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DJL - Apache MXNet engine implementation

Overview

This module contains the Deep Java Library (DJL) EngineProvider for Apache MXNet.

We don't recommend that developers use classes in this module directly. Use of these classes will couple your code with Apache MXNet and make switching between engines difficult. Even so, developers are not restricted from using engine-specific features. For more information, see NDManager#invoke().

Documentation

The latest javadocs can be found on the djl.ai website.

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 MXNet engine from the central Maven repository by including the following dependency:

<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-engine</artifactId>
    <version>0.8.0</version>
    <scope>runtime</scope>
</dependency>

Besides the mxnet-engine library, you may also need to include the MXNet native library in your project. All current provided MXNet native libraries are built with MKLDNN.

Choose a native library based on your platform and needs:

We offer an automatic option that will download the native libraries into cache folder the first time you run DJL. It will automatically determine the appropriate jars for your system based on the platform and GPU support.

<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-auto</artifactId>
    <version>1.7.0-backport</version>
    <scope>runtime</scope>
</dependency>

macOS

For macOS, you can use the following library:

  • ai.djl.mxnet:mxnet-native-mkl:1.7.0-backport:osx-x86_64

    This package takes advantage of the Intel MKL library to boost performance.

<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-mkl</artifactId>
    <classifier>osx-x86_64</classifier>
    <version>1.7.0-backport</version>
    <scope>runtime</scope>
</dependency>

Linux

For the Linux platform, you can choose between CPU, GPU. If you have Nvidia CUDA installed on your GPU machine, you can use one of the following library:

Linux GPU

  • ai.djl.mxnet:mxnet-native-cu102mkl:1.7.0-backport:linux-x86_64 - CUDA 10.2
  • ai.djl.mxnet:mxnet-native-cu101mkl:1.7.0-backport:linux-x86_64 - CUDA 10.1
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-cu102mkl</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>1.7.0-backport</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-cu101mkl</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>1.7.0-backport</version>
    <scope>runtime</scope>
</dependency>

Linux CPU

  • ai.djl.mxnet:mxnet-native-mkl:1.7.0-backport:linux-x86_64
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-mkl</artifactId>
    <classifier>linux-x86_64</classifier>
    <scope>runtime</scope>
    <version>1.7.0-backport</version>
</dependency>

Windows

For the Windows platform, you can use CPU package. MXNet windows GPU native library size are large, we no longer provide GPU package, instead you have to use Automatic package.

Windows GPU

  • ai.djl.mxnet:mxnet-native-auto:1.7.0-backport

    This package supports CUDA 10.1 and CUDA 10.2 for Windows.

Windows CPU

  • ai.djl.mxnet:mxnet-native-mkl:1.7.0-backport:win-x86_64
<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-mkl</artifactId>
    <classifier>win-x86_64</classifier>
    <scope>runtime</scope>
    <version>1.7.0-backport</version>
</dependency>