examples.md

djl.ai - examples

This module contains example projects to demonstrate how developers can use the djl.ai API.

There are three examples in this project:

1. Image classification example
2. Single-shot Object detection example
3. Bert question and answer example

Prerequisite

You need to have JDK 8 (or later) and IntelliJ installed on your system. Read here for more detail.

djl.ai API reference

You should also be familiar with the djl.ai API documentation: Javadoc

Getting started: 30 seconds to run an example

Building with command line

This example project supports building with both gradle and maven. To build, use the following:

gradle

cd examples
./gradlew build

maven build

cd examples
mvn package

Run example code

With the gradle application plugin you can execute example code directly. You can find more detail in each example's detail document. Here is an example that executes classification:

cd examples
./gradlew run

Import djl.ai example project with IntelliJ

1. Open IntelliJ and click Import Project.
2. Select the examples directory in the djl.ai project source folder, and click "Open".
3. Choose Import project form existing model, you can select either Gradle or Maven
4. Use the default configuration and click OK.
5. Select an example to continue.

Engine selection

djl.ai is engine agnostic, so you can choose different engine providers. We currently provide MXNet engine implementation.

With MXNet, you can choose different flavors of the native MXNet library. In this example, we use mxnet-native-mkl for OSX platform. You might need to change it for your platform in pom.xml or build.gradle.

Available MXNet versions are as follows:

Version
mxnet-mkl
mxnet-cu101mkl