## aiSSEMBLE™ Foundation Versioning
[](https://pypi.org/project/aissemble-foundation-versioning-service/)
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[](https://pypi.org/project/aissemble-foundation-versioning-service/)\n\n\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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