[DGL-LifeSci] Release Preparation (CI, Docker, Conda build) (#1399)

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* add docs

* Fix style

* Fix lint

* Bug fix

* Fix test

* Update

* Update

* Update

* Update
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

apps/life_sci/Jenkinsfile

+#!/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

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\ No newline at end of file

apps/life_sci/README.md

@@ -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
+## Example Usage
 
-For the time being, we only support PyTorch.
+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.
 
-Depending on the features you want to use, you may need to manually install the following dependencies:
+```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)})
+"""
+```
 
-- 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).
-    
-## 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
@@ -104,26 +78,60 @@ 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
 

apps/life_sci/conda/dgllife/README.md

+# Conda Recipe
+
+Build the package with `conda build .`
\ No newline at end of file

apps/life_sci/conda/dgllife/build.sh

+git submodule init
+git submodule update --recursive
+cd python
+$PYTHON setup.py install --single-version-externally-managed --record=record.txt
\ No newline at end of file

apps/life_sci/conda/dgllife/conda_build_config.yaml

+python:
+  - 3.5
+  - 3.6
+  - 3.7
\ No newline at end of file

apps/life_sci/conda/dgllife/meta.yaml

+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

+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
\ No newline at end of file

apps/life_sci/docker/Dockerfile.ci_gpu

+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
\ No newline at end of file

apps/life_sci/docker/README.md

+# 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

+build
+
+# tutorials are auto-generated
+source/tutorials
+source/generated

apps/life_sci/docs/Makefile

+# 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

+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

+#!/bin/sh
+
+make clean
+rm -rf build
+rm -rf source/tutorials
+rm -rf source/generated

apps/life_sci/docs/source/api/data.rst

+.. _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

+.. _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

+.. _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

+.. _apimodelgnn:
+
+dgllife.model
+==================
+
+TBD by Murphy
+
+.. autofunction:: dgllife.model.load_pretrained

apps/life_sci/docs/source/api/model.zoo.rst

+.. _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

+.. _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: