Code for arxiv paper: https://arxiv.org/abs/2406.05250
LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation
- If using OpenAI, set up environment variables:
echo "export OPENAI_API_KEY={api_key}" >> ~/.zshrc
echo "export OPENAI_API_VERSION={api_version}" >> ~/.zshrc
## Note: these might be optional
echo "export OPENAI_API_BASE={api_base}" >> ~/.zshrc
echo "export OPENAI_API_TYPE={api_type}" >> ~/.zshrc
In our experiments, we used gpt-turbo-3.5 for all modules and gpt-turbo-3.5-instruct for the generative surrogate model (Note: these models might require separate set of credentials).
- Update the shell with the new variables:
source ~/.zshrc
- Confirm that environmental variables are set:
echo $OPENAI_API_KEY
echo $OPENAI_API_VERSION
echo $OPENAI_API_BASE
echo $OPENAI_API_TYPE
- Set up Conda environment:
git clone
conda create -n llana python=3.11.8
conda install jupyter
conda activate llana
## Note: {project_dir} is the path to where to your local directory
export PROJECT_DIR={project_dir}
conda env config vars set PYTHONPATH=${PYTHONPATH}:${PROJECT_DIR}
conda env config vars set PROJECT_DIR=${PROJECT_DIR}
conda deactivate
conda activate llana
- Install requirements:
pip install -r requirements.txt
To reproduce results, execute any of the shell experimental shell scripts:
- To run benchmark:
run_custom_baselines.sh - To evaluate the surrogate model:
run_evaluate_dis_sm_openai.sh(discriminative SM)
To check the experimal logs
exp_evaluate_sm/logs
exp_custom/openai