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<!-- WEHUB_ZH_README -->
> [!NOTE]
> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
> [English](./README.en.md) · [原始项目](https://github.com/deeplearning4j/deeplearning4j) · [上游 README](https://github.com/deeplearning4j/deeplearning4j/blob/HEAD/README.md)
> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
<p align="center">
<img src="https://www.zeljkoobrenovic.com/tools/tech/images/eclipse_deeplearning4j.png">
</p>
@@ -10,26 +16,26 @@
![GitHub commit activity](https://img.shields.io/github/commit-activity/m/eclipse/deeplearning4j)
The **[Eclipse Deeplearning4J](https://deeplearning4j.konduit.ai/)** (DL4J) ecosystem is a set of projects intended to support all the needs of a JVM based deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
**[Eclipse Deeplearning4J](https://deeplearning4j.konduit.ai/)** (DL4J) 生态系统是一组项目，旨在满足基于 JVM 的深度学习应用的全部需求。这意味着从原始数据出发，无论数据位于何处、以何种格式存在，都能完成加载与预处理，进而构建并调优各种简单与复杂的深度学习网络。
Because Deeplearning4J runs on the JVM you can use it with a wide variety of JVM based languages other than Java, like Scala, Kotlin, Clojure and many more.
由于 Deeplearning4J 运行在 JVM 上，除 Java 外，你还可以将其与多种基于 JVM 的语言配合使用，例如 Scala、Kotlin、Clojure 等。
The DL4J stack comprises of:
- **DL4J**: High level API to build MultiLayerNetworks and ComputationGraphs with a variety of layers, including custom ones. Supports importing Keras models from h5, including tf.keras models (as of 1.0.0-beta7) and also supports distributed training on Apache Spark
- **ND4J**: General purpose linear algebra library with over 500 mathematical, linear algebra and deep learning operations. ND4J is based on the highly-optimized C++ codebase LibND4J that provides CPU (AVX2/512) and GPU (CUDA) support and acceleration by libraries such as OpenBLAS, OneDNN (MKL-DNN), cuDNN, cuBLAS, etc
- **SameDiff** : Part of the ND4J library, SameDiff is our automatic differentiation / deep learning framework. SameDiff uses a graph-based (define then run) approach, similar to TensorFlow graph mode. Eager graph (TensorFlow 2.x eager/PyTorch) graph execution is planned. SameDiff supports importing TensorFlow frozen model format .pb (protobuf) models. Import for ONNX, TensorFlow SavedModel and Keras models are planned. Deeplearning4j also has full SameDiff support for easily writing custom layers and loss functions.
- **DataVec**: ETL for machine learning data in a wide variety of formats and files (HDFS, Spark, Images, Video, Audio, CSV, Excel etc)
- **LibND4J** : C++ library that underpins everything. For more information on how the JVM acceses native arrays and operations refer to [JavaCPP](https://github.com/bytedeco/javacpp)
- **Python4J**: Bundled cpython execution for the JVM
DL4J 技术栈包括：
- **DL4J**：用于构建 MultiLayerNetworks 和 ComputationGraphs 的高级 API，提供多种层（包括自定义层）。支持从 h5 导入 Keras 模型（自 1.0.0-beta7 起包括 tf.keras 模型），并支持在 Apache Spark 上进行分布式训练
- **ND4J**：通用线性代数库，包含 500 多种数学、线性代数与深度学习运算。ND4J 基于高度优化的 C++ 代码库 LibND4J，通过 OpenBLAS、OneDNN（MKL-DNN）、cuDNN、cuBLAS 等库提供 CPU（AVX2/512）与 GPU（CUDA）支持与加速
- **SameDiff**：ND4J 库的一部分，SameDiff 是我们的自动微分 / 深度学习框架。SameDiff 采用基于图的“先定义后运行”（define then run）方式，类似于 TensorFlow 的图模式（graph mode）。急切图（eager graph，类似 TensorFlow 2.x eager/PyTorch）执行已在规划中。SameDiff 支持导入 TensorFlow 冻结模型格式 .pb（protobuf）模型。对 ONNX、TensorFlow SavedModel 和 Keras 模型的导入也在规划中。Deeplearning4j 还提供完整的 SameDiff 支持，便于编写自定义层与损失函数。
- **DataVec**：面向机器学习数据的 ETL，支持多种格式与文件（HDFS、Spark、图像、视频、音频、CSV、Excel 等）
- **LibND4J**：支撑一切的 C++ 库。有关 JVM 如何访问原生数组与运算的更多信息，请参阅 [JavaCPP](https://github.com/bytedeco/javacpp)
- **Python4J**：为 JVM 捆绑的 cpython 执行环境
All projects in the DL4J ecosystem support Windows, Linux and macOS. Hardware support includes CUDA GPUs (10.0, 10.1, 10.2 except OSX), x86 CPU (x86_64, avx2, avx512), ARM CPU (arm, arm64, armhf) and PowerPC (ppc64le).
DL4J 生态系统中的所有项目均支持 Windows、Linux 和 macOS。硬件支持包括 CUDA GPU（10.0、10.1、10.2，OSX 除外）、x86 CPU（x86_64、avx2、avx512）、ARM CPU（arm、arm64、armhf）以及 PowerPC（ppc64le）。
## Community Support
For support for the project, please go over to https://community.konduit.ai/
## 社区支持
如需获取项目支持，请前往 https://community.konduit.ai/
## Using Eclipse Deeplearning4J in your project
## 在项目中使用 Eclipse Deeplearning4J
Deeplearning4J has quite a few dependencies. For this reason we only support usage with a build tool.
Deeplearning4J 依赖项较多。因此，我们仅支持通过构建工具使用。
```xml
<dependencies>
@@ -46,14 +52,11 @@ Deeplearning4J has quite a few dependencies. For this reason we only support usa
</dependencies>
```
Add these dependencies to your pom.xml file to use Deeplearning4J with the CPU backend. A full standalone project example is [available in the example repository](https://github.com/eclipse/deeplearning4j-examples), if you want to start a new Maven project from scratch.
将上述依赖添加到你的 pom.xml 文件中，即可使用 CPU 后端的 Deeplearning4J。若要从零开始新建 Maven 项目，[示例仓库中提供了完整的独立项目示例](https://github.com/eclipse/deeplearning4j-examples),
## Code samples
## 代码示例
Due to DL4J being a multi faceted project
with several modules in the mono repo, we recommend looking at the examples
for a taste of different usages of the different modules. Below
we'll link to examples for each module.
由于 DL4J 是一个多面性的单体仓库（mono repo）项目，包含多个模块，我们建议查看示例以了解各模块的不同用法。下面我们将链接到各模块的示例。
1. ND4J: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/nd4j-ndarray-examples
2. DL4J: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/dl4j-examples
@@ -62,49 +65,49 @@ we'll link to examples for each module.
5. Python4j: https://deeplearning4j.konduit.ai/python4j/tutorials/quickstart
For users looking for being able to run models from other frameworks, see:
对于希望运行其他框架模型的用户，请参阅：
1. Onnx: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/onnx-import-examples
2. Tensorflow/Keras: https://github.com/deeplearning4j/deeplearning4j-examples/tree/master/tensorflow-keras-import-examples
## Documentation, Guides and Tutorials
You can find the official documentation for Deeplearning4J and the other libraries of its ecosystem at http://deeplearning4j.konduit.ai/.
## 文档、指南与教程
你可以在 http://deeplearning4j.konduit.ai/. 找到 Deeplearning4J 及其生态系统其他库的官方文档
## Want some examples?
We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples
## 想要一些示例？
我们有一个单独的仓库，提供各类示例：https://github.com/eclipse/deeplearning4j-examples
## Building from source
It is preferred to use the official pre-compiled releases (see above). But if you want to build from source, first take a look at the prerequisites for building from source here: https://deeplearning4j.konduit.ai/multi-project/how-to-guides/build-from-source. Various instructions for cpu and gpu builds can be found there. Please go to our [forums](https://community.konduit.ai/) for further help.
## 从源码构建
建议优先使用官方预编译发行版（见上文）。但若你想从源码构建，请先查看此处的从源码构建先决条件：https://deeplearning4j.konduit.ai/multi-project/how-to-guides/build-from-source. 其中包含 CPU 与 GPU 构建的各类说明。如需进一步帮助，请前往我们的[论坛](https://community.konduit.ai/)
## Running tests
## 运行测试
In order to run tests, please see the platform-tests module.
This module only runs on jdk 11 (mostly due to spark and bugs with older scala versions + JDK 17)
要运行测试，请参阅 platform-tests 模块。
该模块仅在 jdk 11 上运行（主要由于 spark 以及旧版 scala 与 JDK 17 的兼容问题）。
platform-tests allows you to run dl4j for different backends.
There are a few properties you can specify on the command line:
1. backend.artifactId: this defaults to nd4j-native and will run tests on cpu,you can specify other backends like nd4j-cuda-11.6
2. dl4j.version: You can change the dl4j version that the tests run against. This defaults to 1.0.0-SNAPSHOT.
platform-tests 允许你针对不同后端运行 dl4j。
你可以在命令行上指定以下几个属性：
1. backend.artifactId：默认为 nd4j-native，将在 CPU 上运行测试；你也可以指定其他后端，例如 nd4j-cuda-11.6
2. dl4j.version：可更改测试所针对的 dl4j 版本。默认为 1.0.0-SNAPSHOT。
More parameters can be found here:
更多参数可在此处找到：
https://github.com/deeplearning4j/deeplearning4j/blob/c1bf8717e4839c8930e9c43183bf7b94d0cf84dc/platform-tests/pom.xml#L47
## Running project in Intellij IDEA:
1. Ensure you follow https://stackoverflow.com/questions/45370178/exporting-a-package-from-system-module-is-not-allowed-with-release on jdk 9 or later
2. Ignore all nd4j-shade submodules. Right click on each folder and click: Maven -> Ignore project
## 在 IntelliJ IDEA 中运行项目：
1. 请确保在 jdk 9 或更高版本上遵循 https://stackoverflow.com/questions/45370178/exporting-a-package-from-system-module-is-not-allowed-with-release
2. 忽略所有 nd4j-shade 子模块。右键点击每个文件夹，选择：Maven -> Ignore project
## License
## 许可证
[Apache License 2.0](LICENSE)
## Commercial Support
Deeplearning4J is actively developed by the team at [Konduit K.K.](https://konduit.ai).
## 商业支持
Deeplearning4J 由 [Konduit K.K.](https://konduit.ai). 团队积极开发。
[If you need any commercial support feel free to reach out to us. at [support@konduit.ai](mailto:support@konduit.ai)
[如需任何商业支持，欢迎通过 [support@konduit.ai](mailto:support@konduit.ai) 与我们联系。