typo

Author: tp-nan

docs/backend-reference/cvcuda.mdx

@@ -6,16 +6,16 @@ displayed_sidebar: api
 
 ---
 
-<!-- [The following compilation option](../installation.mdx#compilation-options) need to be enabled to use the following backends.
+[The following compilation option](../installation.mdx#compilation-options) need to be enabled to use the following backends.
 
-`version>=0.3.3b2`
+`version>=0.4.0`
 
 ```bash
-BUILD_PPLCV=1 pip install -e .
+WITH_CVCUDA=1 pip install -e .
 ```
 ## Overview
 
-| Name| Main Initialization Parameters |Runtime Parameters[Type] | Input[Type]  | Output[Type] | Note|
+<!-- | Name| Main Initialization Parameters |Runtime Parameters[Type] | Input[Type]  | Output[Type] | Note|
 | --- | --- | --- | --- | --- | --- |
 |[PPLResizeTensor]|resize_h,resize_w | resize_h[int],resize_w[int]|data[Tensor]|result[Tensor]||
 |[PPLWarpAffineTensor]|target_h,target_w|target_h[int],target_w[int],affine_matrix[vector\<float\>]|data[Tensor]|result[Tensor]|affine_matrix.size() == 6|

i18n/zh/docusaurus-plugin-content-docs/current/backend-reference/cvcuda.mdx

@@ -5,22 +5,22 @@ type: reference
 displayed_sidebar: api
 
 ---
-<!-- 
+
 # Ppl.cv相关后端
 
-`version>=0.3.3b2`
+`version>=0.4.0`
 
 需要编译时启动[相关选项](../installation.mdx#compilation-options)
 
 ```bash
-BUILD_PPLCV=1 pip install -e .
+WITH_CVCUDA=1 pip install -e .
 ```
 ## 速览
-
+<!-- 
 | 名称| 主要初始化参数 |运行时参数[类型] | 输入[类型]  | 输出[类型] | 备注|
 | --- | --- | --- | --- | --- | --- |
 |[PPLResizeTensor]|resize_h,resize_w | resize_h[int],resize_w[int]|data[Tensor]|result[Tensor]||
 |[PPLWarpAffineTensor]|target_h,target_w|target_h[int],target_w[int],affine_matrix[vector\<float\>]|data[Tensor]|result[Tensor]|affine_matrix.size() == 6|
 |[PPLCopyMakeBorderTensor]||top[int],bottom[int],left[int],right[int]|data[Tensor]|result[Tensor]||
-
  -->
+

i18n/zh/docusaurus-plugin-content-docs/current/welcome.md

@@ -5,7 +5,7 @@ type: explainer
 ---
 
 # 欢迎查看 torchpipe 文档！
-torchpipe是一个独立作用于底层加速库（如tensorrt，opencv，torchscript）以及 RPC（如thrift, gRPC）之间的多实例流水线并行库。在满足时延前提下最大限度挖掘服务吞吐能力。
+torchpipe是一个独立作用于底层加速库（如tensorrt，opencv, CVCUDA，torchscript）以及 RPC（如thrift, gRPC）之间的多实例流水线并行库。在满足时延前提下最大限度挖掘服务吞吐能力。
 
 整个方案集并发安全和全链路流水线调度等特点于一身，支持NIDIA硬件平台, 兼顾了开发效率与性能提速的特点。