aissemble-foundation-versioning-service


Nameaissemble-foundation-versioning-service JSON
Version 1.11.1 PyPI version JSON
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authoraiSSEMBLE Baseline Community
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            ## aiSSEMBLE™ Foundation Versioning

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This module contains the implementation for the aiSSEMBLE versioning service. The service provides the ability to version artifacts produced by a Solution Baseline machine-learning pipeline, and uses Maven to package and deploy the artifacts to Nexus.

### Model Versioning

The following GET endpoint is provided for model versioning: `/version/model/{run_id}`

`run_id` is the unique training run identifier assigned to an MLflow training experiment. The versioning service currently only supports models that have been trained using the MLflow implementation of a Solution Baseline machine-learning pipeline. It will package the training run artifacts produced by MLflow and the model artifacts produced when saving through MLflow.

The following environment variables are required for model versioning:
- `MLFLOW_TRACKING_URI`: the MLflow tracking URI path specified in the training pipeline. This is where MLflow saves the training run artifacts.
- `MODEL_DIRECTORY`: The model directory path specified in the training pipeline. This is where the training pipeline saves the trained models.
- `NEXUS_SERVER`: The Nexus server to use to push the versioned artifact to. The credentials for this server must be provided in the Maven settings.xml file.

### Example

Below is a basic example of how to leverage the versioning service:
- Extending the baseline docker image to include Nexus credentials
    ```dockerfile
    FROM boozallen/aissemble-versioning:${VERSION_AISSEMBLE}

    COPY /custom/maven/settings/containing/nexus/credentials.xml /root/.m2/settings.xml
    ```
- Docker-compose configurations for running the service
    ```
    example-versioning-service:
        image: my-extended-versioning-service-image:latest
        ports:
          - '80:80'
        environment:
            MLFLOW_TRACKING_URI: /path/to/mlflow/tracking/uri
            MODEL_DIRECTORY: /path/to/saved/models
            NEXUS_SERVER: http://nexus-server:port
    ```
- Note: By default Versioning requires authorization and therefore must have security services running (policy-decision-point).
    - Add the following to the above docker-compose file:
        ```
        policy-decision-point:
            image: ${CONTAINER_REGISTRY}boozallen/policy-decision-point-docker:latest
            hostname: policy-decision-point
            container_name: policy-decision-point
            ports:
              - "8780:8080"
              - "9700:9000"
        ```
    - Add an `auth.properties` file to your docker image with the following content.
        ```
        pdp_host_url=http://policy-decision-point:8080/api/pdp
        ```
    - Then add the following environment variable that points to your properties file.  
      `ENV KRAUSENING_BASE /path/to/auth.properties/file`
    - To disable authorization you can add the following to the `auth.properties` file: `is_authorization_enabled = False`.

            

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    "description": "## aiSSEMBLE™ Foundation Versioning\n\n[![PyPI](https://img.shields.io/pypi/v/aissemble-foundation-versioning-service?logo=python&logoColor=gold)](https://pypi.org/project/aissemble-foundation-versioning-service/)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/aissemble-foundation-versioning-service?logo=python&logoColor=gold)\n![PyPI - Wheel](https://img.shields.io/pypi/wheel/aissemble-foundation-versioning-service?logo=python&logoColor=gold)\n\nThis module contains the implementation for the aiSSEMBLE versioning service. The service provides the ability to version artifacts produced by a Solution Baseline machine-learning pipeline, and uses Maven to package and deploy the artifacts to Nexus.\n\n### Model Versioning\n\nThe following GET endpoint is provided for model versioning: `/version/model/{run_id}`\n\n`run_id` is the unique training run identifier assigned to an MLflow training experiment. The versioning service currently only supports models that have been trained using the MLflow implementation of a Solution Baseline machine-learning pipeline. It will package the training run artifacts produced by MLflow and the model artifacts produced when saving through MLflow.\n\nThe following environment variables are required for model versioning:\n- `MLFLOW_TRACKING_URI`: the MLflow tracking URI path specified in the training pipeline. This is where MLflow saves the training run artifacts.\n- `MODEL_DIRECTORY`: The model directory path specified in the training pipeline. This is where the training pipeline saves the trained models.\n- `NEXUS_SERVER`: The Nexus server to use to push the versioned artifact to. The credentials for this server must be provided in the Maven settings.xml file.\n\n### Example\n\nBelow is a basic example of how to leverage the versioning service:\n- Extending the baseline docker image to include Nexus credentials\n    ```dockerfile\n    FROM boozallen/aissemble-versioning:${VERSION_AISSEMBLE}\n\n    COPY /custom/maven/settings/containing/nexus/credentials.xml /root/.m2/settings.xml\n    ```\n- Docker-compose configurations for running the service\n    ```\n    example-versioning-service:\n        image: my-extended-versioning-service-image:latest\n        ports:\n          - '80:80'\n        environment:\n            MLFLOW_TRACKING_URI: /path/to/mlflow/tracking/uri\n            MODEL_DIRECTORY: /path/to/saved/models\n            NEXUS_SERVER: http://nexus-server:port\n    ```\n- Note: By default Versioning requires authorization and therefore must have security services running (policy-decision-point).\n    - Add the following to the above docker-compose file:\n        ```\n        policy-decision-point:\n            image: ${CONTAINER_REGISTRY}boozallen/policy-decision-point-docker:latest\n            hostname: policy-decision-point\n            container_name: policy-decision-point\n            ports:\n              - \"8780:8080\"\n              - \"9700:9000\"\n        ```\n    - Add an `auth.properties` file to your docker image with the following content.\n        ```\n        pdp_host_url=http://policy-decision-point:8080/api/pdp\n        ```\n    - Then add the following environment variable that points to your properties file.  \n      `ENV KRAUSENING_BASE /path/to/auth.properties/file`\n    - To disable authorization you can add the following to the `auth.properties` file: `is_authorization_enabled = False`.\n",
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