[install doc] update 2.4.0rc0 install doc (#5312)

* update Tables

* change 2.3 to 2.4

* add cuda117

* add ubuntu22.04

Author: pangyoki

docs/install/Tables.md

@@ -4,10 +4,10 @@
 
 * **Linux 版本 (64 bit)**
     * **CentOS 6 (不推荐，不提供编译出现问题时的官方支持)**
-    * **CentOS 7 (GPU 版本支持 CUDA 10.1/10.2/11.1/11.2/11.6)**
+    * **CentOS 7 (GPU 版本支持 CUDA 10.1/10.2/11.1/11.2/11.6/11.7)**
     * **Ubuntu 14.04 (不推荐，不提供编译出现问题时的官方支持)**
-    * **Ubuntu 16.04 (GPU 版本支持 CUDA 10.1/10.2/11.1/11.2/11.6)**
-    * **Ubuntu 18.04 (GPU 版本支持 CUDA 10.1/10.2/11.1/11.2/11.6)**
+    * **Ubuntu 16.04 (GPU 版本支持 CUDA 10.1/10.2/11.1/11.2/11.6/11.7)**
+    * **Ubuntu 18.04 (GPU 版本支持 CUDA 10.1/10.2/11.1/11.2/11.6/11.7)**
 * **Python 版本 3.6/3.7/3.8/3.9/3.10 (64 bit)**
 
 ## 选择 CPU/GPU
@@ -20,6 +20,7 @@
     * **CUDA 工具包 11.1 配合 cuDNN v8.1.1(如需多卡支持，需配合 NCCL2.7 及更高)**
     * **CUDA 工具包 11.2 配合 cuDNN v8.1.1(如需多卡支持，需配合 NCCL2.7 及更高)**
     * **CUDA 工具包 11.6 配合 cuDNN v8.4.0(如需多卡支持，需配合 NCCL2.7 及更高)**
+    * **CUDA 工具包 11.7 配合 cuDNN v8.4.1(如需多卡支持，需配合 NCCL2.7 及更高)**
     * **GPU 运算能力超过 3.5 的硬件设备**
 
         您可参考 NVIDIA 官方文档了解 CUDA 和 CUDNN 的安装流程和配置方法，请见[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)
@@ -138,7 +139,7 @@ git checkout [分支名]
 例如：
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 注意：python3.6、python3.7 版本从 release/1.2 分支开始支持, python3.8 版本从 release/1.8 分支开始支持, python3.9 版本从 release/2.1 分支开始支持, python3.10 版本从 release/2.3 分支开始支持
@@ -487,7 +488,7 @@ git checkout [分支名]
 例如：
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 #### 10. 并且请创建并进入一个叫 build 的目录下：
@@ -512,7 +513,7 @@ mkdir build && cd build
     > 请注意 PY_VERSION 参数更换为您需要的 python 版本
 
 
-* 对于需要编译**GPU 版本 PaddlePaddle**的用户：(**仅支持 CentOS7（CUDA11.6/CUDA11.2/CUDA11.1/CUDA10.2/CUDA10.1)**)
+* 对于需要编译**GPU 版本 PaddlePaddle**的用户：(**仅支持 CentOS7（CUDA11.7/CUDA11.6/CUDA11.2/CUDA11.1/CUDA10.2/CUDA10.1)**)
 
     1. 请确保您已经正确安装 nccl2，或者按照以下指令安装 nccl2（这里提供的是 CUDA10.2，cuDNN7 下 nccl2 的安装指令，更多版本的安装信息请参考 NVIDIA[官方网站](https://developer.nvidia.com/nccl)）:
 

docs/install/Tables_en.md

@@ -4,10 +4,10 @@
 
 * **Linux version (64 bit)**
     * **CentOS 6 (not recommended, no official support for compilation problems)**
-    * **CentOS 7 (GPU version supports CUDA 10.1/10.2/11.1/11.2/11.6**
+    * **CentOS 7 (GPU version supports CUDA 10.1/10.2/11.1/11.2/11.6/11.7**
     * **Ubuntu 14.04 (not recommended, no official support for compilation problems)**
-    * **Ubuntu 16.04 (GPU version supports CUDA 10.1/10.2/11.1/11.2/11.6)**
-    * **Ubuntu 18.04 (GPU version supports CUDA 10.1/10.2/11.1/11.2/11.6)**
+    * **Ubuntu 16.04 (GPU version supports CUDA 10.1/10.2/11.1/11.2/11.6/11.7)**
+    * **Ubuntu 18.04 (GPU version supports CUDA 10.1/10.2/11.1/11.2/11.6/11.7)**
 * **Python version 3.6/3.7/3.8/3.9/3.10 (64 bit)**
 
 ## Choose CPU/GPU
@@ -20,6 +20,7 @@
     * **CUDA toolkit 11.1 with cuDNN v8.1.1(for multi card support, NCCL2.7 or higher)**
     * **CUDA toolkit 11.2 with cuDNN v8.1.1(for multi card support, NCCL2.7 or higher)**
     * **CUDA toolkit 11.6 with cuDNN v8.4.0(for multi card support, NCCL2.7 or higher)**
+    * **CUDA toolkit 11.7 with cuDNN v8.4.1(for multi card support, NCCL2.7 or higher)**
     * **Hardware devices with GPU computing power over 3.5**
 
         You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)
@@ -144,7 +145,7 @@ git checkout [name of the branch]
 For example：
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 Note: python3.6、python3.7 version started supporting from release/1.2 branch, python3.8 version started supporting from release/1.8 branch, python3.9 version started supporting from release/2.1 branch, python3.10 version started supporting from release/2.3 branch
@@ -474,7 +475,7 @@ git checkout [name of target branch]
 For example:
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 #### 10. And please create and enter a directory called build:

docs/install/compile/linux-compile.md

@@ -94,7 +94,7 @@ git checkout [分支名]
 例如：
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 注意：python3.6、python3.7 版本从 release/1.2 分支开始支持, python3.8 版本从 release/1.8 分支开始支持, python3.9 版本从 release/2.1 分支开始支持, python3.10 版本从 release/2.3 分支开始支持
@@ -253,7 +253,7 @@ git checkout [分支名]
 例如：
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 注意：python3.6、python3.7 版本从 release/1.2 分支开始支持, python3.8 版本从 release/1.8 分支开始支持, python3.9 版本从 release/2.1 分支开始支持, python3.10 版本从 release/2.3 分支开始支持

docs/install/compile/linux-compile_en.md

@@ -95,7 +95,7 @@ git checkout [name of the branch]
 For example：
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 Note: python3.6、python3.7 version started supporting from release/1.2 branch, python3.8 version started supporting from release/1.8 branch, python3.9 version started supporting from release/2.1 branch, python3.10 version started supporting from release/2.3 branch
@@ -257,7 +257,7 @@ git checkout [name of the branch]
 For example：
 
 ```
-git checkout release/2.3
+git checkout release/2.4
 ```
 
 #### 7. And please create and enter a directory called build:

docs/install/compile/macos-compile.md

@@ -15,7 +15,7 @@
 
 * 如果你的计算机硬件没有 NVIDIA® GPU，请编译 CPU 版本的 PaddlePaddle
 
-* 如果你的计算机硬件有 NVIDIA® GPU，推荐编译 GPU 版本的 PaddlePaddle，建议安装 **CUDA 10.1/10.2//11.1/11.2/11.6**
+* 如果你的计算机硬件有 NVIDIA® GPU，推荐编译 GPU 版本的 PaddlePaddle，建议安装 **CUDA 10.1/10.2/11.1/11.2/11.6/11.7**
 
 ## 本机编译过程
 
@@ -51,7 +51,7 @@
 5. 切换到 2.2 分支下进行编译：
 
     ```
-    git checkout release/2.3
+    git checkout release/2.4
     ```
 
 6. 创建名为 build 的目录并进入：

docs/install/compile/macos-compile_en.md

@@ -3,7 +3,7 @@
 ## Environment preparation
 
 * **Windows 7/8/10 Pro/Enterprise(64bit)**
-* **GPU Version support CUDA 10.1/10.2/11.1/11.2/11.6, and only support single GPU**
+* **GPU Version support CUDA 10.1/10.2/11.1/11.2/11.6/11.7, and only support single GPU**
 * **Python version 3.6+/3.7+/3.8+/3.9+/3.10+(64bit)**
 * **pip version 20.2.2 or above (64bit)**
 * **Visual Studio 2017**
@@ -17,6 +17,7 @@
     * **CUDA toolkit 11.1 with cuDNN v8.1.1**
     * **CUDA toolkit 11.2 with cuDNN v8.2.1**
     * **CUDA toolkit 11.6 with cuDNN v8.4.0**
+    * **CUDA toolkit 11.7 with cuDNN v8.4.1**
     * **GPU's computing capability exceeds 3.5**
 
 ## Installation steps
@@ -73,7 +74,7 @@ There is one compilation methods in Windows system:
 
     For example：
     ```
-    git checkout release/2.3
+    git checkout release/2.4
     ```
 
     Note: python3.6、python3.7 version started supporting from release/1.2, python3.8 version started supporting from release/1.8, python3.9 version started supporting from release/2.1, python3.10 version started supporting from release/2.3

docs/install/compile/windows-compile.md

@@ -7,7 +7,7 @@
 
 在进行 PaddlePaddle 安装之前请确保您的 Anaconda 软件环境已经正确安装。软件下载和安装参见 Anaconda 官网(https://www.anaconda.com/)。在您已经正确安装 Anaconda 的情况下请按照下列步骤安装 PaddlePaddle。
 
-* CentOS 7 / Ubuntu16.04 / Ubuntu18.04 / Ubuntu20.04 (64bit)
+* CentOS 7 / Ubuntu16.04 / Ubuntu18.04 / Ubuntu20.04 / Ubuntu22.04 (64bit)
 * GPU 版本支持 CUDA 10.1/10.2/11.2/11.6
 * conda 版本 4.8.3+ (64 bit)
 
@@ -116,7 +116,7 @@ python -c "import platform;print(platform.architecture()[0]);print(platform.mach
 如果您的计算机没有 NVIDIA® GPU，请安装 CPU 版的 PaddlePaddle
 
 ```
-conda install paddlepaddle==2.3.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
+conda install paddlepaddle==2.4.0rc0 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
 ```
 
 
@@ -127,26 +127,26 @@ conda install paddlepaddle==2.3.2 --channel https://mirrors.tuna.tsinghua.edu.cn
 *  对于 `CUDA 10.1`，需要搭配 cuDNN 7 (cuDNN>=7.6.5, 多卡环境下 NCCL>=2.7)，安装命令为:
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=10.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=10.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
   ```
 
 *  对于 `CUDA 10.2`，需要搭配 cuDNN 7 (cuDNN>=7.6.5, 多卡环境下 NCCL>=2.7)，安装命令为:
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
   ```
 
 
 *  对于 `CUDA 11.2`，需要搭配 cuDNN 8.1.1(多卡环境下 NCCL>=2.7)，安装命令为:
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
   ```
 
 *  对于 `CUDA 11.6`，需要搭配 cuDNN 8.4.0(多卡环境下 NCCL>=2.7)，安装命令为:
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
   ```
 
 您可参考 NVIDIA 官方文档了解 CUDA 和 CUDNN 的安装流程和配置方法，请见[CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)

docs/install/compile/windows-compile_en.md

@@ -7,7 +7,7 @@
 
 Before performing PaddlePaddle installation, please make sure that your Anaconda software environment is properly installed. For software download and installation, see Anaconda's official website (https://www.anaconda.com/). If you have installed Anaconda correctly, follow these steps to install PaddlePaddle.
 
-* CentOS 7 / Ubuntu16.04 / Ubuntu18.04 / Ubuntu20.04 (64bit)
+* CentOS 7 / Ubuntu16.04 / Ubuntu18.04 / Ubuntu20.04 / Ubuntu22.04 (64bit)
 * GPU Version support CUDA 10.1/10.2/11.2/11.6
 * conda version 4.8.3+ (64 bit)
 
@@ -120,7 +120,7 @@ You can choose the following version of PaddlePaddle to start installation:
 If your computer doesn't have NVIDIA® GPU, please install `the CPU Version of PaddlePaddle`
 
 ```
-conda install paddlepaddle==2.3.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
+conda install paddlepaddle==2.4.0rc0 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
 ```
 
 
@@ -131,25 +131,25 @@ conda install paddlepaddle==2.3.2 --channel https://mirrors.tuna.tsinghua.edu.cn
 *  If you are using CUDA 10.1，cuDNN 7 (cuDNN>=7.6.5, for multi card support, NCCL>=2.7):
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=10.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=10.1 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
   ```
 
 *  If you are usingCUDA 10.2，cuDNN 7 (cuDNN>=7.6.5, for multi card support, NCCL>=2.7):
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
   ```
 
 *  If you are using CUDA 11.2，cuDNN 8.1.1(for multi card support, NCCL>=2.7):
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=11.2 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
   ```
 
 *  If you are using CUDA 11.6，cuDNN 8.4.0(for multi card support, NCCL>=2.7):
 
   ```
-  conda install paddlepaddle-gpu==2.3.2 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
+  conda install paddlepaddle-gpu==2.4.0rc0 cudatoolkit=11.6 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
   ```
 
 You can refer to NVIDIA official documents for installation process and configuration method of CUDA and cudnn. Please refer to [CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/)，[cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/)