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(serve-managing-deployments-guide)=
Managing Deployments
This section should help you:
- create, query, update and configure deployments
- configure resources of your deployments
- specify different Python dependencies across different deployment using Runtime Environments
:::{tip} Get in touch with us if you're using or considering using Ray Serve. :::
Updating a Deployment
Often you want to be able to update your code or configuration options for a deployment over time.
Deployments can be updated simply by updating the code or configuration options and calling serve.run() again.
:start-after: __updating_a_deployment_start__
:end-before: __updating_a_deployment_end__
:language: python
By default, each call to serve.run() will cause a redeployment, even if the underlying code and options didn't change.
This could be detrimental if you have many deployments in a script and and only want to update one: if you re-run the script, all of the deployments will be redeployed, not just the one you updated.
To prevent this, you may provide a version string for the deployment as a keyword argument in the decorator or Deployment.options().
When a redeployment happens, Serve will perform a rolling update, bringing down at most 20% of the replicas at any given time.
(managing-deployments-user-configuration)=
User Configuration
Suppose you want to update a parameter in your model without needing to restart
the replicas in your deployment. You can do this by writing a reconfigure method
for the class underlying your deployment. At runtime, you can then pass in your
new parameters by setting the user_config option.
The following simple example will make the usage clear:
The reconfigure method is called when the class is created if user_config
is set. In particular, it's also called when new replicas are created in the
future if scale up your deployment later. The reconfigure method is also called
each time user_config is updated.
:::{note}
The user_config and its contents must be JSON-serializable.
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