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Unverified Commit b76ac11c authored by Mufei Li's avatar Mufei Li Committed by GitHub
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[DGL-LifeSci] Release Preparation (CI, Docker, Conda build) (#1399) 



* Update

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* add docs

* Fix style

* Fix lint

* Bug fix

* Fix test

* Update

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Co-authored-by: default avatarMinjie Wang <wmjlyjemaine@gmail.com>
parent e4cc8185
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apps/life_sci/Jenkinsfile 0 → 100644
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#!/usr/bin/env groovy
// Adapted from github.com/dmlc/dgl/Jenkinsfile
app_linux_libs = "_deps/dgl*.whl"
app = "dgllife"
def init_git() {
sh "rm -rf *"
checkout scm
sh "git submodule update --recursive --init"
}
// pack libraries for later use
def pack_dgl(name, libs) {
echo "Packing ${libs} into ${name}"
stash includes: libs, name: name
}
// unpack libraries saved before
def unpack_dgl(name, libs) {
unstash name
echo "Unpacked ${libs} from ${name}"
}
def build_linux(dev) {
init_git()
sh "bash tests/scripts/build.sh ${dev}"
pack_dgl("${app}-${dev}-linux", app_linux_libs)
}
def unit_test_linux(backend, dev) {
init_git()
unpack_dgl("${app}-${dev}-linux", app_linux_libs)
timeout(time: 10, unit: 'MINUTES') {
sh "bash tests/scripts/task_unit_test.sh ${backend} ${dev}"
}
}
pipeline {
agent any
stages {
stage("Lint Check") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/dgl-ci-lint"
}
}
steps {
init_git()
sh "bash tests/scripts/task_lint.sh"
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
stage("Build") {
parallel {
stage("CPU Build") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/${app}-ci-cpu"
}
}
steps {
build_linux("cpu")
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
stage("GPU Build") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/${app}-ci-gpu"
//args "-u root"
}
}
steps {
build_linux("gpu")
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
}
}
stage("Test") {
parallel {
stage("Torch CPU") {
agent {
docker {
label "linux-cpu-node"
image "dgllib/${app}-ci-cpu"
}
}
stages {
stage("Unit test") {
steps {
unit_test_linux("pytorch", "cpu")
}
}
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
stage("Torch GPU") {
agent {
docker {
label "linux-gpu-node"
image "dgllib/${app}-ci-gpu"
args "--runtime nvidia"
}
}
stages {
stage("Unit test") {
steps {
sh "nvidia-smi"
unit_test_linux("pytorch", "gpu")
}
}
}
post {
always {
cleanWs disableDeferredWipeout: true, deleteDirs: true
}
}
}
}
}
}
}
\ No newline at end of file
apps/life_sci/LICENSE 0 → 100644
View file @ b76ac11c
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\ No newline at end of file
apps/life_sci/README.md
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......@@ -4,7 +4,7 @@
Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in
life science such as molecular graphs and biological networks, making it an import area for applying
deep learning on graphs. `dgllife` is a DGL-based package for various applications in life science
deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science
with graph neural networks.
We provide various functionalities, including but not limited to methods for graph construction,
......@@ -12,81 +12,55 @@ featurization, and evaluation, model architectures, training scripts and pre-tra
**For a full list of work implemented in DGL-LifeSci, see [here](examples/README.md).**
## Dependencies
For the time being, we only support PyTorch.
## Example Usage
Depending on the features you want to use, you may need to manually install the following dependencies:
To apply graph neural networks to molecules with DGL, we need to first construct `DGLGraph` --
the graph data structure in DGL and prepare initial node/edge features. Below gives an example of
constructing a bi-directed graph from a molecule and featurizing it with atom and bond features such
as atom type and bond type.
- RDKit 2018.09.3
- We recommend installation with `conda install -c conda-forge rdkit==2018.09.3`. For other installation recipes,
see the [official documentation](https://www.rdkit.org/docs/Install.html).
```python
from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer, CanonicalBondFeaturizer
# Node featurizer
node_featurizer = CanonicalAtomFeaturizer(atom_data_field='h')
# Edge featurizer
edge_featurizer = CanonicalBondFeaturizer(bond_data_field='h')
# SMILES (a string representation for molecule) for Penicillin
smiles = 'CC1(C(N2C(S1)C(C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C'
g = smiles_to_bigraph(smiles=smiles,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer)
print(g)
"""
DGLGraph(num_nodes=23, num_edges=50,
ndata_schemes={'h': Scheme(shape=(74,), dtype=torch.float32)}
edata_schemes={'h': Scheme(shape=(12,), dtype=torch.float32)})
"""
```
## Installation
We implement various models that users can import directly. Below gives an example of defining a GCN-based model
for molecular property prediction.
To install the package,
```python
from dgllife.model import GCNPredictor
```bash
cd python
python setup.py install
model = GCNPredictor(in_feats=1)
```
## Organization
For a full example of applying `GCNPredictor`, run the following command
```bash
python examples/property_prediction/classification.py -m GCN -d Tox21
```
dgllife
data
csv_dataset.py
...
model
gnn
model_zoo
readout
pretrain.py
utils
complex_to_graph.py
early_stop.py
eval.py
featurizers.py
mol_to_graph.py
rdkit_utils.py
splitters.py
```
### `data`
The directory consists of interfaces for working with several datasets. Additionally, one can adapt any
`.csv` dataset to dgl with `MoleculeCSVDataset` in `csv_dataset.py`.
### `model`
For more examples on molecular property prediction, generative models, protein-ligand binding affinity
prediction and reaction prediction, see `examples`.
- `gnn` implements several graph neural networks for message passing and updating node representations.
- `readout` implements several methods for computing graph representations out of node representations.
In the context of molecules, they may be viewed as learned fingerprints.
- `model_zoo` implements several models for property prediction, generative models and protein-ligand
binding affinity prediction. Many of them are based on modules in `gnn` and `readout`.
- `pretrain.py` contains APIs for loading pre-trained models.
### `utils`
- `complex_to_graph.py` contains utils for graph construction and featurization of protein-ligand complexes.
- `early_stop.py` contains utils for early stopping.
- `eval.py` contains utils for evaluating models on property prediction.
- `featurizers.py` contains utils for featurizing molecular graphs.
- `mol_to_graph.py` contains several ways for graph construction of molecules.
- `rdkit_utils.py` contains utils for RDKit, in particular loading RDKit molecule instances from different
formats, including `mol2`, `sdf`, `pdbqt`, and `pdb`.
- `splitters.py` contains several ways for splitting the dataset.
## Example Usage
Currently we provide examples for molecular property prediction, generative models and protein-ligand binding
affinity prediction. See the examples folder for details.
For some examples we also provide pre-trained models, which can be used off-shelf without training from scratch.
We also provide pre-trained models for most examples, which can be used off-shelf without training from scratch.
Below gives an example of loading a pre-trained model for `GCNPredictor` on a molecular property prediction dataset.
```python
"""Load a pre-trained model for property prediction."""
from dgllife.data import Tox21
from dgllife.model import load_pretrained
from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
......@@ -104,27 +78,61 @@ print(label_pred[:, mask != 0]) # Mask non-existing labels
# 2.0957, 0.5919, 0.7715, 1.7273, 0.2070]])
```
```python
"""Load a pre-trained model for generating molecules."""
from IPython.display import SVG
from rdkit import Chem
from rdkit.Chem import Draw
Similarly, we can load a pre-trained model for generating molecules.
```python
from dgllife.model import load_pretrained
model = load_pretrained('DGMG_ZINC_canonical')
model.eval()
mols = []
smiles = []
for i in range(4):
SMILES = model(rdkit_mol=True)
mols.append(Chem.MolFromSmiles(SMILES))
# Generating 4 molecules takes less than a second.
smiles.append(model(rdkit_mol=True))
print(smiles)
# ['CC1CCC2C(CCC3C2C(NC2=CC(Cl)=CC=C2N)S3(=O)=O)O1',
# 'O=C1SC2N=CN=C(NC(SC3=CC=CC=N3)C1=CC=CO)C=2C1=CCCC1',
# 'CC1C=CC(=CC=1)C(=O)NN=C(C)C1=CC=CC2=CC=CC=C21',
# 'CCN(CC1=CC=CC=C1F)CC1CCCN(C)C1']
```
If you are running the code block above in Jupyter notebook, you can also visualize the molecules generated with
```python
from IPython.display import SVG
from rdkit import Chem
from rdkit.Chem import Draw
mols = [Chem.MolFromSmiles(s) for s in smiles]
SVG(Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(180, 150), useSVG=True))
```
![](https://data.dgl.ai/dgllife/dgmg/dgmg_model_zoo_example2.png)
## Installation
DGL-LifeSci requires python 3.5+, DGL 0.4.3+ and PyTorch 1.2.0+.
Additionally, we require `RDKit 2018.09.3` for cheminformatics. We recommend installing it with
`conda install -c conda-forge rdkit==2018.09.3`. For other installation recipes,
see the [official documentation](https://www.rdkit.org/docs/Install.html).
We provide installation of `DGL-LifeSci` with pip. Once you have installed the package,
verify the success of installation with
```python
import dgllife
print(dgllife.__version__)
# 0.2.0
```
### Using pip
```
pip install dgllife
```
## Speed Reference
Below we provide some reference numbers to show how DGL improves the speed of training models per epoch in seconds.
......
apps/life_sci/conda/dgllife/README.md 0 → 100644
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# Conda Recipe
Build the package with `conda build .`
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apps/life_sci/conda/dgllife/build.sh 0 → 100644
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git submodule init
git submodule update --recursive
cd python
$PYTHON setup.py install --single-version-externally-managed --record=record.txt
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apps/life_sci/conda/dgllife/conda_build_config.yaml 0 → 100644
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python:
- 3.5
- 3.6
- 3.7
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apps/life_sci/conda/dgllife/meta.yaml 0 → 100644
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package:
name: dgllife{{ environ.get('APP_PACKAGE_SUFFIX', '') }}
version: "0.2.0"
source:
git_rev: 0.2.0
git_url: https://github.com/dmlc/dgl/tree/master/apps/dgllife
requirements:
build:
- python {{ python }}
- setuptools
- cmake
- git
- cython
run:
- python
- requests
- scikit-learn
- pandas
- tqdm
test:
imports:
- dgllife
about:
home: https://github.com/dmlc/dgl/tree/master/apps/dgllife
license_file: {{ environ.get('SRC_DIR') }}/LICENSE
license: Apache
apps/life_sci/docker/Dockerfile.ci_cpu 0 → 100644
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From dgllib/dgl-sagemaker-cpu:dgl_0.4_pytorch_1.2.0_rdkit
RUN pip uninstall -y scikit-learn
RUN pip install scikit-learn==0.22.2.post1
RUN pip uninstall -y pandas
RUN pip install pandas==0.24.2
RUN pip uninstall -y dgl
RUN pip install --pre dgl
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From dgllib/dgl-sagemaker-gpu:dgl_0.4_pytorch_1.2.0_rdkit
RUN pip uninstall -y scikit-learn
RUN pip install scikit-learn==0.22.2.post1
RUN pip uninstall -y pandas
RUN pip install pandas==0.24.2
RUN pip uninstall -y dgl-cu100
RUN pip install --pre dgl-cu101
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# Build Docker Image for CI
Docker images are used by the CI and release script. Make sure to install necessary requirements in it.
## To build
```bash
docker build -t dgllib/dgllife-ci-cpu:latest -f Dockerfile.ci_cpu .
```
```bash
docker build -t dgllib/dgllife-ci-gpu:latest -f Dockerfile.ci_gpu .
```
## To push
```bash
docker push dgllib/dgllife-ci-cpu:latest
```
```bash
docker push dgllib/dgllife-ci-gpu:latest
```
apps/life_sci/docs/.gitignore 0 → 100644
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build
# tutorials are auto-generated
source/tutorials
source/generated
apps/life_sci/docs/Makefile 0 → 100644
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# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SOURCEDIR = source
BUILDDIR = build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
pytorch:
@echo "##################################################################"
@echo "# #"
@echo "# Step 1: Building PyTorch tutorials #"
@echo "# #"
@echo "##################################################################"
@DGLBACKEND=pytorch $(SPHINXBUILD) -M html "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
html-noexec:
$(SPHINXBUILD) -D plot_gallery=0 -b html "$(SOURCEDIR)" "$(BUILDDIR)/html"
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
html: Makefile pytorch
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
apps/life_sci/docs/README.md 0 → 100644
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Documentation and Tutorials
===
Requirements
------------
* sphinx
* sphinx-gallery
* sphinx_rtd_theme
Build documents
---------------
First, clean up existing files:
```
./clean.sh
```
Then build:
```
make html
```
Render locally
--------------
```
cd build/html
python3 -m http.server 8000
```
apps/life_sci/docs/clean.sh 0 → 100755
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#!/bin/sh
make clean
rm -rf build
rm -rf source/tutorials
rm -rf source/generated
apps/life_sci/docs/source/api/data.rst 0 → 100644
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.. _apidata:
dgllife.data
============
TBD by Murphy
dgllife.data.alchemy
--------------------
.. automodule:: dgllife.data.alchemy
:members:
dgllife.data.csv_dataset
------------------------
.. automodule:: dgllife.data.csv_dataset
:members:
dgllife.data.pdbbind
---------------------
.. automodule:: dgllife.data.pdbbind
:members:
dgllife.data.pubchem_aromaticity
---------------------------------
.. automodule:: dgllife.data.pubchem_aromaticity
:members:
dgllife.data.tox21
---------------------------------
.. automodule:: dgllife.data.tox21
:members:
dgllife.data.uspto
---------------------------------
.. automodule:: dgllife.data.uspto
:members:
apps/life_sci/docs/source/api/model.gnn.rst 0 → 100644
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.. _apimodelgnn:
dgllife.model.gnn
==================
TBD by Murphy
dgllife.model.gnn.attentivefp
-------------------------------------------
.. automodule:: dgllife.model.gnn.attentivefp
:members:
dgllife.model.gnn.gat
----------------------------
.. automodule:: dgllife.model.gnn.gat
:members:
dgllife.model.gnn.gcn
----------------------------
.. automodule:: dgllife.model.gnn.gcn
:members:
dgllife.model.gnn.mgcn
----------------------------
.. automodule:: dgllife.model.gnn.mgcn
:members:
dgllife.model.gnn.mpnn
----------------------------
.. automodule:: dgllife.model.gnn.mpnn
:members:
dgllife.model.gnn.schnet
----------------------------
.. automodule:: dgllife.model.gnn.schnet
:members:
dgllife.model.gnn.wln
----------------------------
.. automodule:: dgllife.model.gnn.wln
:members:
apps/life_sci/docs/source/api/model.readout.rst 0 → 100644
View file @ b76ac11c
.. _apimodelreadout:
dgllife.model.readout
========================
TBD by Murphy
dgllife.model.readout.attentivefp_readout
------------------------------------------
.. automodule:: dgllife.model.readout.attentivefp_readout
:members:
dgllife.model.readout.mlp_readout
------------------------------------------
.. automodule:: dgllife.model.readout.mlp_readout
:members:
dgllife.model.readout.weighted_sum_and_max
--------------------------------------------
.. automodule:: dgllife.model.readout.weighted_sum_and_max
:members:
apps/life_sci/docs/source/api/model.rst 0 → 100644
View file @ b76ac11c
.. _apimodelgnn:
dgllife.model
==================
TBD by Murphy
.. autofunction:: dgllife.model.load_pretrained
apps/life_sci/docs/source/api/model.zoo.rst 0 → 100644
View file @ b76ac11c
.. _apimodelzoo:
dgllife.model.model_zoo
=============================
TBD by Murphy
dgllife.model.model_zoo.attentivefp_predictor
-----------------------------------------------
.. automodule:: dgllife.model.model_zoo.attentivefp_predictor
:members:
dgllife.model.model_zoo.gat_predictor
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.gat_predictor
:members:
dgllife.model.model_zoo.gcn_predictor
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.gcn_predictor
:members:
dgllife.model.model_zoo.mgcn_predictor
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.mgcn_predictor
:members:
dgllife.model.model_zoo.mlp_predictor
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.mlp_predictor
:members:
dgllife.model.model_zoo.mpnn_predictor
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.mpnn_predictor
:members:
dgllife.model.model_zoo.schnet_predictor
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.schnet_predictor
:members:
dgllife.model.model_zoo.wln_reaction_center
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.wln_reaction_center
:members:
dgllife.model.model_zoo.acnn
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.acnn
:members:
dgllife.model.model_zoo.dgmg
-------------------------------------------
.. automodule:: dgllife.model.model_zoo.dgmg
:members:
dgllife.model.model_zoo.jtnn
-------------------------------------------
.. autoclass:: dgllife.model.model_zoo.jtnn.DGLJTNNVAE
:members:
apps/life_sci/docs/source/api/utils.rst 0 → 100644
View file @ b76ac11c
.. _apiutils:
dgllife.utils
==================
TBD by Murphy
dgllife.utils.complex_to_graph
-------------------------------------------
.. automodule:: dgllife.utils.complex_to_graph
:members:
dgllife.utils.early_stop
-------------------------------------------
.. automodule:: dgllife.utils.early_stop
:members:
dgllife.utils.eval
-------------------------------------------
.. automodule:: dgllife.utils.eval
:members:
dgllife.utils.featurizers
-------------------------------------------
.. automodule:: dgllife.utils.featurizers
:members:
dgllife.utils.mol_to_graph
-------------------------------------------
.. automodule:: dgllife.utils.mol_to_graph
:members:
dgllife.utils.rdkit_utils
-------------------------------------------
.. automodule:: dgllife.utils.rdkit_utils
:members:
dgllife.utils.splitters
-------------------------------------------
.. automodule:: dgllife.utils.splitters
:members:
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