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[Doc] update build doc for jetsons (#1621)
* update build doc for jetsons * set lint only true for docs * change base revision of circleci config Co-authored-by: RunningLeon <mnsheng@yeah.net>
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@ -26,12 +26,13 @@ workflows:
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csrc/.* lint_only false
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demo/csrc/.* lint_only false
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docker/.* lint_only false
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docs/.* lint_only true
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mmdeploy/.* lint_only false
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requirements/.* lint_only false
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tests/.* lint_only false
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third_party/.* lint_only false
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tools/.* lint_only false
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base-revision: master
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base-revision: dev-1.x
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# this is the path of the configuration we should trigger once
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# path filtering and pipeline parameter value updates are
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# complete. In this case, we are using the parent dynamic
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@ -3,7 +3,6 @@
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In this chapter, we introduce how to install MMDeploy on NVIDIA Jetson platforms, which we have verified on the following modules:
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- Jetson Nano
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- Jetson Xavier NX
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- Jetson TX2
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- Jetson AGX Xavier
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@ -174,12 +173,27 @@ It takes about 1 hour 40 minutes to install MMCV on a Jetson Nano. So, please be
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#### Install ONNX
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Don't install the latest ONNX. The recommended ONNX version is 1.10.0.
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```shell
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# Execute one of the following commands
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pip install onnx
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pip install onnx==1.10.0
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conda install -c conda-forge onnx
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```
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If the installation failed and showed the following error:
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```
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CMake Error at CMakeLists.txt:299 (message):
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Protobuf compiler not found
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```
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Please install dependencies:
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```shell
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sudo apt-get install protobuf-compiler libprotoc-dev
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```
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#### Install h5py and pycuda
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Model Converter employs HDF5 to save the calibration data for TensorRT INT8 quantization and needs `pycuda` to copy device memory.
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@ -156,12 +156,27 @@ conda activate mmdeploy
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- 安装 ONNX
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不要安装最新的 ONNX,推荐的 ONNX 版本是 1.10.0。
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```shell
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# 以下方式二选一
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python3 -m pip install onnx
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python3 -m pip install onnx==1.10.0
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conda install -c conda-forge onnx
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```
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如果安装时,出现如下的报错信息:
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```
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CMake Error at CMakeLists.txt:299 (message):
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Protobuf compiler not found
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```
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请根据如下指令,安装依赖项:
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```shell
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sudo apt-get install protobuf-compiler libprotoc-dev
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```
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- 安装 h5py 和 pycuda
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Model Converter 使用 HDF5 存储 TensorRT INT8 量化的校准数据;需要 pycuda 拷贝显存
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