engine_config

引擎配置

包括PaddlePaddle, Pytorch, MxNet, Tensorflow使用CPU计算的配置（自动配置，手动配置）。初次运行时，自动配置会从亚马逊服务器下载native引擎，所以速度可能会比较慢。而手动配置会从maven服务器（可以指向国内服务器）下载，速度会很快。并且本地库会一起打包，部署后运行时，不会再加载本地运行库。

架构：

支持的引擎及操作系统：

MXNet - full support
PyTorch - full support
TensorFlow - supports inference and NDArray operations
ONNX Runtime - supports basic inference
PaddlePaddle - supports basic inference
TFLite - supports basic inference
TensorRT - supports basic inference
DLR - supports basic inference

补充信息：下面这个版本可以直接支持 CentOS Linux release 7.9.2009 (Core)及以上 ubuntu 18.04及以上

详细的配置信息（包括CPU，GPU）参考链接：

PaddlePaddle
Pytorch
MxNet
Tensorflow
ONNX
TensorFlow Lite
DLR
TensorRT TensorRT驱动版本查询下面Dockerfile的 TRT_VERSION https://github.com/deepjavalibrary/djl/blob/master/docker/tensorrt/Dockerfile

下面是配置参考例子：

version 0.20.0（版本可能不会及时维护，请参考上面的文档）

1. PaddlePaddle engine

1). 自动配置


<dependency>
    <groupId>ai.djl.paddlepaddle</groupId>
    <artifactId>paddlepaddle-engine</artifactId>
    <version>0.20.0</version>
    <scope>runtime</scope>
</dependency>

2). macOS - CPU


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<dependency>
    <groupId>ai.djl.paddlepaddle</groupId>
    <artifactId>paddlepaddle-native-cpu</artifactId>
    <classifier>osx-x86_64</classifier>
    <version>2.2.2</version>
    <scope>runtime</scope>
</dependency>

3). Linux - CPU


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<dependency>
    <groupId>ai.djl.paddlepaddle</groupId>
    <artifactId>paddlepaddle-native-cpu</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>2.2.2</version>
    <scope>runtime</scope>
</dependency>

4). Linux - GPU 需要设置环境变量LD_LIBRARY_PATH，如在 /etc/profile中设置。


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LD_LIBRARY_PATH=$HOME/.djl.ai/paddle/x.x.x-<cuda-flavor>-linux-x86_64


x
<dependency>
    <groupId>ai.djl.paddlepaddle</groupId>
    <artifactId>paddlepaddle-native-cu102</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>2.2.2</version>
    <scope>runtime</scope>
</dependency>

<dependency>
    <groupId>ai.djl.paddlepaddle</groupId>
    <artifactId>paddlepaddle-native-cu112</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>2.2.2</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


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- <artifactId>paddlepaddle-native-cuXXX</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.paddlepaddle 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/paddlepaddle/
CUDA 11.2: paddlepaddle-native-cu112
CUDA 11.0: paddlepaddle-native-cu110
CUDA 10.2: pytorch-native-cu102
CUDA 10.1: pytorch-native-cu101

5). Windows - CPU


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<dependency>
    <groupId>ai.djl.paddlepaddle</groupId>
    <artifactId>paddlepaddle-native-cpu</artifactId>
    <classifier>win-x86_64</classifier>
    <version>2.2.2</version>
    <scope>runtime</scope>
</dependency>

6). Windows GPU


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<dependency>
    <groupId>ai.djl.paddlepaddle</groupId>
    <artifactId>paddlepaddle-native-cu112</artifactId>
    <classifier>win-x86_64</classifier>
    <version>2.2.2</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


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- <artifactId>paddlepaddle-native-cuXXX</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.paddlepaddle 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/paddlepaddle/
CUDA 11.2: paddlepaddle-native-cu112
CUDA 11.0: paddlepaddle-native-cu110
CUDA 10.2: pytorch-native-cu102
CUDA 10.1: pytorch-native-cu101

2. Pytorch engine

1). 自动配置


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-engine</artifactId>
    <version>0.20.0</version>
    <scope>runtime</scope>
</dependency>

如果需要明确的指定pytorch版本，可以用下面的配置方法：

http://docs.djl.ai/engines/pytorch/pytorch-engine/index.html

PyTorch engine version	PyTorch native library version
pytorch-engine:0.20.0	1.11.0, 1.12.1, 1.13.0
pytorch-engine:0.19.0	1.10.0, 1.11.0, 1.12.1
pytorch-engine:0.18.0	1.9.1, 1.10.0, 1.11.0
pytorch-engine:0.17.0	1.9.1, 1.10.0, 1.11.0

......

2). macOS - CPU


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cpu</artifactId>
    <classifier>osx-x86_64</classifier>
    <version>1.13.0</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

3). macOS - M1


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cpu</artifactId>
    <classifier>osx-aarch64</classifier>
    <version>1.13.0</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

4). Linux - CPU


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cpu</artifactId>
    <classifier>linux-x86_64</classifier>
    <scope>runtime</scope>
    <version>1.13.0</version>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

5). For aarch64 build


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cpu-precxx11</artifactId>
    <classifier>linux-aarch64</classifier>
    <scope>runtime</scope>
    <version>1.13.0</version>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

6). For Pre-CXX11 build


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# We also provide packages for the system like CentOS 7/Ubuntu 14.04 with GLIBC >= 2.17. 
# All the package were built with GCC 7, we provided a newer libstdc++.so.6.24 in the package that 
# contains CXXABI_1.3.9 to use the package successfully.
ai.djl.pytorch:pytorch-jni:1.11.0-0.17.0
ai.djl.pytorch:pytorch-native-cu113-precxx11:1.11.0:linux-x86_64 - CUDA 11.3
ai.djl.pytorch:pytorch-native-cpu-precxx11:1.11.0:linux-x86_64 - CPU

<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cu117-precxx11</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>1.13.0</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

#---------------------------------------------------------------#

<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cpu-precxx11</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>1.13.0</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

7). Linux - GPU


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cu117</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>1.13.0</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


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- <artifactId>pytorch-native-cuXXX</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.pytorch 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/pytorch/
CUDA 11.7: pytorch-native-cu117
CUDA 11.6: pytorch-native-cu116
CUDA 11.3: pytorch-native-cu113
CUDA 11.1: pytorch-native-cu111
CUDA 10.2: pytorch-native-cu102
CUDA 10.1: pytorch-native-cu101
CUDA 10.0: pytorch-native-cu110
CUDA 9.2: pytorch-native-cu92

8). Windows - CPU


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cpu</artifactId>
    <classifier>win-x86_64</classifier>
    <scope>runtime</scope>
    <version>1.13.0</version>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

9). Windows - GPU


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<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-cu117</artifactId>
    <classifier>win-x86_64</classifier>
    <version>1.13.0</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.13.0-0.20.0</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


xxxxxxxxxx
- <artifactId>pytorch-native-cuXXX</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.pytorch 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/pytorch/
CUDA 11.7: pytorch-native-cu117
CUDA 11.6: pytorch-native-cu116
CUDA 11.3: pytorch-native-cu113
CUDA 11.1: pytorch-native-cu111
CUDA 10.2: pytorch-native-cu102
CUDA 10.1: pytorch-native-cu101
CUDA 10.0: pytorch-native-cu110
CUDA 9.2: pytorch-native-cu92

3. MxNet engine

1). 自动配置


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<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-engine</artifactId>
    <version>0.20.0</version>
    <scope>runtime</scope>
</dependency>

2). macOS - CPU


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<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-mkl</artifactId>
    <classifier>osx-x86_64</classifier>
    <version>1.9.1</version>
    <scope>runtime</scope>
</dependency>

3). Linux - CPU


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<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-mkl</artifactId>
    <classifier>linux-x86_64</classifier>
    <scope>runtime</scope>
    <version>1.9.1</version>
</dependency>

4). Linux - GPU


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<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-cu112mkl</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>1.9.1</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


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  <artifactId>mxnet-native-cuXXXmkl</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.mxnet 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/mxnet/
CUDA 11.2: mxnet-native-cu112mkl
CUDA 11.0: mxnet-native-cu101mkl
CUDA 10.2: mxnet-native-cu101mkl
CUDA 10.1: mxnet-native-cu102mkl
CUDA 9.2: mxnet-native-cu102mkl

5). Windows - CPU


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<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-mkl</artifactId>
    <classifier>win-x86_64</classifier>
    <scope>runtime</scope>
    <version>1.9.1</version>
</dependency>

6). Windows - GPU


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<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-native-cu112mkl</artifactId>
    <classifier>win-x86_64</classifier>
    <version>1.9.1</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


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  <artifactId>mxnet-native-cuXXXmkl</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.mxnet 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/mxnet/
CUDA 11.2: mxnet-native-cu112mkl
CUDA 11.0: mxnet-native-cu101mkl
CUDA 10.2: mxnet-native-cu101mkl
CUDA 10.1: mxnet-native-cu102mkl
CUDA 9.2: mxnet-native-cu102mkl

4. Tensorflow engine

1). 自动配置


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<dependency>
    <groupId>ai.djl.tensorflow</groupId>
    <artifactId>tensorflow-engine</artifactId>
    <version>0.20.0</version>
    <scope>runtime</scope>
</dependency>

2). macOS - CPU


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<dependency>
    <groupId>ai.djl.tensorflow</groupId>
    <artifactId>tensorflow-native-cpu</artifactId>
    <classifier>osx-x86_64</classifier>
    <version>2.7.0</version>
    <scope>runtime</scope>
</dependency>

3). Linux - CPU


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<dependency>
    <groupId>ai.djl.tensorflow</groupId>
    <artifactId>tensorflow-native-cpu</artifactId>
    <classifier>linux-x86_64</classifier>
    <scope>runtime</scope>
    <version>2.7.0</version>
</dependency>

4). Linux - GPU


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<dependency>
    <groupId>ai.djl.tensorflow</groupId>
    <artifactId>tensorflow-native-cu113</artifactId>
    <classifier>linux-x86_64</classifier>
    <version>2.7.0</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


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  <artifactId>tensorflow-native-cuXXX</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.tensorflow 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/tensorflow/
CUDA 11.3:  tensorflow-native-cu113
CUDA 11.0:  tensorflow-native-cu110
CUDA 10.1:  tensorflow-native-cu101

5). Windows - CPU


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<dependency>
    <groupId>ai.djl.tensorflow</groupId>
    <artifactId>tensorflow-native-cpu</artifactId>
    <classifier>win-x86_64</classifier>
    <scope>runtime</scope>
    <version>2.7.0</version>
</dependency>

6). Windows - GPU


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<dependency>
    <groupId>ai.djl.tensorflow</groupId>
    <artifactId>tensorflow-native-cu113</artifactId>
    <classifier>win-x86_64</classifier>
    <version>2.7.0</version>
    <scope>runtime</scope>
</dependency>

CUDA 支持的版本


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  <artifactId>tensorflow-native-cuXXX</artifactId>
  <version>X.X.X</version>


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# 搜索 ai.djl.tensorflow 查询 ： https://mvnrepository.com/ 
# 或者 https://repo1.maven.org/maven2/ai/djl/tensorflow/
CUDA 11.3:  tensorflow-native-cu113
CUDA 11.0:  tensorflow-native-cu110
CUDA 10.1:  tensorflow-native-cu101