ray/doc/source/serve/managing-java-deployments.md

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# Managing Java Deployments
Java is a mainstream programming language for production services. Ray Serve offers a native Java API for creating, updating, and managing deployments. You can create Ray Serve deployments using Java and call them via Python, or vice versa.
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This section helps you to:
- create, query, and update Java deployments
- configure Java deployment resources
- manage Python deployments using the Java API
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```{contents}
```
## Creating a Deployment
By specifying the full name of the class as an argument to the `Serve.deployment()` method, as shown in the code below, you can create and deploy a deployment of the class.
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```{literalinclude} ../../../java/serve/src/test/java/io/ray/serve/docdemo/ManageDeployment.java
:start-after: docs-create-start
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:end-before: docs-create-end
:language: java
```
## Accessing a Deployment
Once a deployment is deployed, you can fetch its instance by name.
```{literalinclude} ../../../java/serve/src/test/java/io/ray/serve/docdemo/ManageDeployment.java
:start-after: docs-query-start
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:end-before: docs-query-end
:language: java
```
## Updating a Deployment
You can update a deployment's code and configuration and then redeploy it. The following example updates the `"counter"` deployment's initial value to 2.
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```{literalinclude} ../../../java/serve/src/test/java/io/ray/serve/docdemo/ManageDeployment.java
:start-after: docs-update-start
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:end-before: docs-update-end
:language: java
```
## Configuring a Deployment
Ray Serve lets you configure your deployments to:
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- scale out by increasing the number of [deployment replicas](serve-architecture-high-level-view)
- assign [replica resources](serve-cpus-gpus) such as CPUs and GPUs.
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The next two sections describe how to configure your deployments.
### Scaling Out
By specifying the `numReplicas` parameter, you can change the number of deployment replicas:
```{literalinclude} ../../../java/serve/src/test/java/io/ray/serve/docdemo/ManageDeployment.java
:start-after: docs-scale-start
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:end-before: docs-scale-end
:language: java
```
### Resource Management (CPUs, GPUs)
Through the `rayActorOptions` parameter, you can reserve resources for each deployment replica, such as one GPU:
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```{literalinclude} ../../../java/serve/src/test/java/io/ray/serve/docdemo/ManageDeployment.java
:start-after: docs-resource-start
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:end-before: docs-resource-end
:language: java
```
## Managing a Python Deployment
A Python deployment can also be managed and called by the Java API. Suppose you have a Python file `counter.py` in the `/path/to/code/` directory:
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```python
from ray import serve
@serve.deployment
class Counter(object):
def __init__(self, value):
self.value = int(value)
def increase(self, delta):
self.value += int(delta)
return str(self.value)
```
You can deploy it through the Java API and call it through a `RayServeHandle`:
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```java
import io.ray.api.Ray;
import io.ray.serve.api.Serve;
import io.ray.serve.deployment.Deployment;
import io.ray.serve.generated.DeploymentLanguage;
import java.io.File;
public class ManagePythonDeployment {
public static void main(String[] args) {
System.setProperty(
"ray.job.code-search-path",
System.getProperty("java.class.path") + File.pathSeparator + "/path/to/code/");
Serve.start(true, false, null);
Deployment deployment =
Serve.deployment()
.setDeploymentLanguage(DeploymentLanguage.PYTHON)
.setName("counter")
.setDeploymentDef("counter.Counter")
.setNumReplicas(1)
.setInitArgs(new Object[] {"1"})
.create();
deployment.deploy(true);
System.out.println(Ray.get(deployment.getHandle().method("increase").remote("2")));
}
}
```
:::{note}
Before `Ray.init` or `Serve.start`, you need to specify a directory to find the Python code. For details, please refer to [Cross-Language Programming](cross_language).
:::
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## Future Roadmap
In the future, Ray Serve plans to provide more Java features, such as:
- an improved Java API that matches the Python version
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- HTTP ingress support
- bring-your-own Java Spring project as a deployment