README.md

DJL Serving - WorkLoadManager

DJL Serving can be divided into a frontend and backend. The frontend is a netty webserver that manages incoming requests and operates the control plane. The backend WorkLoadManager handles the model batching, workers, and threading for high-performance inference.

For those who already have a web server infrastructure but want to operate high-performance inference, it is possible to use only the WorkLoadManager. For this reason, we have it split apart into a separate module.

Using the WorkLoadManager is quite simple. First, create a new one through the constructor:

WorkLoadManager wlm = new WorkLoadManager();

You can also configure the WorkLoadManager by using the static WlmConfigManager.

Then, you can construct a ModelInfo for each model you will want to run through wlm. With the ModelInfo, you are able to build a Job once you receive input:

ModelInfo modelInfo = new ModelInfo(...);
Job job = new Job(modelInfo, input);

Once you have your job, it can be submitted to the WorkLoadManager. It will automatically spin up workers if none are created and manage worker numbers. Then, it returns a CompletableFuture<Output> for the result.

CompletableFuture<Output> futureResult = wlm.runJob(job);

View the javadocs for the WorkLoadManager for more options.

Documentation

The latest javadocs can be found on the javadoc.io.

You can also build the latest javadocs locally using the following command:

# for Linux/macOS:
./gradlew javadoc

# for Windows:
..\..\gradlew javadoc

The javadocs output is built in the build/doc/javadoc folder.

Installation

You can pull the server from the central Maven repository by including the following dependency:

<dependency>
    <groupId>ai.djl.serving</groupId>
    <artifactId>wlm</artifactId>
    <version>0.30.0</version>
</dependency>