Convert ToolDeployment to a django model #1423
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Allows us to track the specific tool release that a user has deployed. This commit includes minimal changes to get the model working, so that every time a tool is deployed from the front end, a database object is created. This does introduce a bug about status of the tool deployment, but this will be fixed later.
Use an is_active field to allow us to track past deployments. This also makes it easier to uninstall an old tool before we install a new one. NB a bug will occur if a user has no active ToolDeployment but tries to switch from a jupyter datascience to all-spark release (or vice versa). To resolve, we have to manually install the users Jupyter release, or deploy the same type of Jupyter to create an "active" ToolDeployment object, and then the user can deploy the other type of Jupyter.
Deletes a lot of code that was used to display the deployed tool, to read information from the tool models where possible. Further refactoring will be needed to add a form to handle tool choices.
Simplify the logic to deploy tools based on the ID. Update ToolDeployment serializer
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Allows us to track the specific tool release that
a user has deployed.
This commit includes minimal changes to get the
model working, so that every time a tool is
deployed from the front end, a database object is
created. This does introduce a bug about status of the tool deployment, but this will be fixed later.
📝 Summary
This PR resolves ...
This PR ...
The changes in this PR are needed because ...
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