(serve-migration)=

1.x to 2.x API Migration Guide

This section covers what to consider or change in your application when migrating from Ray versions 1.x to 2.x.

What has been changed?

In Ray Serve 2.0, we released a new deployment API. The 1.x deployment API can still be used, but it will be deprecated in the future version.

In the 1.x deployment, we usually pass handle of deployment to chain the deployments.

:start-after: __raw_handle_graph_start__
:end-before: __raw_handle_graph_end__
:language: python

With the 2.0 deployment API, you can use the following code to update the above one.

:start-after: __graph_with_new_api_start__
:end-before: __graph_with_new_api_end__
:language: python

:::{note}

get_handle can be replaced by bind() function to fulfill same functionality.
serve.run will return the entry point deployment handle for your whole chained deployments. :::

In the 1.x deployment API, we usually have the following code for deployment.

:start-after: __single_deployment_old_api_start__
:end-before: __single_deployment_old_api_end__
:language: python

With the 2.0 deployment API, you can use the following code to update the above one.

:start-after: __single_deployment_new_api_start__
:end-before: __single_deployment_new_api_end__
:language: python

When you have multiple deployments, here is the normal code for 1.x API

:start-after: __multi_deployments_old_api_start__
:end-before: __multi_deployments_old_api_end__
:language: python

With the 2.0 deployment API, you can use the following code to update the above one.

:start-after: __multi_deployments_new_api_start__
:end-before: __multi_deployments_new_api_end__
:language: python

:::{note}

predict method is defined inside DAGDriver class as an entry point to fulfil requests
Similar to predict method, predict_with_route method is defined inside DAGDriver class as an entry point to fulfil requests.
DAGDriver is a special class to handle multi entry points for different deployments
DAGDriver.bind can accept dictionary and each key is represented as entry point route path.
predict_with_route accepts a route path as the first argument to select which model to use.
In the example, you can also use an HTTP request to fulfill your request. Different models will bind with different route paths based on the user inputs; e.g. http://localhost:8000/model1 and http://localhost:8000/model2 :::

Sometimes, you have a customized route prefix for each deployment:

:start-after: __customized_route_old_api_start__
:end-before: __customized_route_old_api_end__
:language: python

With the 2.0 deployment API, you can use the following code to update the above one.

:start-after: __customized_route_old_api_1_start__
:end-before: __customized_route_old_api_1_end__
:language: python

Or if you have multiple deployments and want to customize the HTTP route prefix for each model, you can use the following code:

:start-after: __customized_route_old_api_2_start__
:end-before: __customized_route_old_api_2_end__
:language: python