Suite of tools for cancer drug response prediction.
- Make tensorflow an optional dependency
- Each dataset should be a subclass of the Dataset class and should have a download method that stores the raw data in ~/.cdrpy/datasets or another specified directory
conda create python=3.9.13 --name=cdrpy-tf-gpu-v2
conda activate cdrpy-tf-gpu-v2
module load cuda/11.3 cudnn/8.2.0
conda install -c conda-forge cudatoolkit=11.8.0
pip install nvidia-cudnn-cu11==8.6.0.163
pip install tensorflow==2.11.*
pip install tensorflow-probability==0.19.0
conda create python=3.9.13 --name=cdrpy-tf-gpu
module load cuda/11.2 cudnn/8.1.1
conda activate cdrpy-tf-gpu
conda install -c conda-forge cudatoolkit=11.2.2 cudnn=8.1.0.77
pip install tensorflow==2.10.0
pip install tensorflow-probability==0.18.0
Create a conda environments:
conda create python=3.9.13 --name=cdrpy-tf-cpu
conda create --name cdrpy-tf python=3.9.13
conda activate cdrpy-tf
python -m pip install tensorflow
python -m pip install tensorflow-metal
- Currently, scripts must be run from root directory
For GPU usage:
module load cuda/11.2 cudnn/8.1.1
conda activate cdrpy-tf-gpu
module load cuda/11.3 cudnn/8.2.0
conda create --name cdrpy-tf-gpu-v2 python=3.9.13
- networkx
- pandas
- deepchem
- tensorflow-propability
- scikit-learn
- hydra-core