From 1dd3ed8a1c93b85afb02206554175e726cf00004 Mon Sep 17 00:00:00 2001 From: yinfan98 <32722923+yinfan98@users.noreply.github.com> Date: Thu, 7 Dec 2023 00:49:21 +0800 Subject: [PATCH] Create Jetson_docker.md --- docs/zh_cn/01-how-to-build/Jetson_docker.md | 120 ++++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100644 docs/zh_cn/01-how-to-build/Jetson_docker.md 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..40620cf057 --- /dev/null +++ b/docs/zh_cn/01-how-to-build/Jetson_docker.md @@ -0,0 +1,120 @@ +# 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:v2`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 $TAG +``` + +## 构建docker镜像（可选） +如果预构建的docker镜像不符合您的要求，您可以通过运行以下脚本来构建自己的镜像。docker文件分别为docker/jetson/jetpack5/Dockerfile和docker/jetson/jetpack4.6/Dockerfile， + +```shell +sudo docker build docker/jetson/jetpack5 -t mmdeploy_jetson5:v1 . +// +sudo docker build docker/jetson/jetpack4.6 -t mmdeploy_jetson4.6:v1 . +``` + +## 运行docker容器 +拉取或构建docker镜像后，您可以使用docker run来启动docker服务： + +```shell +sudo docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack5:v2 +// +sudo docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack4.6:v1 +``` + +## 故障排除 +如果您使用的是jetpack5，可能需要解决一些问题。 + +1. OpenCV问题 +如果您发现import cv2时出错，找不到libpng15.so +```shell + ln -s /usr/local/lib/python3.x/dist-packages/opencv-python.libs/* /usr/lib +``` +2. mmdetection问题 +如果您发现安装了mmdetection，但无法导入mmdet。您应该将mmdet路径添加到PYTHONPATH +```shell + export PYTHONPATH=$PYTHONPATH/your/mmdetection/you/git/clone +``` +3. Jetson分布式问题 +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 + 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:)