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Release 2.6.0.Dev Deepchem-Contributors deepchem Release 2.6.0.dev deepchem-contributors Sep 24, 2021 GET STARTED 1 What is DeepChem? 3 2 Quick Start 5 3 About Us 7 Index 439 i ii deepchem, Release 2.6.0.dev The DeepChem project aims to democratize deep learning for science. GET STARTED 1 deepchem, Release 2.6.0.dev 2 GET STARTED CHAPTER ONE WHAT IS DEEPCHEM? The DeepChem project aims to build high quality tools to democratize the use of deep learning in the sciences. The origin of DeepChem focused on applications of deep learning to chemistry, but the project has slowly evolved past its roots to broader applications of deep learning to the sciences. The core DeepChem Repo serves as a monorepo that organizes the DeepChem suite of scientific tools. As the project matures, smaller more focused tool will be surfaced in more targeted repos. DeepChem is primarily developed in Python, but we are experimenting with adding support for other languages. What are some of the things you can use DeepChem to do? Here’s a few examples: • Predict the solubility of small drug-like molecules • Predict binding affinity for small molecule to protein targets • Predict physical properties of simple materials • Analyze protein structures and extract useful descriptors • Count the number of cells in a microscopy image • More coming soon. We should clarify one thing up front though. DeepChem is a machine learning library, so it gives you the tools to solve each of the applications mentioned above yourself. DeepChem may or may not have prebaked models which can solve these problems out of the box. Over time, we hope to grow the set of scientific applications DeepChem can address. This means we need lots of help! If you’re a scientist who’s interested in open source, please pitch on building DeepChem. 3 deepchem, Release 2.6.0.dev 4 Chapter 1. What is DeepChem? CHAPTER TWO QUICK START The fastest way to get up and running with DeepChem is to run it on Google Colab. Check out one of the DeepChem Tutorials or this forum post for Colab quick start guides. If you’d like to install DeepChem locally, we recommend installing deepchem which is nightly version and RDKit. RDKit is a soft requirement package, but many useful methods depend on it. pip install tensorflow~=2.4 pip install --pre deepchem conda install -y -c conda-forge rdkit Then open your python and try running. import deepchem 5 deepchem, Release 2.6.0.dev 6 Chapter 2. Quick Start CHAPTER THREE ABOUT US DeepChem is managed by a team of open source contributors. Anyone is free to join and contribute! DeepChem has weekly developer calls. You can find meeting minutes on our forums. DeepChem developer calls are open to the public! To listen in, please email [email protected], where X=bharath and Y=ramsundar to introduce yourself and ask for an invite. Important: Join our community gitter to discuss DeepChem. Sign up for our forums to talk about research, development, and general questions. 3.1 Installation 3.1.1 Stable version Please install tensorflow ~2.4 before installing deepchem. pip install tensorflow~=2.4 Then, you install deepchem via pip or conda. pip install deepchem or conda install -c conda-forge deepchem RDKit is a soft requirement package, but many useful methods like molnet depend on it. We recommend installing RDKit with deepchem if you use conda. conda install -y -c conda-forge rdkit 7 deepchem, Release 2.6.0.dev 3.1.2 Nightly build version The nightly version is built by the HEAD of DeepChem. For using general utilites like Molnet, Featurisers, Datasets, etc, then, you install deepchem via pip. pip install deepchem Deepchem provides support for tensorflow, pytorch, jax and each require a individual pip Installation. For using models with tensorflow dependencies, you install using pip install --pre deepchem[tensorflow] For using models with Pytorch dependencies, you install using pip install --pre deepchem[torch] For using models with Jax dependencies, you install using pip install --pre deepchem[jax] If GPU support is required, then make sure CUDA is installed and then install the desired deep learning framework using the links below before installing deepchem 1. tensorflow - just cuda installed 2. pytorch - https://pytorch.org/get-started/locally/#start-locally 3. jax - https://github.com/google/jax#pip-installation-gpu-cuda In zsh square brackets are used for globbing/pattern matching. This means you need to escape the square brack- ets in the above installation. You can do so by including the dependencies in quotes like pip install --pre 'deepchem[jax]' 3.1.3 Google Colab The fastest way to get up and running with DeepChem is to run it on Google Colab. Check out one of the DeepChem Tutorials or this forum post for Colab quick start guides. 3.1.4 Docker If you want to install using a docker, you can pull two kinds of images from DockerHub. • deepchemio/deepchem:x.x.x – Image built by using a conda (x.x.x is a version of deepchem) – This image is built when we push x.x.x. tag – Dockerfile is put in `docker/tag`_ directory • deepchemio/deepchem:latest – Image built from source codes – This image is built every time we commit to the master branch – Dockerfile is put in `docker/nightly`_ directory First, you pull the image you want to use. 8 Chapter 3. About Us deepchem, Release 2.6.0.dev docker pull deepchemio/deepchem:latest Then, you create a container based on the image. docker run --rm -it deepchemio/deepchem:latest If you want GPU support: # If nvidia-docker is installed nvidia-docker run --rm -it deepchemio/deepchem:latest docker run --runtime nvidia --rm -it deepchemio/deepchem:latest # If nvidia-container-toolkit is installed docker run --gpus all --rm -it deepchemio/deepchem:latest You are now in a docker container which deepchem was installed. You can start playing with it in the command line. (deepchem) root@xxxxxxxxxxxxx:~/mydir# python Python3.6.10 |Anaconda, Inc.|(default, May8 2020, 02:54:21) [GCC7.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import deepchem as dc If you want to check the tox21 benchmark: # you can run our tox21 benchmark (deepchem) root@xxxxxxxxxxxxx:~/mydir# wget https://raw.githubusercontent.com/ ,!deepchem/deepchem/master/examples/benchmark.py (deepchem) root@xxxxxxxxxxxxx:~/mydir# python benchmark.py -d tox21 -m graphconv -s ,!random 3.1.5 From source with conda Installing via these steps will ensure you are installing from the source. Prerequisite • Shell: Bash, Zsh, PowerShell • Conda: >4.6 First, please clone the deepchem repository from GitHub. git clone https://github.com/deepchem/deepchem.git cd deepchem Then, execute the shell script. The shell scripts require two arguments, python version and gpu/cpu. source scripts/install_deepchem_conda.sh3.7 cpu If you want GPU support (we supports only CUDA 10.1): source scripts/install_deepchem_conda.sh3.7 gpu If you are using the Windows and the PowerShell: 3.1. Installation 9 deepchem, Release 2.6.0.dev . .\scripts\install_deepchem_conda.ps1 3.7 cpu Before activating deepchem environment, make sure conda has been initialized. Check if there is a (XXXX) in your command line. If not, use conda init <YOUR_SHELL_NAME> to activate it, then: conda activate deepchem pip install -e . pytest -m "not slow" deepchem # optional 3.1.6 From source lightweight guide Installing via these steps will ensure you are installing from the source. Prerequisite • Shell: Bash, Zsh, PowerShell • Conda: >4.6 First, please clone the deepchem repository from GitHub. git clone https://github.com/deepchem/deepchem.git cd deepchem We would advise all users to use conda environment, following below- conda create --name deepchem python=3.8 conda activate deepchem pip install -e . DeepChem provides diffrent additional packages depending on usage & contribution If one also wants to build the tensorflow environment, add this pip install -e .[tensorflow] If one also wants to build the Pytorch environment, add this pip install -e .[torch] If one also wants to build the Jax environment, add this pip install -e .[jax] DeepChem has soft requirements, which can be installed on the fly during development inside the environment but if you would a install all the soft-dependencies at once, then take a look `deepchem/requirements/<https://github.com/ deepchem/deepchem/tree/master/requirements>`___ 10 Chapter 3. About Us deepchem, Release 2.6.0.dev 3.2 Requirements 3.2.1 Hard requirements DeepChem officially supports Python 3.6 through 3.7 and requires these packages on any condition. • joblib • NumPy • pandas • scikit-learn • SciPy • TensorFlow – deepchem>=2.4.0 depends on TensorFlow v2 (2.3.x) – deepchem<2.4.0 depends on TensorFlow v1 (>=1.14) 3.2.2 Soft requirements DeepChem has a number of “soft” requirements. 3.2. Requirements 11 deepchem, Release 2.6.0.dev Package name Version Location where this package is used (dc: deepchem) BioPython latest dc.utlis.genomics_utils Deep Graph Library 0.5.x dc.feat.graph_data, dc.models. torch_models DGL-LifeSci 0.2.x dc.models.torch_models HuggingFace Trans- Not Testing dc.feat.smiles_tokenizer formers LightGBM latest dc.models.gbdt_models matminer latest dc.feat.materials_featurizers MDTraj latest dc.utils.pdbqt_utils Mol2vec latest dc.utils.molecule_featurizers Mordred latest dc.utils.molecule_featurizers NetworkX latest dc.utils.rdkit_utils OpenAI Gym Not Testing dc.rl OpenMM latest dc.utils.rdkit_utils PDBFixer latest dc.utils.rdkit_utils Pillow latest dc.data.data_loader, dc.trans. transformers PubChemPy latest dc.feat.molecule_featurizers pyGPGO latest dc.hyper.gaussian_process Pymatgen latest dc.feat.materials_featurizers PyTorch 1.6.0 dc.data.datasets PyTorch Geometric 1.6.x (with PyTorch dc.feat.graph_data dc.models.
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