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Merge pull request #4 from colbyford/main
Add Docker Image
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‎Dockerfile

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FROM pytorch/pytorch:2.2.1-cuda11.8-cudnn8-runtime
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LABEL authors="Colby T. Ford <[email protected]>"
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## Install system requirements
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RUN apt update && \
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apt-get install -y --reinstall \
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ca-certificates && \
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apt install -y \
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git \
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wget \
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libxml2 \
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libgl-dev \
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libgl1 \
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gcc \
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g++
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## Set working directory
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RUN mkdir -p /software/flowdock
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WORKDIR /software/flowdock
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## Clone project
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RUN git clone https://github.com/BioinfoMachineLearning/FlowDock /software/flowdock
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## Create conda environment
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# RUN conda env create -f environments/flowdock_environment.yaml
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COPY environments/flowdock_environment_docker.yaml /software/flowdock/environments/flowdock_environment_docker.yaml
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RUN conda env create -f environments/flowdock_environment_docker.yaml
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## Automatically activate conda environment
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RUN echo "source activate flowdock" >> /etc/profile.d/conda.sh && \
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echo "source /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
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echo "conda activate flowdock" >> ~/.bashrc
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## Default shell and command
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SHELL ["/bin/bash", "-l", "-c"]
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CMD ["/bin/bash"]

‎README.md

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- [How to create comparative plots of evaluation results](#how-to-create-comparative-plots-of-evaluation-results)
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- [How to predict new protein-ligand complex structures and their affinities using FlowDock](#how-to-predict-new-protein-ligand-complex-structures-using-flowdock)
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- [For developers](#for-developers)
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- [Docker](#docker)
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- [Acknowledgements](#acknowledgements)
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- [License](#license)
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- [Citing this work](#citing-this-work)
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</details>
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## Docker
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<details>
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Given that this tool has a number of dependencies, it may be easier to run it in a Docker container.
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Pull from [Docker Hub](https://hub.docker.com/repository/docker/cford38/flowdock): `docker pull cford38/flowdock:latest`
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Alternatively, build the Docker image locally:
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```bash
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docker build --platform linux/amd64 -t flowdock .
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```
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Then, run the Docker container (and mount your local `checkpoints/` directory)
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```bash
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docker run --gpus all -v ./checkpoints:/software/flowdock/checkpoints --rm --name flowdock -it flowdock /bin/bash
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# docker run --gpus all -v ./checkpoints:/software/flowdock/checkpoints --rm --name flowdock -it cford38/flowdock:latest /bin/bash
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```
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</details>
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## Acknowledgements
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`FlowDock` builds upon the source code and data from the following projects:

‎environments/flowdock_environment_docker.yaml

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name: flowdock
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channels:
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- pyg
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- iopath
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- pytorch
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- nvidia
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- defaults
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- conda-forge
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dependencies:
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- hydra-core=1.3.2=pyhd8ed1ab_0
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- mendeleev=0.15.0=pyhc1e730c_0
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- msgpack-python=1.0.3=py39hd09550d_0
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- networkx=3.1=py39h06a4308_0
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- omegaconf=2.3.0=pyhd8ed1ab_0
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- pandas=2.1.4=py39h1128e8f_0
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- python=3.9.17=h0755675_0_cpython
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- pytorch=2.2.1=py3.9_cuda11.8_cudnn8.7.0_0
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- pytorch-cuda=11.8=h7e8668a_5
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- pytorch-mutex=1.0=cuda
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- pytorch-scatter=2.1.2=py39_torch_2.2.0_cu118
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- rdkit=2024.03.1=py39h6cc1c65_0
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- rich=13.3.5=py39h06a4308_0
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- scikit-learn=1.4.1.post1=py39ha22ef79_0
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- scipy=1.12.0=py39h474f0d3_2
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- torchaudio=2.2.1=py39_cu118
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- torchtriton=2.2.0=py39
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- torchvision=0.17.1=py39_cu118
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- tqdm=4.66.1=pyhd8ed1ab_0
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- pip:
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- beartype==0.17.2
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- biopandas==0.4.1
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- biopython>=1.79
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- dm-tree==0.1.8
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- einops==0.7.0
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- fair-esm==2.0.0
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- hydra-colorlog==1.2.0
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- lightning==2.2.2
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- lightning-utilities==0.11.1
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- lovely-numpy==0.2.11
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- lovely-tensors==0.1.15
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- ml-collections==0.1.1
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- msgpack-numpy==0.4.8
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- numpy==1.23.5
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- git+https://github.com/amorehead/openfold.git@fe1275099639bf7e617e09ef24d6af778647dd64
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- prody==2.4.1
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- pytorch-lightning==2.2.2
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- git+https://github.com/facebookresearch/pytorch3d.git@3da7703c5ac10039645966deddffe8db52eab8c5
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- rootutils==1.0.7
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- torchmetrics==1.3.1
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- wandb==0.17.0

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