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Tool/software:
I am trying to run the Jupyter notebook examples in the edgeai-tidl-tools repository but have encountered segmentation faults when running them.
The segmentation faults are thrown once the InferenceSession
is created for the models (in the onnx examples). I am able to run the examples provided under examples/osrt_python
, so the edgeai-tidl-tools
should be installed. Ive used the docker based installation (SOC=am62a) and have followed the outlined Jupyter notebook setup instructions. I have tried with the latest SDK version as well as one that is compatible with my devboard (version 9.0.0).
Any suggestions?
Hi Stefan,
Could you mention which specific jupyter notebook is giving you this issue and which model? I can try to reproduce on my side
Is this during compilation / import or during inference?
I have tried with the latest SDK version as well as one that is compatible with my devboard (version 9.0.0).
Are you using the AM62A-SK-LP board (the official EVM from TI site) or a different development board? As noted in our previous thread, the compiled models ("artifacts" in our jargon) are tied to an SDK version. The most recent 10.0 release has made improvements on logging and verbosity. If it's possible to reflash the most recent SDK, I would recommend so. If you are using a 3rd party SOM or dev board, then please stick with their provided BSP/SDK
BR,
Reese
Hi ,
Ive just gotten back to looking at this issue. I have issues with multiple notebooks, but I am looking at "custom-model-onnx" now. As soon as I create the onnx inference session (sess = rt.InferenceSession(onnx_model_path ,providers=EP_list, provider_options=[compile_options, {}], sess_options=so) ) the kernel just dies. Increasing the debugging level doesnt help, since the kernel dies before any outputs are logged. This is during the compilation workflow.
The osrt_python/ort example doesnt work for me as well (although the kernel doesnt die right away). The tflite scripts worked without issues.
I am using a AM62A-SK-LP board. Right now, I need to stick to the 9.0.0 version. I would greatly appreciate some help here.
BR,
Stefan
Hi Stefan,
Increasing the debugging level doesnt help, since the kernel dies before any outputs are logged. This is during the compilation workflow.
This is a weakness of Jupyter, I believe -- it doesn't handle segfaults or similar lower level failures well. Debugging a model in this workflow is rather challenging, which is why I recommend the explicit python scripts for this.
The osrt_python/ort example doesnt work for me as well (although the kernel doesnt die right away
I can help more with this -- can you post a log of this failure? I assume this model is a custom architecture. If you're willing to, please post the model file (random weights are fine; the weights don't matter much for import, so best to avoid any IP leak)
I am using a AM62A-SK-LP board. Right now, I need to stick to the 9.0.0 version. I would greatly appreciate some help here.
9.0.0, got it. Some of the debugging logs are not as helpful as the more recent SW release, although SDK version is tightly linked to the TIDL tools version. I understand if it's not acceptable to upgrade.