PYNQ: PYTHON PRODUCTIVITY

Can I import torch on PYNQ?

Can I import the pytorch framework package on PYNQ ?

Hey @B1D1ng from reading this post under the section results. The author mentions that building the PyTorch Framework on a Ulrta96 Board never finishes.

There is another approach in this post. I didn’t try out either of both approaches. It just happens that I wanted to try out the same thing and was reading through the posts. So if you have any results, it would be nice if you share it here :smile:

Thanks for your reply!! Have you ever used the QNN-MO-PYNQ?(https://github.com/Xilinx/QNN-MO-PYNQ) I do not know how can this model detect new targets like smoke, helmet etc.
On the Pytorch-Yolov3 I can train my custom dataset and finally detect the new target. QNN-MO-PYNQ’s author seems do not tell something about that.

I’ve looked into the GitHub Site and saw that they’ve used the FINN Framework with the HLS workflow. But I’ve never used it before I am sorry. I would probably create a new post for further instructions.

Might be interesting for you to try to compile your model for the DPU since PYNQ now supports Xilinx DPU IP. The DPU workflow seems to be easier than the FINN workflow.

thank u very much!
I searched DPU-PYNQ and found that it supports the Yolov3 model and can even be trained and tested. Is the Yolov3 model reproduced in this project? Do I only need to provide the dataset for training?

If you want to train your own models, I would recommend to follow this instructions. They show an example how to train and compile a model with the MNIST data set for the DPU.

Thanks for your reply!
The MNIST data set is for numbers, but my data set is a picture data set. I plan to deploy the target detection model to the PYNQ-Z2 board. After deployment, the performance of the model is temporarily ignored, as long as it can be successfully deployed .
I understand that the PYNQ-Z2 board can also support DPU. Does this mean that I can quantify and compile deep learning models such as Tensorflow-yolov3 and deploy them on the PYNQ-Z2 board? Even if the board performance is not particularly suitable.
Looking forward to your reply! Thank you very much !

for the answer: " Does this mean that I can quantify and compile deep learning models such as Tensorflow-yolov3 and deploy them on the PYNQ-Z2 board? "

I think so, but unfortunately I didn’t try it out myself.
I am not sure if the DPU supports the PYNQ-Z2. Probably somebody else on the forum tried it out before.

Probably it is better to reformulate the question and try to get somebody who is more knowledgeable than me. :wink:

OK, anyway, thank u so much! Have a good day!!

Hey, have you tried the instructions on the PYNQ-Torch work?