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--- lwc:hardware:jetson [2025/03/04 19:22] – John Harrison
+++ lwc:hardware:jetson [2025/03/05 17:12] (current) – [misc] John Harrison
@@ Line 19: / Line 19: @@
     * at that point I switched to ethernet as both units were connected to my router. I'm not sure if sticking with usb might have worked or would have been faster
-==== Installing pyTorch (WIP. May not be correct) ====
+==== Installing pyTorch ====
+//corrected from: [[https://docs.nvidia.com/deeplearning/frameworks/install-pytorch-jetson-platform/index.html]])//
   * install ''jetpack''. (This is not the same as installing the OS for Jetson): ''sudo apt install nvidia-jetpack''
   * get version of CUDA: ''nvcc --version''
-  * it's unclear if the following is needed (run as root) (from: [[https://docs.nvidia.com/deeplearning/frameworks/install-pytorch-jetson-platform/index.html]])
+  * install ''cusparselt'':
-'''
+<code>
-wget
+wget raw.githubusercontent.com/pytorch/pytorch/5c6af2b583709f6176898c017424dc9981023c28/.ci/docker/common/install_cusparselt.sh
-raw.githubusercontent.com/pytorch/pytorch/5c6af2b583709f6176898c017424dc9981023c28/.ci/docker/
+sudo -s # set CUDA version and install as root
-common/install_cusparselt.sh
+export CUDA_VERSION=12.1 # as an example. Use the version returned from ''nvcc --version''
-export CUDA_VERSION=12.1 # as an example. (example putting in the correct CUDA version caused this to fail. Only 12.1 worked)
 bash ./install_cusparselt.sh
-'''
+</code>
-  * install correct numpy version: ''sudo pip install numpy=1.26.1''
+  * install correct numpy version: ''sudo pip install numpy==1.26.1''
   * download correct version of pytorch for jetpack version. Search for it at [[https://developer.download.nvidia.cn/compute/redist/jp/]]
+<code>
+sudo pip3 install --no-cache http://<URL TO PYTORCH WHL>
+</code>
+==== misc ====
+  * [[https://elinux.org/Jetson_Zoo#ONNX_Runtime|onnxruntime for Jetson]]
+  * if ''onnxruntime'' fails with message ''Invalid argument. Specify the number of threads explicitly'' fix with one-time cli command: ''sudo nvpmodel -m 0'' which forces the computer to with no throttling
+  * ''tegrastats'' might show load on GPU?
+    * ''tegrastats | cut -f 14 -d " "'' shows % load on GPU
+  * you can get rid of the warning ''System Throttled due to Over-current'' by editing ''/usr/share/nvpmodel_indicator/nvpmodel_indicator.py''