Guide to using the DFC to convert a modified YoloV11 on Google Colab

Fixed the Optimization Issue

For this I referred to this article.

To do this in colab, this is what I did:

Step 1:

!wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda-repo-ubuntu2204-11-8-local_11.8.0-520.61.05-1_amd64.deb

Step 2:

!sudo dpkg -i cuda-repo-ubuntu2204-11-8-local_11.8.0-520.61.05-1_amd64.deb

Step 3:

!sudo cp /var/cuda-repo-ubuntu2204-11-8-local/cuda-D95DBBE2-keyring.gpg /usr/share/keyrings/

Step 4:

!sudo apt-get update

Step 5:

!sudo apt-get install -y cuda-11-8

For this step you have to fill provide the language, I’m using English as my default so I entered “32”, then “1” for the dialect of english. There should be a little text box next to the terminal prompt, so click the area next to it and you should be able to type.


Step 6:

!echo 'export PATH=/usr/local/cuda-11.8/bin:$PATH' >> ~/.bashrc
!echo 'export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
!source ~/.bashrc

Step 7:

!ls /usr/local | grep cuda

This step is just a sanity check to see if Cuda 11.8 was installed.

Step 8:

!sudo ln -sf /usr/local/cuda-11.8 /usr/local/cuda

Step 9:

!ls -l /usr/local/cuda
!sudo update-alternatives --display cuda
!sudo update-alternatives --config cuda

This should be the expected output, for the last couple steps.

I did this step right before the second step of the compilation process, but you can do it at any time before optimization.
Note: For optimization level 1, you will need at least a calibration dataset of 64 images. For optimization level 2, you will need 1024 images.

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