determining the bucket and path from s3:// -like uri, and rely on the storage_key secret’s AWS_S3_BUCKET

resulting in:

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/3" \       
  -H 'accept: application/json' | jq 
{
  "createTimeSinceEpoch": "1707765756856",
  "customProperties": {},
  "id": "3",
  "lastUpdateTimeSinceEpoch": "1707765756856",
  "name": "my-model-from-s3",
  "state": "LIVE"
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/3/versions?pageSize=100&orderBy=ID&sortOrder=DESC&nextPageToken=" \
  -H 'accept: application/json' | jq 
{
  "items": [
    {
      "author": "mmortari",
      "createTimeSinceEpoch": "1707765757254",
      "customProperties": {},
      "description": "used for demo purposes",
      "id": "4",
      "lastUpdateTimeSinceEpoch": "1707765757254",
      "name": "v1.nb20231222141832",
      "state": "LIVE"
    }
  ],
  "nextPageToken": "",
  "pageSize": 100,
  "size": 1
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/model_versions/4/artifacts" \                                                       
  -H 'accept: application/json' | jq
{
  "items": [
    {
      "artifactType": "model-artifact",
      "createTimeSinceEpoch": "1707765757755",
      "customProperties": {},
      "id": "2",
      "lastUpdateTimeSinceEpoch": "1707765757755",
      "modelFormatName": "onnx",
      "modelFormatVersion": "1",
      "name": "my-model-from-s3",
      "state": "UNKNOWN",
      "storageKey": "aws-connection-mybucket",
      "storagePath": "v1.nb20231222141832",
      "uri": "s3://mybucket/v1.nb20231222141832/mnist.onnx"
    }
  ],
  "nextPageToken": "",
  "pageSize": 0,
  "size": 1
}

A ML model from S3 bucket with all required location details except of credentials:

model = registry.register_model(
    "my-model-from-s3-anotherone",  
    "s3://mybucket/v1.nb20231222141832/mnist.onnx",  
    version="v1.nb20231222141832",
    description="used for demo purposes",
    model_format_name="onnx",
    model_format_version="1",
    storage_key="aws-connection-mybucket",
    storage_path="v1.nb20231222141832",
    metadata={
      key: os.environ[key] for key in ['AWS_S3_ENDPOINT', 'AWS_S3_BUCKET', 'AWS_DEFAULT_REGION'] if key in os.environ
    }
)

resulting in:

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/5" \                                                               
  -H 'accept: application/json' | jq 
{
  "createTimeSinceEpoch": "1707765990857",
  "customProperties": {},
  "id": "5",
  "lastUpdateTimeSinceEpoch": "1707765990857",
  "name": "my-model-from-s3-anotherone",
  "state": "LIVE"
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/5/versions?pageSize=100&orderBy=ID&sortOrder=DESC&nextPageToken=" \
  -H 'accept: application/json' | jq 
{
  "items": [
    {
      "author": "mmortari",
      "createTimeSinceEpoch": "1707765991255",
      "customProperties": {
        "AWS_DEFAULT_REGION": {
          "string_value": "us-east-1"
        },
        "AWS_S3_BUCKET": {
          "string_value": "mybucket"
        },
        "AWS_S3_ENDPOINT": {
          "string_value": "https://minio-..."
        }
      },
      "description": "used for demo purposes",
      "id": "6",
      "lastUpdateTimeSinceEpoch": "1707765991255",
      "name": "v1.nb20231222141832",
      "state": "LIVE"
    }
  ],
  "nextPageToken": "",
  "pageSize": 100,
  "size": 1
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/model_versions/6/artifacts" \                                                      
  -H 'accept: application/json' | jq
{
  "items": [
    {
      "artifactType": "model-artifact",
      "createTimeSinceEpoch": "1707765991755",
      "customProperties": {},
      "id": "3",
      "lastUpdateTimeSinceEpoch": "1707765991755",
      "modelFormatName": "onnx",
      "modelFormatVersion": "1",
      "name": "my-model-from-s3-anotherone",
      "state": "UNKNOWN",
      "storageKey": "aws-connection-mybucket",
      "storagePath": "v1.nb20231222141832",
      "uri": "s3://mybucket/v1.nb20231222141832/mnist.onnx"
    }
  ],
  "nextPageToken": "",
  "pageSize": 0,
  "size": 1
}

or with supplied-values:

model = registry.register_model(
    "my-model-from-s3-anotherone",  
    "s3://mybucket/v1.nb20231222141832/mnist.onnx",  
    version="v1.nb20231222141832",
    description="used for demo purposes",
    model_format_name="onnx",
    model_format_version="1",
    storage_key="aws-connection-mybucket",
    storage_path="v1.nb20231222141832",
    metadata={
        "AWS_S3_ENDPOINT": "https://minio-...",
        "AWS_S3_BUCKET": "mybucket",
        "AWS_DEFAULT_REGION": "us-east-1",
    }
)

please note this example contains all the required details in order to reproduce access to the S3 bucket, with the exception of AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY credentials which were omitted while indexing on Model Registry intentionally.

These additional details can also be used by the downstream solution, for example, to:

first, create a K8s Secret “aws-connection-mybucket” with the Endpoint, Bucket, Region and externally user-supplied credentials,
then, fallback into the previous example use-case

A ML model from S3 bucket using a ServiceAccount:

model = registry.register_model(
    "mnist-s3",  
    "s3://kserve-examples/mnist",  
    version="v1",
    description="used for demo purposes",
    model_format_name="onnx",
    model_format_version="1",
    service_account_name="sa",
)

this example make reference to KServe tutorial: https://kserve.github.io/website/master/modelserving/storage/s3/s3/#deploy-the-model-on-s3-with-inferenceservice

resulting in:

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/7" \                                                               
  -H 'accept: application/json' | jq 
{
  "createTimeSinceEpoch": "1707766117250",
  "customProperties": {},
  "id": "7",
  "lastUpdateTimeSinceEpoch": "1707766117250",
  "name": "mnist-s3",
  "state": "LIVE"
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/registered_models/7/versions?pageSize=100&orderBy=ID&sortOrder=DESC&nextPageToken=" \
  -H 'accept: application/json' | jq 
{
  "items": [
    {
      "author": "mmortari",
      "createTimeSinceEpoch": "1707766117651",
      "customProperties": {},
      "description": "used for demo purposes",
      "id": "8",
      "lastUpdateTimeSinceEpoch": "1707766117651",
      "name": "v1",
      "state": "LIVE"
    }
  ],
  "nextPageToken": "",
  "pageSize": 100,
  "size": 1
}

curl --silent -X 'GET' \
  "$MR_HOSTNAME/api/model_registry/v1alpha3/model_versions/8/artifacts" \                                                       
  -H 'accept: application/json' | jq
{
  "items": [
    {
      "artifactType": "model-artifact",
      "createTimeSinceEpoch": "1707766118244",
      "customProperties": {},
      "id": "4",
      "lastUpdateTimeSinceEpoch": "1707766118244",
      "modelFormatName": "onnx",
      "modelFormatVersion": "1",
      "name": "mnist-s3",
      "serviceAccountName": "sa",
      "state": "UNKNOWN",
      "uri": "s3://kserve-examples/mnist"
    }
  ],
  "nextPageToken": "",
  "pageSize": 0,
  "size": 1
}

Doc Artifact

Represent additional documentation artifacts in a given Model Version.

Examples:

A README.md file

Note

Implemented as a MLMD Artifact with MLMD Association to the Model Version. To avoid name clashes this is technically named with the prefix of the owned entity (Model Version).

Logical model diagram

This diagram summarizes the relationship between the entities:

classDiagram
    class RegisteredModel{
        +String name
        +String owner
        +Map customProperties
    }
    class ModelVersion{
        +String name
        +String author
        +Map customProperties
    }
    class Artifact{
        Enum artifactType
        +String name
        +String uri
        +Map customProperties
    }
    class ModelArtifact{
        +String modelFormatName
        +String modelFormatVersion
        +String storageKey
        +String storagePath
        +String serviceAccountName 
    }
    RegisteredModel "1" <--> "*" ModelVersion
    ModelVersion "0..1" -- "*" Artifact
    ModelArtifact --|> Artifact
    DocArtifact --|> Artifact

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note: in order to keep the diagram simple, only the most relevant properties of each entity are depicted.

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logical_model.md

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Model Registry logical model

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Model Version

Model Artifact

Examples of Model Artifact details

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Logical model diagram

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Model Registry logical model

Registered Model

Model Version

Model Artifact

Examples of Model Artifact details

Doc Artifact

Logical model diagram