Merge pull request #1 from yinfan98/patch-3

Create Jetson_docker.md

docs/en/01-how-to-build/Jetson_docker.md

@@ -0,0 +1,130 @@
+# 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:v2` 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 $TAG
+```
+## Build docker images (optional)
+
+If the prebuilt docker images do not meet your requirements,
+then you can build your own image by running the following script.
+The docker file is `docker/jetson/jetpack5/Dockerfile` and `docker/jetson/jetpack4.6/Dockerfile`,
+
+```shell
+sudo docker build docker/jetson/jetpack5 -t mmdeploy_jetson5:v1 .
+//
+sudo docker build docker/jetson/jetpack4.6 -t mmdeploy_jetson4.6:v1 .
+```
+
+## Run docker container
+
+After pulling or building the docker image, you can use `docker run` to launch the docker service:
+
+```shell
+sudo docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack5:v2
+//
+sudo docker run -it --rm --runtime nvidia --network host openmmlab/mmdeploy_jetpack4.6:v1
+```
+
+## TroubleShooting
+If you using the jetpack5, it has some question need to solve.
+1. OpenCV problem
+  if you find import cv2 wrong, cant find the libpng15.so
+```shell
+  ln -s /usr/local/lib/python3.x/dist-packages/opencv-python.libs/* /usr/lib
+```
+
+2. mmdetection problem
+  if you find installed the mmdetection, but import the mmdet failed. you should add the mmdet path to PYTHONPATH
+```shell
+  export PYTHONPATH=$PYTHONPATH/your/mmdetection/you/git/clone
+```
+
+3.Jetson No distributed problem
+  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 konw is really not ellegant, but it works well...(for Jetson AGX Orin)
+4.Jetpack with PyTorch 2.0 has some issue
+  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