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# Deploy the Project on CamelX Platform
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# Deep Java Library
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Deploy on CamelX Platform in three steps
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Infer Deep Learning models from message exchanges data using Deep Java Library (DJL).
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## Step 1: Create a release
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## Metadata
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From the project space, click on **"Create a release"**
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The new version is automatically available in the list
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| Property | Value |
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|----------|-------|
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| Scheme | `djl` |
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| Support Level | Stable |
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| Labels | ai |
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| Version | 4.10.2 |
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## Step 2: Deploy
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## Maven Dependency
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Click on **"Deploy"**
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- **Version:** Select the desired release
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```xml
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- **Environment:** Choose `Development`, `Staging`, or `Production`
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<dependency>
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- **Configuration:** Select the configuration source
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<groupId>org.apache.camel</groupId>
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- **Resources:** Set CPU and Memory
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<artifactId>camel-djl</artifactId>
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<version>4.10.2</version>
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</dependency>
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```
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## Step 3: Expose
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## Endpoint Properties
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Enable **"Expose"**
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Choose an **API Gateway** (Internal, Public, etc.)
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| Name | Type | Required | Default | Description |
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|------|------|----------|---------|-------------|
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| `application` | string | ✓ | | Application name |
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| `artifactId` | string | | | Model Artifact |
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| `model` | string | | | Model |
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| `showProgress` | boolean | | `false` | Show progress while loading zoo models. This parameter takes effect only with zoo models |
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| `translator` | string | | | Translator |
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| `lazyStartProducer` | boolean | | `false` | Whether the producer should be started lazy (on the first message). By starting lazy you can use this to allow CamelContext and routes to startup in situations where a producer may otherwise fail during starting and cause the route to fail being started. By deferring this startup to be lazy then the startup failure can be handled during routing messages via Camel's routing error handlers. Beware that when the first message is processed then creating and starting the producer may take a little time and prolong the total processing time of the processing. |
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