diff --git a/README.md b/README.md
index 83ea7e560d..6a7ba76d5d 100644
--- a/README.md
+++ b/README.md
@@ -284,6 +284,7 @@ Please read [getting_started](docs/en/get_started.md) for the basic usage of MMD
   - [Build for Win10](docs/en/01-how-to-build/windows.md)
   - [Build for Android](docs/en/01-how-to-build/android.md)
   - [Build for Jetson](docs/en/01-how-to-build/jetsons.md)
+  - [BUild for Jetson Docker](docs/en/01-how-to-build/Jetson_docker.md)
   - [Build for SNPE](docs/en/01-how-to-build/snpe.md)
   - [Cross Build for aarch64](docs/en/01-how-to-build/cross_build_ncnn_aarch64.md)
 - User Guide
diff --git a/README_zh-CN.md b/README_zh-CN.md
index e40d75200a..f6e9aadb68 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -268,6 +268,7 @@ MMDeploy 是 [OpenMMLab](https://openmmlab.com/) 模型部署工具箱，**为
   - [Build for Win10](docs/zh_cn/01-how-to-build/windows.md)
   - [Build for Android](docs/zh_cn/01-how-to-build/android.md)
   - [Build for Jetson](docs/zh_cn/01-how-to-build/jetsons.md)
+  - [BUild for Jetson Docker](docs/zh_cn/01-how-to-build/Jetson_docker.md)
   - [Build for SNPE](docs/zh_cn/01-how-to-build/snpe.md)
   - [Cross Build for aarch64](docs/zh_cn/01-how-to-build/cross_build_ncnn_aarch64.md)
 - 使用
diff --git a/docker/Jetson/Jetpack4.6/Dockerfile b/docker/Jetson/Jetpack4.6/Dockerfile
new file mode 100644
index 0000000000..115c095436
--- /dev/null
+++ b/docker/Jetson/Jetpack4.6/Dockerfile
@@ -0,0 +1,119 @@
+FROM nvcr.io/nvidia/l4t-pytorch:r32.7.1-pth1.10-py3
+
+ARG MMDEPLOY_VERSION=main
+ENV NVIDIA_VISIBLE_DEVICE all
+ENV NVIDIA_DRIVER_CAPABILITIES all
+ENV CUDA_HOME="/usr/local/cuda"
+ENV PATH="/usr/local/cuda/bin:${PATH}"
+ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/lib/python3.8/dist-packages/opencv-python.libs/${LD_LIBRARY_PATH}"
+ENV TENSORRT_DIR="/usr/include/aarch64-linux-gnu"
+
+ENV DEBIAN_FRONTEND=nointeractive
+ENV FORCE_CUDA="1"
+
+USER root
+WORKDIR /root/workspace
+
+# install dependencies && reinstall python3.8
+RUN  apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 42D5A192B819C5DA &&\
+     apt-get remove python3 &&\ 
+     apt-get update &&\ 
+     apt-get install -y vim wget libspdlog-dev libssl-dev libpng-dev pkg-config libhdf5-100 libhdf5-dev patch --no-install-recommends\
+                        python3.8 python3.8-dev python3.8-pip --no-install-recommends &&\
+     python3.8 -m pip install --upgrade --no-cache-dir setuptools packaging 'Cython<3' wheel &&\
+     python3.8 -m pip install --no-cache-dir --verbose wget psutil numpy &&\
+     python3.8 -m pip install --upgrade --force-reinstall --no-cache-dir --verbose cmake protobuf
+     python3.8 -m pip install onnx==1.10 versioned-hdf5 numpy
+
+# build pytorch 1.10.0 for python3.8
+# Hope it can works lol
+# patch for https://github.com/pytorch/pytorch/issues/45323. Save here, maybe I will meet this issue.
+# RUN PYTHON_ROOT=`pip3 show torch | grep Location: | cut -d' ' -f2` && \
+#    TORCH_CMAKE_CONFIG=$PYTHON_ROOT/torch/share/cmake/Torch/TorchConfig.cmake && \
+#    echo "patching _GLIBCXX_USE_CXX11_ABI in ${TORCH_CMAKE_CONFIG}" && \
+#    sed -i 's/  set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=")/  set(TORCH_CXX_FLAGS "-D_GLIBCXX_USE_CXX11_ABI=0")/g' ${TORCH_CMAKE_CONFIG}
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+		  libopenblas-dev \
+		  libopenmpi-dev \
+            openmpi-bin \
+            openmpi-common \
+		  gfortran \
+		  libomp-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+    
+RUN git clone --branch v1.10.0 --depth=1 --recursive https://github.com/pytorch/pytorch /tmp/pytorch && \
+    cd /tmp/pytorch && \
+    wget https://gist.githubusercontent.com/dusty-nv/ce51796085178e1f38e3c6a1663a93a1/raw/4f1a0f948150c91f877aa38075835df748c81fe5/pytorch-1.10-jetpack-4.5.1.patch &&\
+    patch -p1 < pytorch-1.10-jetpack-4.5.1.patch &&\
+    export USE_NCCL=0 && \
+    export USE_QNNPACK=0 && \
+    export USE_PYTORCH_QNNPACK=0 && \
+    export USE_NATIVE_ARCH=1 && \
+    export USE_DISTRIBUTED=1 && \
+    export USE_TENSORRT=0 && \
+    python3.8 -m pip install -r requirements.txt && \
+    python3.8 -m pip install --no-cache-dir scikit-build ninja && \
+    python3.8 setup.py bdist_wheel && \
+    cp dist/*.whl /root/workspace && \
+    rm -rf /tmp/pytorch    
+RUN python3 -m pip install --verbose /opt/torch*.whl
+
+# build torchvision for python3.8
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+            libjpeg-dev \
+		  zlib1g-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+RUN git clone --branch v0.11.1 --recursive --depth=1 https://github.com/pytorch/vision torchvision && \
+    cd torchvision && \
+    git checkout v0.11.1 && \
+    python3.8 setup.py bdist_wheel && \
+    cp dist/torchvision*.whl /opt && \
+    rm -rf ../torchvision
+RUN python3.8 -m pip install --no-cache-dir --verbose /opt/torchvision*.whl
+    
+# build onnxruntime for python3.8
+RUN wget https://nvidia.box.com/shared/static/m9bz827ljmn771kvkjksdchmkczt3xke.whl -O onnxruntime_gpu-1.10.0-cp38-cp38-linux_aarch64.whl &&\
+    python3.8 -m pip install --no-cache-dir onnxruntime_gpu-1.10.0-cp38-cp38-linux_aarch64.whl
+
+# install mmcv
+RUN git clone --branch 2.x https://github.com/open-mmlab/mmcv.git
+RUN cd mmcv &&\ 
+    python3.8 -m pip install --no-cache-dir opencv-python==4.5.4.60 &&\
+    MMCV_WITH_OPS=1 python3 -m pip install -e .
+
+# build ppl.cv
+RUN git clone https://github.com/openppl-public/ppl.cv.git &&\
+    echo "export PPLCV_DIR=/root/workspace/ppl.cv" >> ~/.bashrc &&\
+    cd ppl.cv &&\
+    ./build.sh cuda
+  
+# build mmdeploy
+RUN git clone --recursive -b $MMDEPLOY_VERSION --depth 1 https://github.com/open-mmlab/mmdeploy &&\
+    cd mmdeploy &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR    &&\
+    make -j$(nproc) && make install && cd .. &&\
+    python3 -m pip install --upgrade setuptools &&\ 
+    python3 -m pip install -e . &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_BUILD_SDK=ON \
+    	-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
+    	-DMMDEPLOY_BUILD_EXAMPLES=ON \
+    	-DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR \ 
+    	-Dpplcv_DIR=/root/workspace/ppl.cv/cuda-build/install/lib/cmake/ppl \
+    	-DMMDEPLOY_CODEBASES=all && \
+    make -j$(nproc) && make install
+
+ENV MMDeploy_DIR="/root/workspace/mmdeploy/build/install/lib/cmake/MMDeploy"
+ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${BACKUP_LD_LIBRARY_PATH}"
+ENV PATH="/root/workspace/mmdeploy/build/bin:${PATH}"
+ENV PYTHONPATH="/root/workspace/mmdeploy:${PYTHONPATH}"
diff --git a/docker/Jetson/Jetpack5/Dockerfile b/docker/Jetson/Jetpack5/Dockerfile
new file mode 100644
index 0000000000..a0f982d077
--- /dev/null
+++ b/docker/Jetson/Jetpack5/Dockerfile
@@ -0,0 +1,78 @@
+FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
+
+ARG MMDEPLOY_VERSION=main
+ENV NVIDIA_VISIBLE_DEVICE all
+ENV NVIDIA_DRIVER_CAPABILITIES all
+ENV CUDA_HOME="/usr/local/cuda"
+ENV PATH="/usr/local/cuda/bin:${PATH}"
+ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/lib/python3.8/dist-packages/opencv-python.libs${LD_LIBRARY_PATH}"
+ENV TENSORRT_DIR="/usr/include/aarch64-linux-gnu"
+
+ENV DEBIAN_FRONTEND=nointeractive
+ENV FORCE_CUDA="1"
+
+USER root
+WORKDIR /root/workspace
+
+# install dependencies
+RUN  apt-get update &&\ 
+     apt-get install -y vim wget libspdlog-dev libssl-dev libpng-dev pkg-config libhdf5-103 libhdf5-dev --no-install-recommends &&\
+     python3 -m pip install onnx versioned-hdf5
+
+# install onnxruntime
+RUN wget https://nvidia.box.com/shared/static/mvdcltm9ewdy2d5nurkiqorofz1s53ww.whl -O onnxruntime_gpu-1.15.1.whl &&\
+    python3 -m pip install --no-cache-dir onnxruntime_gpu-1.15.1-cp38-cp38-linux_aarch64.whl
+
+# install mmcv
+RUN git clone --branch 2.x https://github.com/open-mmlab/mmcv.git &&\
+    python3 -m pip install --no-cache-dir opencv-python==4.5.4.60 opencv-contrib-python==4.5.4.60 opencv-python-headless==4.5.4.60 &&\
+    MMCV_WITH_OPS=1 python3 -m pip install -e .
+
+# build ppl.cv
+RUN git clone https://github.com/openppl-public/ppl.cv.git &&\
+    echo "export PPLCV_DIR=/root/workspace/ppl.cv" >> ~/.bashrc &&\
+    ./build.sh cuda
+  
+# build mmdeploy
+RUN git clone --recursive -b $MMDEPLOY_VERSION --depth 1 https://github.com/open-mmlab/mmdeploy &&\
+    cd mmdeploy &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR    &&\
+    make -j$(nproc) && make install && cd .. &&\
+    cd mmdeploy &&\
+    python3 -m pip install --upgrade setuptools &&\
+    python3 -m pip install -e . &&\
+    mkdir -p build && cd build &&\
+    cmake .. \
+    	-DMMDEPLOY_BUILD_SDK=ON \
+    	-DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
+    	-DMMDEPLOY_BUILD_EXAMPLES=ON \
+    	-DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
+    	-DMMDEPLOY_TARGET_BACKENDS="trt" \
+    	-DTENSORRT_DIR=TENSORRT_DIR \ 
+    	-Dpplcv_DIR=/root/workspace/ppl.cv/cuda-build/install/lib/cmake/ppl \
+    	-DMMDEPLOY_CODEBASES=all && \
+    make -j$(nproc) && make install
+
+# add patch to solve the build error
+RUN sed -i '/def _run_symbolic_method(g, op_name, symbolic_fn, args):/,/except TypeError as e:/\
+   {
+       s/\
+       return symbolic_fn(g, \*args)/\
+       graph_context = jit_utils.GraphContext(\
+            graph=g,\
+            block=g.block(),\
+            opset=GLOBALS.export_onnx_opset_version,\
+            original_node=None,  # type: ignore[arg-type]\
+            params_dict=_params_dict,\
+            env={},\
+       )\
+       return symbolic_fn(graph_context, \*args)/\
+    }' /usr/local/lib/python3.8/dist-packages/torch/onnx/symbolic_helper.py
+
+ENV MMDeploy_DIR="/root/workspace/mmdeploy/build/install/lib/cmake/MMDeploy"
+ENV LD_LIBRARY_PATH="/root/workspace/mmdeploy/build/lib:${BACKUP_LD_LIBRARY_PATH}"
+ENV PATH="/root/workspace/mmdeploy/build/bin:${PATH}"
+ENV PYTHONPATH="/root/workspace/mmdeploy:${PYTHONPATH}"
diff --git a/docs/en/01-how-to-build/Jetson_docker.md b/docs/en/01-how-to-build/Jetson_docker.md
new file mode 100644
index 0000000000..c583c88b7f
--- /dev/null
+++ b/docs/en/01-how-to-build/Jetson_docker.md
@@ -0,0 +1,134 @@
+# Use Jetson Docker Image
+
+This document guides how to install mmdeploy with [Docker](https://docs.docker.com/get-docker/) on Jetson.
+
+## Get prebuilt docker images
+
+MMDeploy provides prebuilt docker images for the convenience of its users on [Docker Hub](https://hub.docker.com/r/openmmlab/mmdeploy). The docker images are built on
+the latest and released versions. We release two docker version, for Jetpack=5.1 and Jetpack=4.6.1
+For instance, the image with tag `openmmlab/mmdeploy_jetpack5:v1` is built for Jetpack5.1 and the image with tag `openmmlab/mmdeploy_jetpack4.6.1:v1` is build for Jetpack 4.6.1.
+The specifications of the Docker Images are shown below.
+
+- jetpack5.1
+
+|    Item     |   Version   |
+| :---------: | :---------: |
+| Jetpack     |     5.1     |
+|   Python    |   3.8.10    |
+|    Torch    |    2.0.0    |
+| TorchVision |   0.15.0    |
+
+- jetpack4.6.1
+
+|    Item     |   Version   |
+| :---------: | :---------: |
+|   Jetpack   |   4.6.1     |
+|   Python    |   3.8.10    |
+|    Torch    |   1.10.0    |
+| TorchVision |   0.11.0    |
+
+- jetpack 5.1
+```shell
+export TAG=openmmlab/mmdeploy_jetpack5:v1
+docker pull $TAG
+```
+- jetpack 4.6.1
+```shell
+export TAG=openmmlab/mmdeploy_jetpack4.6:v1
+docker pull $TAG
+```
+## Build docker images (optional)
+
+If the prebuilt docker images do not meet your requirements,
+then you can build your own image by running the following script.
+The docker file is `docker/jetson/jetpack5/Dockerfile` and `docker/jetson/jetpack4.6/Dockerfile`,
+
+```shell
+docker build docker/jetson/jetpack5 -t openmmlab/mmdeploy_jetpack5:v1 .
+//
+docker build docker/jetson/jetpack4.6 -t openmmlab/mmdeploy_jetpack4.6:v1 .
+```
+
+## Run docker container
+
+After pulling or building the docker image, you can use `docker run` to launch the docker service:
+
+```shell
+docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack5:v1
+//
+docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack4.6:v1
+```
+
+## TroubleShooting
+update: I solved the problem 3, 4 using sed in docker.
+If you using the jetpack5, it has some question need to solve.
+1. OpenCV problem
+  if you find import cv2 wrong, can't find the libpng15.so
+```shell
+  ln -s /usr/local/lib/python3.x/dist-packages/opencv-python.libs/* /usr/lib
+```
+
+2. mmdetection problem
+  if you find installed the mmdetection, but import the mmdet failed. you should use this to install
+```shell
+  python3 -m pip install --user -e .
+```
+
+3. Jetson No distributed problem(this is rewrited with the PR)
+  if you convert the model like [Jetson.md](https://github.com/open-mmlab/mmdeploy/blob/main/docs/en/01-how-to-build/jetsons.md)
+  you may find torch.distributed has no attribute ReduceOp.
+  I just issue and make a simple patch, add file jetson_patch.py on ./mmdeploy/tools/
+```python
+import torch.distributed
+if not torch.distributed.is_available():
+  torch.distributed.ReduceOp = lambda: None
+```
+  and import jetson_patch at the beginning which file you want.
+  I know is not quietly ellegant, but it works well...(for Jetson AGX Orin)
+  
+4. Jetpack with PyTorch 2.0 has some issue
+> If you use the docker, we help you change the PyTorch in dockerfile.
+
+  we need to modify torch.onnx._run_symbolic_method 
+  **from**
+```python
+def _run_symbolic_method(g, op_name, symbolic_fn, args):
+    r"""
+    This trampoline function gets invoked for every symbolic method
+    call from C++.
+    """
+    try:
+        return symbolic_fn(g, *args)
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch
+        # to symbolic_fn.  Otherwise, the backtrace will have the clues
+        # you need.
+        e.args = ("{} (occurred when translating {})".format(e.args[0], op_name),)
+        raise
+```
+  **to**
+```python
+@_beartype.beartype
+def _run_symbolic_method(g, op_name, symbolic_fn, args):
+    r"""
+    This trampoline function gets invoked for every symbolic method
+    call from C++.
+    """
+    try:
+        graph_context = jit_utils.GraphContext(
+            graph=g,
+            block=g.block(),
+            opset=GLOBALS.export_onnx_opset_version,
+            original_node=None,  # type: ignore[arg-type]
+            params_dict=_params_dict,
+            env={},
+        )
+        return symbolic_fn(graph_context, *args)
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch
+        # to symbolic_fn.  Otherwise, the backtrace will have the clues
+        # you need.
+        e.args = (f"{e.args[0]} (occurred when translating {op_name})",)
+        raise
+  ```
+Finally we can use Jetpack5.1 && MMDeploy happily:)
diff --git a/docs/zh_cn/01-how-to-build/Jetson_docker.md b/docs/zh_cn/01-how-to-build/Jetson_docker.md
new file mode 100644
index 0000000000..233334a063
--- /dev/null
+++ b/docs/zh_cn/01-how-to-build/Jetson_docker.md
@@ -0,0 +1,123 @@
+# 使用Jetson Docker镜像
+
+本文档指导如何在Jetson上通过[Docker](https://docs.docker.com/get-docker/)安装mmdeploy。
+
+## 获取预构建的docker镜像
+
+MMDeploy为用户在[Docker Hub](https://hub.docker.com/r/openmmlab/mmdeploy)上提供了预构建的docker镜像。这些docker镜像基于最新和已发布的版本构建。我们发布了两个版本的docker镜像，分别为Jetpack=5.1和Jetpack=4.6.1。例如，标签为`openmmlab/mmdeploy_jetpack5:v1`的镜像是为Jetpack5.1构建的，而标签为`openmmlab/mmdeploy_jetpack4.6.1:v1`的镜像则是为Jetpack 4.6.1构建的。Docker镜像的规格如下所示。
+
+- jetpack5.1
+
+|    项目    |   版本    |
+| :--------: | :------: |
+| Jetpack    |   5.1    |
+| Python     |  3.8.10  |
+| Torch      |   2.0.0  |
+| TorchVision|  0.15.0  |
+
+- jetpack4.6.1
+
+|    项目    |   版本    |
+| :--------: | :------: |
+| Jetpack    |   4.6.1  |
+| Python     |  3.8.10  |
+| Torch      |  1.10.0  |
+| TorchVision|  0.11.0  |
+
+- jetpack 5.1
+```shell
+export TAG=openmmlab/mmdeploy_jetpack5:v1
+docker pull $TAG
+```
+- jetpack 4.6.1
+```shell
+export TAG=openmmlab/mmdeploy_jetpack4.6:v1
+docker pull $TAG
+```
+
+## 构建docker镜像（可选）
+如果预构建的docker镜像不符合您的要求，您可以通过运行以下脚本来构建自己的镜像。docker文件分别为docker/jetson/jetpack5/Dockerfile和docker/jetson/jetpack4.6/Dockerfile，
+
+```shell
+docker build docker/jetson/jetpack5 -t openmmlab/mmdeploy_jetpack5:v1 .
+//
+docker build docker/jetson/jetpack4.6 -t openmmlab/mmdeploy_jetpack4.6:v1 .
+```
+
+## 运行docker容器
+拉取或构建docker镜像后，您可以使用docker run来启动docker服务：
+
+```shell
+docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack5:v1
+//
+docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack4.6:v1
+```
+
+## 故障排除
+如果您使用的是jetpack5，可能需要解决一些问题。
+
+1. OpenCV问题
+如果您发现import cv2时出错，找不到libpng15.so
+```shell
+  ln -s /usr/local/lib/python3.x/dist-packages/opencv-python.libs/* /usr/lib
+```
+2. mmdetection问题
+如果您发现安装了mmdetection，但无法导入mmdet。您可以使用以下命令来安装
+```shell
+  python3 -m pip install --user -e .
+```
+3. Jetson分布式问题(已在PR中重写)
+如果您按照Jetson.md中的方法转换模型，您可能会发现torch.distributed没有ReduceOp属性。我只是提出了问题并做了一个简单的补丁，在./mmdeploy/tools/下添加jetson_patch.py文件
+```python
+import torch.distributed
+if not torch.distributed.is_available():
+  torch.distributed.ReduceOp = lambda: None
+```
+并在您需要的文件开头导入jetson_patch。我知道这并不优雅，但它确实有效...(适用于Jetson AGX Orin)
+
+4. Jetpack5.1 PyTorch2.0有一些问题
+> 如果您直接使用我们的预编译docker镜像或者使用dockerfile进行构建镜像，这个问题已在dockerfile中解决
+
+  我们需要修改 torch.onnx._run_symbolic_method 这个函数。
+  **从**
+```python
+def _run_symbolic_method(g, op_name, symbolic_fn, args):
+    r"""
+    This trampoline function gets invoked for every symbolic method
+    call from C++.
+    """
+    try:
+        return symbolic_fn(g, *args)
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch
+        # to symbolic_fn.  Otherwise, the backtrace will have the clues
+        # you need.
+        e.args = ("{} (occurred when translating {})".format(e.args[0], op_name),)
+        raise
+```
+  **到**
+```python
+@_beartype.beartype
+def _run_symbolic_method(g, op_name, symbolic_fn, args):
+    r"""
+    This trampoline function gets invoked for every symbolic method
+    call from C++.
+    """
+    try:
+        graph_context = jit_utils.GraphContext(
+            graph=g,
+            block=g.block(),
+            opset=GLOBALS.export_onnx_opset_version,
+            original_node=None,  # type: ignore[arg-type]
+            params_dict=_params_dict,
+            env={},
+        )
+        return symbolic_fn(graph_context, *args)
+    except TypeError as e:
+        # Handle the specific case where we didn't successfully dispatch
+        # to symbolic_fn.  Otherwise, the backtrace will have the clues
+        # you need.
+        e.args = (f"{e.args[0]} (occurred when translating {op_name})",)
+        raise
+  ```
+最后您就可以开心的使用这个镜像了:)
diff --git a/mmdeploy/pytorch/functions/distribute.py b/mmdeploy/pytorch/functions/distribute.py
new file mode 100644
index 0000000000..a069d175f7
--- /dev/null
+++ b/mmdeploy/pytorch/functions/distribute.py
@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+import torch
+
+from mmdeploy.core import FUNCTION_REWRITER
+
+@FUNCTION_REWRITER.register_rewriter(func_name='torch.distributed')
+def distributed_rewriter():
+    """rewrite torch.distributed to support some embedding device for higher PyTorch"""
+    # check torch.distributed is available?
+    if not torch.distributed.is_available():
+        torch.distributed.ReduceOp = lambda: None