Revert "[serve] Replace Ray Client with Ray Job Submission for `serve…

doc/source/serve/dev-workflow.md

@@ -61,10 +61,6 @@ serve run local_dev:graph
 
 The `serve run` command blocks the terminal and can be canceled with Ctrl-C. Typically, `serve run` should not be run simultaneously from multiple terminals, unless each `serve run` is targeting a separate running Ray cluster.
 
-:::{note} 
-If you already have a local Ray Cluster running before executing `serve run`, make sure that the path to your Serve app is accessible from the working directory in which you started the Ray Cluster using `ray start --head`. Otherwise, you can pass in `app-dir` or `working-dir` when executing `serve run`. See the documentation for [serve run](serve_cli.html#serve-run) for more details.
-:::
-
 Now that Serve is running, we can send HTTP requests to the application.
 For simplicity, we'll just use the `curl` command to send requests from another terminal.
 
@@ -85,17 +81,17 @@ Note that rerunning `serve run` will redeploy all deployments. To prevent redepl
 
 ## Testing on a remote cluster
 
-To test on a remote cluster, you'll use `serve run` again, but this time you'll pass in an `--address` argument to specify the address of the Ray cluster to connect to.  For remote clusters, this address has the form `http://<head-node-ip-address>:8265` and will be passed to Ray Job Submission; see [Ray Jobs](jobs-overview) for more information.
+To test on a remote cluster, you'll use `serve run` again, but this time you'll pass in an `--address` argument to specify the address of the Ray cluster to connect to.  For remote clusters, this address has the form `ray://<head-node-ip-address>:10001`; see [Ray Client](ray-client-ref) for more information.
 
 When making the transition from your local machine to a remote cluster, you'll need to make sure your cluster has a similar environment to your local machine--files, environment variables, and Python packages, for example.  
 
 Let's see a simple example that just packages the code. Run the following command on your local machine, with your remote cluster head node IP address substituted for `<head-node-ip-address>` in the command:
 
 ```bash
-serve run  --address=http://<head-node-ip-address>:8265 --working_dir="./project/src" local_dev:graph
+serve run  --address=ray://<head-node-ip-address>:10001 --working_dir="./project/src" local_dev:graph
 ```
 
-This will upload the `working_dir` directory to the remote cluster and run your Serve application as a Ray Job on the remote cluster.  Here, the local directory specified by `working_dir` must contain `local_dev.py` so that it can be uploaded to the cluster and imported by Ray Serve.
+This will connect to the remote cluster via Ray Client, upload the `working_dir` directory, and run your serve application.  Here, the local directory specified by `working_dir` must contain `local_dev.py` so that it can be uploaded to the cluster and imported by Ray Serve.
 
 Once this is up and running, we can send requests to the application:
 
@@ -107,7 +103,7 @@ curl -X PUT http://<head-node-ip-address>:8000/?name=Ray
 For more complex dependencies, including files outside the working directory, environment variables, and Python packages, you can use {ref}`Runtime Environments<runtime-environments>`. Here is an example using the --runtime-env-json argument:
 
 ```bash
-serve run  --address=http://<head-node-ip-address>:8265 --runtime-env-json='{"env_vars": {"MY_ENV_VAR": "my-value"}, "working_dir": "./project/src", "pip": ["requests", "chess"]}' local_dev:graph
+serve run  --address=ray://<head-node-ip-address>:10001 --runtime-env-json='{"env_vars": {"MY_ENV_VAR": "my-value"}, "working_dir": "./project/src", "pip": ["requests", "chess"]}' local_dev:graph
 ```
 
 You can also specify the `runtime_env` via a YAML file; see [serve run](serve_cli.html#serve-run) for details.