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Suggested changes for installing Netdiffusion dependencies. #5
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Thanks!this really helped me a lot!Also do you know where the full data set is? Thanks a lot! |
@TracyTd Unfortunately, the dataset used for the paper is private. The authors only shared an example of PCAP to play with the model. Try to collect your own traffic data or download PCAP datasets from other papers. |
@CiroGuida Thanks a lot! |
@CiroGuida May I ask you one more question? In the Data Preprocessing,is fine_tune/sd-webui-fork/stable-diffusion-webui/webui.sh downloading from https://github.com/AUTOMATIC1111/stable-diffusion-webui or other places? I can't find correct file in https://github.com/Chasexj/stable-diffusion-webui-fork/tree/2533cf84cad7313b8653717d15728a2d9a862c15. Thank you so much! |
Yes, the link is automatic1111. However, the correct version of stable diffusion (for netdiffusion) is already forked into the repository. So, you have to manage two env variables in webui-user.sh (.bat if you are using Windows and modify it according to Windows). Modify them as follows:
Also, you might want to delete the empty directory stable-diffusion-webui |
Thank you very much! |
Please consider making the YAML configuration file public to facilitate the installation of Netdiffusion. Currently, the requirements.txt file is not usable by the pip utility.
In my case, I had to create a YAML configuration file from the requirements file thanks to the script provided here: https://gist.github.com/mfansler/5a1a703d1ac6bb2139547003248c3827
In your case, you can create the correct YAML when your environment is active, with this command:
conda env export | head -n -1 | tail -n +2 > environment.yml
Then I create a new virtual environment with the following:
conda create --name my_env pip python=3.10.9
and activate the environment with
conda activate my_env
Then I installed tensorrt-libs from the pypi nvidia repository, as this package is not available in pypi and conda cannot resolve it:
pip install --extra-index-url https://pypi.nvidia.com tensorrt-libs
This step is mandatory to install all requirements listed in the YAML config file.
Next, I downgraded torch and triton to 2.0.1 and 2.0.0 respectively, because some packages were not compatible with torch 2.2.0 stated in the original requirements.txt
Finally, I updated all dependencies with:
conda env update -n my_env -f environment.yml
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