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The Morpheus MLflow Helm chart offers MLflow server with Triton plugin to deploy, update, and remove models from the Morpheus AI Engine. The MLflow server UI can be accessed using NodePort `30500`. Follow the below steps to install the Morpheus MLflow:
Refer to the [Morpheus CLI Overview](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/docs/source/basics/overview.rst) and [Building a Pipeline](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/docs/source/basics/building_a_pipeline.md) documentation for more information regarding the commands.
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Refer to the [Morpheus CLI Overview](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/docs/source/basics/overview.rst) and [Building a Pipeline](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/docs/source/basics/building_a_pipeline.md) documentation for more information regarding the commands.
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> **Note**: Before running the example pipelines, ensure the criteria below are met:
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- Ensure models specific to the pipeline are deployed.
Copy file name to clipboardexpand all lines: docs/source/getting_started.md
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### Pull the Morpheus Image
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1. Go to [https://catalog.ngc.nvidia.com/orgs/nvidia/teams/morpheus/containers/morpheus/tags](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/morpheus/containers/morpheus/tags)
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1. Choose a version
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1. Download the selected version, for example for `24.03`:
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1. Download the selected version, for example for `24.06`:
> Morpheus uses Calendar Versioning ([CalVer](https://calver.org/)). For each Morpheus release there will be an image tagged in the form of `YY.MM-runtime` this tag will always refer to the latest point release for that version. In addition to this there will also be at least one point release version tagged in the form of `vYY.MM.00-runtime` this will be the initial point release for that version (ex. `v24.03.00-runtime`). In the event of a major bug, we may release additional point releases (ex. `v24.03.01-runtime`, `v24.03.02-runtime` etc...), and the `YY.MM-runtime` tag will be updated to reference that point release.
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> Morpheus uses Calendar Versioning ([CalVer](https://calver.org/)). For each Morpheus release there will be an image tagged in the form of `YY.MM-runtime` this tag will always refer to the latest point release for that version. In addition to this there will also be at least one point release version tagged in the form of `vYY.MM.00-runtime` this will be the initial point release for that version (ex. `v24.06.00-runtime`). In the event of a major bug, we may release additional point releases (ex. `v24.06.01-runtime`, `v24.06.02-runtime` etc...), and the `YY.MM-runtime` tag will be updated to reference that point release.
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>
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> Users who want to ensure they are running with the latest bug fixes should use a release image tag (`YY.MM-runtime`). Users who need to deploy a specific version into production should use a point release image tag (`vYY.MM.00-runtime`).
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### Starting the Morpheus Container
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1. Ensure that [The NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) is installed.
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1. Start the container downloaded from the previous section:
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./docker/run_container_release.sh
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```
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The `./docker/run_container_release.sh` script accepts the same `DOCKER_IMAGE_NAME`, and `DOCKER_IMAGE_TAG` environment variables that the `./docker/build_container_release.sh` script does. For example, to run version `v24.03.00` use the following:
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The `./docker/run_container_release.sh` script accepts the same `DOCKER_IMAGE_NAME`, and `DOCKER_IMAGE_TAG` environment variables that the `./docker/build_container_release.sh` script does. For example, to run version `v24.06.00` use the following:
Copy file name to clipboardexpand all lines: examples/digital_fingerprinting/starter/README.md
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## Environment Setup
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Follow the instructions [here](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/docs/source/developer_guide/contributing.md) to set up your development environment in either a Docker container or conda environment.
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Follow the instructions [here](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/docs/source/developer_guide/contributing.md) to set up your development environment in either a Docker container or conda environment.
The training dataset consists of AWS CloudTrail logs. It contains logs from two entities, providing information about their activities within the AWS environment.
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*[hammah-role-g-training-part1.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/training-data/cloudtrail/hammah-role-g-training-part1.json): 700 records <br>
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*[hammah-role-g-training-part2.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/training-data/cloudtrail/hammah-role-g-training-part2.json): 1187 records <br>
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*[hammah-user123-training-part2.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/training-data/cloudtrail/hammah-user123-training-part2.json): 1000 records <br>
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*[hammah-user123-training-part3.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/training-data/cloudtrail/hammah-user123-training-part3.json): 1000 records <br>
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*[hammah-user123-training-part4.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/training-data/cloudtrail/hammah-user123-training-part4.json): 387 records <br>
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*[hammah-role-g-training-part1.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/training-data/cloudtrail/hammah-role-g-training-part1.json): 700 records <br>
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*[hammah-role-g-training-part2.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/training-data/cloudtrail/hammah-role-g-training-part2.json): 1187 records <br>
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*[hammah-user123-training-part2.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/training-data/cloudtrail/hammah-user123-training-part2.json): 1000 records <br>
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*[hammah-user123-training-part3.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/training-data/cloudtrail/hammah-user123-training-part3.json): 1000 records <br>
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*[hammah-user123-training-part4.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/training-data/cloudtrail/hammah-user123-training-part4.json): 387 records <br>
The evaluation dataset consists of AWS CloudTrail logs. It contains logs from two entities, providing information about their activities within the AWS environment.
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*[hammah-role-g-validation.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/validation-data/cloudtrail/hammah-role-g-validation.json): 314 records
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*[hammah-user123-validation-part1.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/validation-data/cloudtrail/hammah-user123-validation-part1.json): 300 records
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*[hammah-user123-validation-part2.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/validation-data/cloudtrail/hammah-user123-validation-part2.json): 300 records
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*[hammah-user123-validation-part3.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.03/models/datasets/validation-data/cloudtrail/hammah-user123-validation-part3.json): 247 records
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*[hammah-role-g-validation.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/validation-data/cloudtrail/hammah-role-g-validation.json): 314 records
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*[hammah-user123-validation-part1.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/validation-data/cloudtrail/hammah-user123-validation-part1.json): 300 records
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*[hammah-user123-validation-part2.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/validation-data/cloudtrail/hammah-user123-validation-part2.json): 300 records
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*[hammah-user123-validation-part3.json](https://github.com/nv-morpheus/Morpheus/blob/branch-24.06/models/datasets/validation-data/cloudtrail/hammah-user123-validation-part3.json): 247 records
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## Inference:
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**Engine:**
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### List the steps explaining how this model works.
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* The model works by training on baseline behaviors and subsequently detecting deviations from the established baseline, triggering alerts accordingly.
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