A2D2 / environment.yml
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# Conda environment shared across the molecule, peptide, and language experiments.
# Create with:
# conda env create -f environment.yml
# conda activate a2d2
#
# NOTE: flash-attn is hardware-specific and must be built against your installed torch
# and CUDA, so it is not listed below. It is imported by the shared transformer backbone
# (model/casual_transformer.py, model/rotary.py) and is required for all experiments.
# After creating the env, install it with:
# pip install flash-attn==2.8.3 --no-build-isolation
# Adjust pytorch-cuda below to match your CUDA toolkit / GPU.
name: a2d2
channels:
- pytorch
- nvidia
- conda-forge
dependencies:
- python=3.11
- pip
- pytorch
- pytorch-cuda=12.1
- rdkit=2023.9.6
- jupyterlab # for demo/quality_inference_demo.ipynb
- pip:
# --- core scientific / DL stack ---
- numpy==1.26.4
- scipy==1.17.1
- pandas==2.1.4
- scikit-learn==1.8.0
- pytorch-lightning==2.6.0
- lightning==2.6.1
- transformers==4.55.4
- tokenizers==0.21.4
- safetensors==0.7.0
- accelerate==0.33.0
- peft==0.15.1 # LoRA adapters (language experiment)
- datasets==2.19.2
- huggingface-hub==0.36.2
- einops==0.8.2
- timm==1.0.26
- omegaconf==2.3.0
- wandb==0.26.1
# --- molecule experiment ---
- safe-mol==0.1.14
- datamol==0.12.5
- PyTDC==1.1.15
# --- peptide experiment ---
- SmilesPE==0.0.3
- fair-esm==2.0.0
- xgboost==3.2.0
# --- plotting / utilities ---
- matplotlib==3.10.6
- seaborn==0.13.2
- tqdm==4.67.1
- joblib==1.5.3
- loguru==0.7.3
- fsspec==2024.3.1