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A full guide can be found on the Python Package Index.
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Deploying the SDK
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This diagram depicts the general principle of how the SDK can be deployed and operated. GenericallyGenerally, it does not matter which setup is chosen. An example implementation can be seen in Hosted SDK.
Clients can also self-host modules with the SDK on any hyper scaler hyperscaler or even on-premise. The generic process that is modelled modeled above includes five key components:
Component | Function | Examples |
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A code repository | Developers develop the module and then iterate it Most of these tools allow enforcing four-eye principles for developing and deploying separately | GitHub GitLab Bitbucket On premise Git solution |
Automation | Runs tests and checks, builds the image, ships it to a registry and triggers a deployment | GitHub Actions GitLab CI Runners Bitbucket Pipelines Jenkins |
Container Registry | Stores immutable versions of the modules | Azure Container Registry DockerHub |
Container-running infrastructure | Since its only a docker image, it can be ran literally nearly everywhere, the only requirement being able to run container images. | Azure Container Apps Azure Functions AWS Lambda Google Kubernetes Engine …etc. |
Unique | Calls the modules |
Unique examples
Unique makes the source code available for both the repository (as a template) as well as some base tooling for automation with GitHub Actions.
Clients are free to reuse this code or use it as inspiration to rebuild the same on another Git platform or respective CI/CDs.
For co-development partners of Unique (see Hosted SDK) Unique and closed-source provide terraform code to provision Azure resources needed to actually run the modules (Registry and Containner-running Infrastructure above).
Is this whole setup needed?
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