Deploy to Android Studio

Deploy to Android Studio#

MLange supports both for Kotlin and Java implementations.

Deploy your own On-device AI Android application easily. There are only three steps are needed.

  1. Download zeticMLange.aar and place it in your application library directory

  2. Add module dependency in your library

  3. Initiailize ZeticMLangeModel with your Model Key and run

Prerequisite#

  1. Model Key

  2. Aar Library

1. Download and place zeticMLange.aar to your project#

  • Leave zeticMLange.aar under app/libs/zeticlibs

    • e.g. Android project structure

      app
└── libs
    └── zeticlibs
        └── zeticMLange.aar

2. Add zeticMLange.aar dependency to your app#

  • Update gradle to use zeticMLange.aar

  • You can choose Groovy build or Kotlin DSL build

    • build.gradle (Groovy)

    android {
    ...
    packagingOptions {
        jniLibs {
            useLegacyPackaging true
        }
    }
}

dependencies {
    implementation files('libs/zeticlibs/zeticMLange.aar')
}

    • build.gradle.kts (Kotlin DSL)

    android {
    ...
    packaging {
        jniLibs {
            useLegacyPackaging = true
        }
    }
}

dependencies {
    implementation(files("libs/zeticlibs/zeticMLange.aar"))
}

3. Initialize and run ZeticMLangeModel model with your Model Key#

  • MLange supports both Kotlin and Java implementation

    1. Zetic MLange model running (Java)

    // 1. Zetic MLange model running

// (1) Load Zetic MLange model
ZeticMLangeModel model = new ZeticMLangeModel(this, "MLANGE_MODEL_KEY");

// (2) Run model after preparing model inputs
ByteBuffer[] inputs = // Prepare your inputs;
model.run(inputs);

// (3) Get output buffers of the model
ByteBuffer[] outputs = model.getOutputBuffers();

    1. Zetic MLange model running (Kotlin)

    // 1. Zetic MLange Model Running

// (1) Load Zetic MLange model
val model = ZeticMLangeModel(this, "MLANGE_MODEL_KEY")

// (2) Run model after preparing model inputs
val inputs: Array<ByteBuffer> = // Prepare your inputs
model.run(inputs)

// (3) Get output buffers of the model
val outputs = model.outputBuffers

MLange Android Sample App#

(+) Additional API for MLange-Android usage#

  • As a default we set the model to use FP16 data type over NPU.

  • We set 2 more options for user to choose runtime mode for (1) Better output accuracy and (2) High-speed inference.

  • You cas set model option while you laod the model like sample code below.

        // (1) Output Accuracy mode - `FP32 - (CPU, GPU)` mode
    val model = ZeticMLangeModel(this, "MLANGE_MODEL_KEY", ZeticMLangeModel.ZETIC_MLANGE_RUN_MODE_FP32)

    // (2) High-speed inference mode - `Quantized - (NPU)` mode
    val model = ZeticMLangeModel(this, "MLANGE_MODEL_KEY", ZeticMLangeModel.ZETIC_MLANGE_RUN_MODE_QUANTIZED)

        // (1) Output Accuracy mode - `FP32 - (CPU, GPU)` mode
    ZeticMLangeModel model = new ZeticMLangeModel(this, "MLANGE_MODEL_KEY", ZeticMLangeModel.ZETIC_MLANGE_RUN_MODE_FP32);

    // (2) High-speed inference mode - `Quantized - (NPU)` mode
    ZeticMLangeModel model = new ZeticMLangeModel(this, "MLANGE_MODEL_KEY", ZeticMLangeModel.ZETIC_MLANGE_RUN_MODE_QUANTIZED);
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