vis

docs/Intra-node/extensible_backend.mdx

@@ -95,6 +95,25 @@ model(input)
 assert(input["result"] == b"123")
 ```
 
+Or you can:
+```python
+tp.utils.cpp_extension.load_filter(
+    name = 'Skip', 
+    sources='status forward(dict data){return status::Skip;}',
+    sources_header="")
+
+
+
+tp.utils.cpp_extension.load_backend(
+    name = 'identity', 
+    sources='void forward(dict data){(*data)["result"] = (*data)["data"];}',
+    sources_header="")
+model = tp.pipe({"backend":'identity'})
+input = {"data":2}
+model(input)
+assert input["result"] == 2
+```
+
 ## Binding with Python
 When using Python as the front-end language, the back-end is called from Python and the results are returned to Python, requiring type conversion.
 ### From Python Types to Any {#py2any}

docs/installation.mdx

@@ -145,12 +145,12 @@ For more examples, see [Showcase](./showcase/showcase.mdx).
 ## Customizing Dockerfile {#selfdocker}
 
 Refer to the [example Dockerfile](https://github.com/torchpipe/torchpipe/blob/main/docker/trt9.1.base). After downloading [TensorRT](https://github.com/NVIDIA/TensorRT/tree/release/9.1#optional---if-not-using-tensorrt-container-specify-the-tensorrt-ga-release-build-path) in advance, you can compile the corresponding base image.
-```
-# put TensorRT-9.1.0.4.Linux.x86_64-gnu.cuda-11.8.tar.gz into thirdparty/
+```bash
+# put TensorRT-9.*.Linux.x86_64-gnu.cuda-11.8.tar.gz into thirdparty/
 
-# docker build --network=host -f docker/trt9.1.base -t torchpipe:base_trt-9.1 .
+# docker build --network=host -f docker/trt9.base -t torchpipe:base_trt-9 .
 
-# docker run --rm   --network=host --gpus=all --ulimit memlock=-1 --ulimit stack=67108864 --privileged=true  -v `pwd`:/workspace -it torchpipe:base_trt-9.1  /bin/bash 
+# docker run --rm   --network=host --gpus=all --ulimit memlock=-1 --ulimit stack=67108864 --privileged=true  -v `pwd`:/workspace -it torchpipe:base_trt-9  /bin/bash 
 
 ```
 Base images compiled in this way have smaller sizes than NGC PyTorch images. Please note that `_GLIBCXX_USE_CXX11_ABI==0`.

docs/tools/vis.mdx

@@ -4,25 +4,25 @@ title: Configuration Visualizing
 type: explainer
 ---
 
+(From v0.4.0)
 We provide a simple web-based visualization feature for configuration files.
 ## Environment Setup
 
 ```bash
-apt-get update
-apt install graphviz
-pip install pydot gradio
+pip install  gradio
 ```
 
 ## Usage {#parameter}
 
- `torchpipe.utils.vis [-h] [--port PORT] [--save] toml`
 
-:::tip Parameters
-- **--save** - Whether to save the graph as an SVG image. The image will be saved in the current directory with a different file extension than the TOML file.
-::: 
+```python
+import torchpipe as tp
 
+a=tp.parse_toml("examples/ppocr/ocr.toml")
 
-## Example
-```bash
-python -m torchpipe.utils.vis your.toml # --port 2211
-```
+tp.utils.Visual(a).launch()
+```
+
+
+
+

i18n/zh/docusaurus-plugin-content-docs/current/Intra-node/extensible_backend.mdx

@@ -99,6 +99,26 @@ model(input)
 assert(input["result"] == b"123")
 ```
 
+
+Or you can:
+```python
+tp.utils.cpp_extension.load_filter(
+    name = 'Skip', 
+    sources='status forward(dict data){return status::Skip;}',
+    sources_header="")
+
+
+
+tp.utils.cpp_extension.load_backend(
+    name = 'identity', 
+    sources='void forward(dict data){(*data)["result"] = (*data)["data"];}',
+    sources_header="")
+model = tp.pipe({"backend":'identity'})
+input = {"data":2}
+model(input)
+assert input["result"] == 2
+```
+
 ## 与python的绑定
 以python为前端语言时，会从python中调用后端，并且将结果返回到python中，需要进行类型转换。
 ### 从python类型到any {#py2any}

i18n/zh/docusaurus-plugin-content-docs/current/installation.mdx

@@ -133,13 +133,13 @@ print(input["result"].shape)  # 失败则此键值一定不存在，即使输入
 
 ## 自定义dockerfile {#selfdocker}
 
-参考[示例dockerfile](https://github.com/torchpipe/torchpipe/blob/main/docker/trt9.1.base)，预先下载[TensorRT](https://github.com/NVIDIA/TensorRT/tree/release/9.1#optional---if-not-using-tensorrt-container-specify-the-tensorrt-ga-release-build-path)后可编译相关基础环境镜像。
+参考[示例dockerfile](https://github.com/torchpipe/torchpipe/blob/main/docker/trt9.base)，预先下载[TensorRT](https://github.com/NVIDIA/TensorRT/tree/release/9.1#optional---if-not-using-tensorrt-container-specify-the-tensorrt-ga-release-build-path)后可编译相关基础环境镜像。
 ```bash
-# put TensorRT-9.1.0.4.Linux.x86_64-gnu.cuda-11.8.tar.gz into thirdparty/
+# put TensorRT-9.*.Linux.x86_64-gnu.cuda-11.8.tar.gz into thirdparty/
 
-# docker build --network=host -f docker/trt9.1.base -t torchpipe:base_trt-9.1 .
+# docker build --network=host -f docker/trt9.base -t torchpipe:base_trt-9 .
 
-# docker run --rm   --network=host --gpus=all --ulimit memlock=-1 --ulimit stack=67108864 --privileged=true  -v `pwd`:/workspace -it torchpipe:base_trt-9.1  /bin/bash 
+# docker run --rm   --network=host --gpus=all --ulimit memlock=-1 --ulimit stack=67108864 --privileged=true  -v `pwd`:/workspace -it torchpipe:base_trt-9  /bin/bash 
 
 ```
 这种方式编译出的基础镜像比NGC pytorch镜像体积更小. 需要注意，其`_GLIBCXX_USE_CXX11_ABI==0`

i18n/zh/docusaurus-plugin-content-docs/current/tools/vis.mdx

@@ -3,26 +3,23 @@ id: vis
 title: 配置文件可视化
 type: explainer
 ---
+从0.4.4版本开始生效
 
 针对配置文件，我们提供了简单的网页可视化功能。
 
 ## 环境准备
 ```bash
-apt-get update
-apt install graphviz
-pip install pydot gradio
+
+pip install gradio
 ```
 
 ## 使用方法 {#parameter}
 
- `torchpipe.utils.vis [-h] [--port PORT] [--save] toml`
 
-:::tip 参数
-- **--save** - 是否将图保存为svg图片。图片将保存在当前目录下，与toml文件（后缀不同）
-:::
+```python
+import torchpipe as tp
 
+a=tp.parse_toml("examples/ppocr/ocr.toml")
 
-## 示例
-```bash
-python -m torchpipe.utils.vis your.toml # --port 2211
+tp.utils.Visual(a).launch()
 ```