Merge pull request #204 from bitqiang/feature/update-deployment

update: deployment.md

Author: yangapku

deployment.md

@@ -9,11 +9,17 @@
 + **GPU硬件要求**：请准备**Volta架构及以上**的Nvidia GPU显卡（配备FP16 Tensor Core），Nvidia各架构对应显卡型号请参见[此文档表格](https://en.wikipedia.org/wiki/CUDA#GPUs_supported)。本文我们以T4显卡为例
 + **CUDA**：推荐[CUDA](https://developer.nvidia.com/cuda-11-6-0-download-archive)版本11.6及以上，本文以11.6为例
 + **CUDNN**：推荐[CUDNN](https://developer.nvidia.com/rdp/cudnn-archive) 8.6.0及以上，本文以8.6.0为例。请注意TensorRT和CUDNN有版本match关系，如TensorRT 8.5.x必须使用CUDNN 8.6.0，详见TensorRT的版本要求
-+ **ONNX**：请安装`pip install onnx onnxruntime-gpu onnxmltools`。注意我们转换TensorRT模型时，将沿着Pytorch → ONNX → TensorRT的步骤，所以准备TensorRT模型也需要先安装ONNX库。本文以onnx版本1.13.0，onnxruntime-gpu版本1.13.1，onnxmltools版本1.11.1为例
-+ **TensorRT**：推荐[TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8)版本8.5.x，本文以8.5.2.2为例，使用pip即可安装`pip install tensorrt==8.5.2.2`。TensorRT各版本对应的CUDNN匹配版本，请从[文档页面](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8)，查阅此TensorRT版本的"NVIDIA TensorRT Support Matrix"
++ **ONNX**：注意我们转换TensorRT模型时，将沿着Pytorch → ONNX → TensorRT的步骤，所以准备TensorRT模型也需要先安装ONNX库。本文以onnx版本1.13.0，onnxruntime-gpu版本1.13.1，onnxmltools版本1.11.1为例
++ **TensorRT**：推荐[TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8)版本8.5.x，本文以8.5.2.2为例。TensorRT各版本对应的CUDNN匹配版本，请从[文档页面](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8)，查阅此TensorRT版本的"NVIDIA TensorRT Support Matrix"
 + **Pytorch**：推荐1.12.1及以上，本文以1.12.1为例（建议直接pip安装1.12.1+cu116，环境尽量不要再使用conda安装cudatoolkit，避免环境CUDNN版本变化，导致TensorRT报错）
 + [requirements.txt](requirements.txt)要求的其他依赖项
 
+执行代码
+``` 
+pip install tensorrt==8.5.2.2 onnx==1.13.0 onnxruntime-gpu==1.13.1 onnxmltools==1.11.1
+pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 -f https://download.pytorch.org/whl/torch_stable.html
+pip install -r requirements.txt 
+```
 
 ## 转换和运行ONNX模型
 

deployment_En.md

@@ -9,11 +9,17 @@ Our latest Chinese-CLIP code supports the conversion of Pytorch models of all sc
 + **GPU hardware requirements**: Please prepare Nvidia GPUs **with Volta architecture and above** (equipped with FP16 Tensor Core). Please refer to [this document](https://en.wikipedia.org/wiki/CUDA#GPUs_supported) for the corresponding GPUs of each Nvidia architecture. Here we take T4 GPU as an example.
 + **CUDA**: [CUDA](https://developer.nvidia.com/cuda-11-6-0-download-archive) version 11.6 and above is recommended. We take version 11.6 as an example.
 + **CUDNN**: [CUDNN](https://developer.nvidia.com/rdp/cudnn-archive) version 8.6.0 and above is recommended. We take version 8.6.0 as an example. Please note that TensorRT and CUDNN have version correspondence, e.g. TensorRT 8.5.x must correspond to CUDNN 8.6.0, see the TensorRT version requirements for details.
-+ **ONNX**: Please run `pip install onnx onnxruntime-gpu onnxmltools` to install. Note that when we convert the TensorRT model, we will follow the steps Pytorch → ONNX → TensorRT, so preparing the TensorRT model also requires installing the ONNX library first. Here we take onnx version 1.13.0, onnxruntime-gpu version 1.13.1, and onnxmltools version 1.11.1 as examples.
-+ **TensorRT**: The recommended [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8) version is 8.5.x. We use 8.5.2.2 as an example, using pip to install `pip install tensorrt==8.5.2.2`. For the CUDNN version corresponding to each TensorRT version, please refer to the "NVIDIA TensorRT Support Matrix" from this [documentation page]((https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8)).
++ **ONNX**: Note that when we convert the TensorRT model, we will follow the steps Pytorch → ONNX → TensorRT, so preparing the TensorRT model also requires installing the ONNX library first. Here we take onnx version 1.13.0, onnxruntime-gpu version 1.13.1, and onnxmltools version 1.11.1 as examples.
++ **TensorRT**: The recommended [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8) version is 8.5.x. We use 8.5.2.2 as an example. For the CUDNN version corresponding to each TensorRT version, please refer to the "NVIDIA TensorRT Support Matrix" from this [documentation page]((https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_8)).
 + **Pytorch**: Pytorch version 1.12.1 and above is recommended. We take version 1.12.1 as an example. (It is recommended to directly pip install 1.12.1 + cu116, and try not to use conda to install cudatoolkit, avoiding TensorRT errors due to CUDNN version changes. )
 + Other dependencies as required in [requirements.txt](requirements.txt).
 
+executable code
+``` 
+pip install tensorrt==8.5.2.2 onnx==1.13.0 onnxruntime-gpu==1.13.1 onnxmltools==1.11.1
+pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 -f https://download.pytorch.org/whl/torch_stable.html
+pip install -r requirements.txt 
+```
 
 ## Converting and Running ONNX Models