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DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology.
- Requirements
- Installation
- FAQ and Troubleshooting
- Getting Started
- Contributing to DeepChem
- About Us
- Citing DeepChem
DeepChem requires these packages on any condition.
- joblib
- NumPy
- pandas
- scikit-learn
- SciPy
- TensorFlow
deepchem>=2.4.0requires tensorflow v2deepchem<2.4.0requires tensorflow v1
DeepChem has a number of "soft" requirements. These are packages which are needed for various submodules of DeepChem but not for the package as a whole.
RDKit is a soft requirement package, but many useful methods like molnet depend on it. If you use conda, we recommend installing RDKit with deepchem.
deepchem>=2.4.0
Coming soon...
deepchem<2.4.0
pip install tensorflow==1.14
conda install -c rdkit -c conda-forge rdkit deepchem==2.3.0If you want GPU support:
pip install tensorflow-gpu==1.14
conda install -c rdkit -c conda-forge rdkit deepchem==2.3.0You are able to try to install deepchem via pip using the following command.
However, pip installation is under development, so this command may not work well.
deepchem>=2.4.0
Coming soon...
deepchem<2.4.0
pip install pandas pillow scikit-learn==0.22 tensorflow==1.14 deepchem==2.2.1.dev54If you want GPU support:
pip install pandas pillow scikit-learn==0.22 tensorflow-gpu==1.14 deepchem==2.2.1.dev54You can install deepchem in a new conda environment using the conda commands in scripts/install_deepchem_conda.sh. Installing via this script will ensure that you are installing from the source.
The following script requires conda>=4.4 because it uses the conda activate command. (Please see the detail from here)
First, please clone the deepchem repository from GitHub.
git clone https://github.com/deepchem/deepchem.git
cd deepchemThen, execute the shell script.
bash scripts/install_deepchem_conda.sh deepchemIf you are using the Windows and the PowerShell:
.\scripts\install_deepchem_conda.ps1 deepchemBefore activating deepchem environment, make sure conda has been initialized.
Check if there is a (base) in your command line. If not, use conda init <YOUR_SHELL_NAME> to activate it, then:
conda activate deepchem
python setup.py install
pytest -m "not slow" deepchem # optionalCheck this link for more information about the installation of conda environments.
We created sample Dockerfiles based on the nvidia/cuda:10.1-cudnn7-devel image.
If you want to build your own deepchem environment, these files may be helpful.
docker/x.x.x: build an image by using conda package manager (x.x.x is a version of deepchem)docker/master: build an image from master branch of deepchem source codes
We couldn't check if this introduction works well or not.
First, you pull the latest stable deepchem docker image.
docker pull deepchemio/deepchemThen, you create a container based on our latest image.
docker run -it deepchemio/deepchemIf you want GPU support:
# If nvidia-docker is installed
nvidia-docker run -it deepchemio/deepchem
docker run --runtime nvidia -it deepchemio/deepchem
# If nvidia-container-toolkit is installed
docker run --gpus all -it deepchemio/deepchemYou are now in a docker container whose python has deepchem installed.
# you can start playing with it in the command line
pip install jupyter
ipython
import deepchem as dc
# you can run our tox21 benchmark
cd /deepchem/examples
python benchmark.py -d tox21-
DeepChem currently supports Python 3.5 through 3.7, and is supported on 64 bit Linux and Mac OSX. Note that DeepChem is not currently maintained for older versions of Python or with other operating systems.
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Question: I'm seeing some failures in my test suite having to do with MKL
Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.Answer: This is a general issue with the newest version of
scikit-learnenabling MKL by default. This doesn't play well with many linux systems. See BVLC/caffe#3884 for discussions. The following seems to fix the issueconda install nomkl numpy scipy scikit-learn numexpr conda remove mkl mkl-service
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Note that when using Ubuntu 16.04 server or similar environments, you may need to ensure libxrender is provided via e.g.:
sudo apt-get install -y libxrender-devThe DeepChem project maintains an extensive colelction of tutorials. All tutorials are designed to be run on Google colab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence which will take you from beginner to proficient at molecular machine learning and computational biology more broadly.
After working through the tutorials, you can also go through other examples. To apply deepchem to a new problem, try starting from one of the existing examples or tutorials and modifying it step by step to work with your new use-case. If you have questions or comments you can raise them on our gitter.
Join us on gitter at https://gitter.im/deepchem/Lobby. Probably the easiest place to ask simple questions or float requests for new features.
DeepChem is managed by a team of open source contributors. Anyone is free to join and contribute!
If you have used DeepChem in the course of your research, we ask that you cite the "Deep Learning for the Life Sciences" book by the DeepChem core team.
To cite this book, please use this bibtex entry:
@book{Ramsundar-et-al-2019,
title={Deep Learning for the Life Sciences},
author={Bharath Ramsundar and Peter Eastman and Patrick Walters and Vijay Pande and Karl Leswing and Zhenqin Wu},
publisher={O'Reilly Media},
note={\url{https://www.amazon.com/Deep-Learning-Life-Sciences-Microscopy/dp/1492039837}},
year={2019}
}
2.3.0