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[pytorch] Update PyTorch engine README for version 2.2.2 #3165

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May 8, 2024
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[pytorch] Update PyTorch engine README for version 2.2.2 #3165

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74 changes: 37 additions & 37 deletions engines/pytorch/pytorch-engine/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -24,13 +24,13 @@ The javadocs output is built in the `build/doc/javadoc` folder.
## Installation
You can pull the PyTorch engine from the central Maven repository by including the following dependency:

- ai.djl.pytorch:pytorch-engine:0.27.0
- ai.djl.pytorch:pytorch-engine:0.28.0

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-engine</artifactId>
<version>0.27.0</version>
<version>0.28.0</version>
<scope>runtime</scope>
</dependency>
```
Expand All @@ -46,7 +46,7 @@ The following table illustrates which pytorch version that DJL supports:

| PyTorch engine version | PyTorch native library version |
|------------------------|-------------------------------------------|
| pytorch-engine:0.28.0 | 1.13.1, **2.1.2** |
| pytorch-engine:0.28.0 | 1.13.1, 2.1.2, **2.2.2** |
| pytorch-engine:0.27.0 | 1.13.1, **2.1.1** |
| pytorch-engine:0.26.0 | 1.13.1, 2.0.1, **2.1.1** |
| pytorch-engine:0.25.0 | 1.11.0, 1.12.1, **1.13.1**, 2.0.1 |
Expand Down Expand Up @@ -115,21 +115,21 @@ export PYTORCH_FLAVOR=cpu
### macOS
For macOS, you can use the following library:

- ai.djl.pytorch:pytorch-jni:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cpu:2.1.1:osx-x86_64

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>osx-x86_64</classifier>
<version>2.1.1</version>
<version>2.2.2</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```
Expand All @@ -139,21 +139,21 @@ For macOS, you can use the following library:
### macOS M1
For macOS M1, you can use the following library:

- ai.djl.pytorch:pytorch-jni:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-native-cpu:2.1.1:osx-aarch64
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cpu:2.2.2:osx-aarch64

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>osx-aarch64</classifier>
<version>2.1.1</version>
<version>2.2.2</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```
Expand All @@ -164,63 +164,63 @@ installed on your GPU machine, you can use one of the following library:

#### Linux GPU

- ai.djl.pytorch:pytorch-jni:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-native-cu121:2.1.1:linux-x86_64 - CUDA 12.1
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cu121:2.2.2:linux-x86_64 - CUDA 12.1

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cu121</artifactId>
<classifier>linux-x86_64</classifier>
<version>2.1.1</version>
<version>2.2.2</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```

### Linux CPU

- ai.djl.pytorch:pytorch-jni:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-native-cpu:2.1.1:linux-x86_64
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cpu:2.2.2:linux-x86_64

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>linux-x86_64</classifier>
<scope>runtime</scope>
<version>2.1.1</version>
<version>2.2.2</version>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```

### For aarch64 build

- ai.djl.pytorch:pytorch-jni:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-native-cpu-precxx11:2.1.1:linux-aarch64
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cpu-precxx11:2.2.2:linux-aarch64

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu-precxx11</artifactId>
<classifier>linux-aarch64</classifier>
<scope>runtime</scope>
<version>2.1.1</version>
<version>2.2.2</version>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```
Expand All @@ -230,22 +230,22 @@ installed on your GPU machine, you can use one of the following library:
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:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-native-cu121-precxx11:2.1.1:linux-x86_64 - CUDA 12.1
- ai.djl.pytorch:pytorch-native-cpu-precxx11:2.1.1:linux-x86_64 - CPU
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cu121-precxx11:2.2.2:linux-x86_64 - CUDA 12.1
- ai.djl.pytorch:pytorch-native-cpu-precxx11:2.2.2:linux-x86_64 - CPU

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cu121-precxx11</artifactId>
<classifier>linux-x86_64</classifier>
<version>2.1.1</version>
<version>2.2.2</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```
Expand All @@ -255,13 +255,13 @@ All the package were built with GCC 7, we provided a newer `libstdc++.so.6.24` i
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu-precxx11</artifactId>
<classifier>linux-x86_64</classifier>
<version>2.1.1</version>
<version>2.2.2</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```
Expand All @@ -276,42 +276,42 @@ For the Windows platform, you can choose between CPU and GPU.

#### Windows GPU

- ai.djl.pytorch:pytorch-jni:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-native-cu121:2.1.1:win-x86_64 - CUDA 12.1
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cu121:2.2.2:win-x86_64 - CUDA 12.1

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cu121</artifactId>
<classifier>win-x86_64</classifier>
<version>2.1.1</version>
<version>2.2.2</version>
<scope>runtime</scope>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```

### Windows CPU

- ai.djl.pytorch:pytorch-jni:2.1.1-0.27.0
- ai.djl.pytorch:pytorch-native-cpu:2.1.1:win-x86_64
- ai.djl.pytorch:pytorch-jni:2.2.2-0.28.0
- ai.djl.pytorch:pytorch-native-cpu:2.2.2:win-x86_64

```xml
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-native-cpu</artifactId>
<classifier>win-x86_64</classifier>
<scope>runtime</scope>
<version>2.1.1</version>
<version>2.2.2</version>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-jni</artifactId>
<version>2.1.1-0.27.0</version>
<version>2.2.2-0.28.0</version>
<scope>runtime</scope>
</dependency>
```
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