[DGL-LifeSci] Fix broken links (#1248)

* Fix

* Update

apps/life_sci/README.md

@@ -117,7 +117,7 @@ for i in range(4):
 SVG(Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(180, 150), useSVG=True))
 ```
 
-![](https://s3.us-east-2.amazonaws.com/dgl-data/model_zoo/drug_discovery/dgmg_model_zoo_example2.png)
+![](https://data.dgl.ai/dgllife/dgmg/dgmg_model_zoo_example2.png)
 
 ## Speed Reference
 

apps/life_sci/examples/generative_models/dgmg/README.md

@@ -83,7 +83,7 @@ You can also use your own dataset with additional arguments
 
 We can monitor the training process with tensorboard as below:
 
-![](https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/dgmg/tensorboard.png)
+![](https://data.dgl.ai/dgllife/dgmg/tensorboard.png)
 
 To use tensorboard, you need to install [tensorboardX](https://github.com/lanpa/tensorboardX) and 
 [TensorFlow](https://www.tensorflow.org/). You can lunch tensorboard with `tensorboard --logdir=.`
@@ -124,15 +124,13 @@ directory, with three statistics logged in `generation_stats.txt` under `eval_re
 
 We also provide a jupyter notebook where you can visualize the generated molecules 
 
-![](https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/dgmg/DGMG_ZINC_canonical_vis.png)
+![](https://data.dgl.ai/dgllife/dgmg/DGMG_ZINC_canonical_vis.png)
 
 and compare their property distributions against the training molecule property distributions
 
-![](https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/dgmg/DGMG_ZINC_canonical_dist.png)
+![](https://data.dgl.ai/dgllife/dgmg/DGMG_ZINC_canonical_dist.png)
 
-Download it with `wget https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/dgmg/eval_jupyter.ipynb` from the s3
-bucket in U.S. or `wget https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dgllife/dgmg/eval_jupyter.ipynb` from the 
-s3 bucket in China.
+You can download the notebook with `wget https://data.dgl.ai/dgllife/dgmg/eval_jupyter.ipynb`.
 
 ### Pre-trained models
 

apps/life_sci/examples/generative_models/jtnn/README.md

@@ -89,10 +89,7 @@ Below gives the statistics of pre-trained `JTNN_ZINC` model.
 ### Visualization
 
 Here we draw some "neighbor" of a given molecule, by adding noises on the intermediate representations. 
-You can download the script with `https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/jtnn_viz_neighbor_mol.ipynb` from the s3
-bucket in U.S. or `https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dgllife/jtnn_viz_neighbor_mol.ipynb` from the s3 bucket 
-in mainland China. 
-
+You can download the script with `wget https://data.dgl.ai/dgllife/jtnn_viz_neighbor_mol.ipynb`. 
 Please put this script at the current directory (`examples/pytorch/model_zoo/chem/generative_models/jtnn/`).
 
 #### Given Molecule

apps/life_sci/examples/property_prediction/README.md

@@ -113,12 +113,9 @@ Note that the dataset is randomly split so this number is only for reference.
 
 [8] visualizes the weights of atoms in readout for possible interpretations like the figure below. 
 We provide a jupyter notebook for performing the visualization and you can download it with 
-`wget https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/attentive_fp/atom_weight_visualization.ipynb` 
-from the s3 bucket in U.S. or 
-`wget https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dgllife/attentive_fp/atom_weight_visualization.ipynb` 
-from the s3 bucket in China.
+`wget https://data.dgl.ai/dgllife/attentive_fp/atom_weight_visualization.ipynb`.
 
-![](https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/attentive_fp_vis_example.png)
+![](https://data.dgl.ai/dgllife/attentive_fp_vis_example.png)
 
 ## Dataset Customization
 

apps/life_sci/implemented.md

@@ -3,43 +3,43 @@
 ## Datasets/Benchmarks
 
 - MoleculeNet: A Benchmark for Molecular Machine Learning [[paper]](https://arxiv.org/abs/1703.00564), [[website]](http://moleculenet.ai/)
-    - [Tox21 with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/data/tox21.py)
-    - [PDBBind with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/data/pdbbind.py)
+    - [Tox21 with DGL](dgllife/data/tox21.py)
+    - [PDBBind with DGL](dgllife/data/pdbbind.py)
 - Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models [[paper]](https://arxiv.org/abs/1906.09427), [[github]](https://github.com/tencent-alchemy/Alchemy)
-    - [Alchemy with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/data/alchemy.py)
+    - [Alchemy with DGL](dgllife/data/alchemy.py)
 
 ## Property Prediction
 
 - Semi-Supervised Classification with Graph Convolutional Networks (GCN) [[paper]](https://arxiv.org/abs/1609.02907), [[github]](https://github.com/tkipf/gcn)
-    - [GCN-Based Predictor with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/gcn_predictor.py)
-    - [Example for Molecule Classification](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/property_prediction/classification.py)
+    - [GCN-Based Predictor with DGL](dgllife/model/model_zoo/gcn_predictor.py)
+    - [Example for Molecule Classification](examples/property_prediction/classification.py)
 - Graph Attention Networks (GAT) [[paper]](https://arxiv.org/abs/1710.10903), [[github]](https://github.com/PetarV-/GAT)
-    - [GAT-Based Predictor with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/gat_predictor.py)
-    - [Example for Molecule Classification](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/property_prediction/classification.py)
+    - [GAT-Based Predictor with DGL](dgllife/model/model_zoo/gat_predictor.py)
+    - [Example for Molecule Classification](examples/property_prediction/classification.py)
 - SchNet: A continuous-filter convolutional neural network for modeling quantum interactions [[paper]](https://arxiv.org/abs/1706.08566), [[github]](https://github.com/atomistic-machine-learning/SchNet)
-    - [SchNet with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/schnet_predictor.py)
-    - [Example for Molecule Regression](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/property_prediction/regression.py)
+    - [SchNet with DGL](dgllife/model/model_zoo/schnet_predictor.py)
+    - [Example for Molecule Regression](examples/property_prediction/regression.py)
 - Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective (MGCN) [[paper]](https://arxiv.org/abs/1906.11081)
-    - [MGCN with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/mgcn_predictor.py)
-    - [Example for Molecule Regression](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/property_prediction/regression.py)
+    - [MGCN with DGL](dgllife/model/model_zoo/mgcn_predictor.py)
+    - [Example for Molecule Regression](examples/property_prediction/regression.py)
 - Neural Message Passing for Quantum Chemistry (MPNN) [[paper]](https://arxiv.org/abs/1704.01212), [[github]](https://github.com/brain-research/mpnn)
-    - [MPNN with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/mpnn_predictor.py)
-    - [Example for Molecule Regression](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/property_prediction/regression.py)
+    - [MPNN with DGL](dgllife/model/model_zoo/mpnn_predictor.py)
+    - [Example for Molecule Regression](examples/property_prediction/regression.py)
 - Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism (AttentiveFP) [[paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.9b00959)
-    - [AttentiveFP with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/attentivefp_predictor.py)
-    - [Example for Molecule Regression](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/property_prediction/regression.py)
+    - [AttentiveFP with DGL](dgllife/model/model_zoo/attentivefp_predictor.py)
+    - [Example for Molecule Regression](examples/property_prediction/regression.py)
 
 ## Generative Models
 
 - Learning Deep Generative Models of Graphs (DGMG) [[paper]](https://arxiv.org/abs/1803.03324)
-    - [DGMG with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/dgmg.py)
-    - [Example Training Script](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/generative_models/dgmg)
+    - [DGMG with DGL](dgllife/model/model_zoo/dgmg.py)
+    - [Example Training Script](examples/generative_models/dgmg)
 - Junction Tree Variational Autoencoder for Molecular Graph Generation (JTNN) [[paper]](https://arxiv.org/abs/1802.04364)
-    - [JTNN with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/jtnn)
-    - [Example Training Script](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/generative_models/jtnn)
+    - [JTNN with DGL](dgllife/model/model_zoo/jtnn)
+    - [Example Training Script](examples/generative_models/jtnn)
 
 ## Binding Affinity Prediction
 
 - Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity (ACNN) [[paper]](https://arxiv.org/abs/1703.10603), [[github]](https://github.com/deepchem/deepchem/tree/master/contrib/atomicconv)
-    - [ACNN with DGL](https://github.com/dmlc/dgl/tree/master/apps/life_sci/dglls/model/model_zoo/acnn.py)
-    - [Example Training Script](https://github.com/dmlc/dgl/tree/master/apps/life_sci/examples/binding_affinity_prediction)
+    - [ACNN with DGL](dgllife/model/model_zoo/acnn.py)
+    - [Example Training Script](examples/binding_affinity_prediction)

examples/pytorch/jtnn/jtnn/datautils.py

@@ -27,7 +27,7 @@ class JTNNDataset(Dataset):
         self.dir = get_download_dir()
         self.zip_file_path='{}/jtnn.zip'.format(self.dir)
 
-        download(_get_dgl_url('dglls/jtnn.zip'), path=self.zip_file_path)
+        download(_get_dgl_url('dgllife/jtnn.zip'), path=self.zip_file_path)
         extract_archive(self.zip_file_path, '{}/jtnn'.format(self.dir))
         print('Loading data...')
         data_file = '{}/jtnn/{}.txt'.format(self.dir, data)

examples/pytorch/model_zoo/chem/README.md

@@ -129,7 +129,7 @@ for i in range(4):
 SVG(Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(180, 150), useSVG=True))
 ```
 
-![](https://s3.us-east-2.amazonaws.com/dgl.ai/model_zoo/drug_discovery/dgmg_model_zoo_example2.png)
+![](https://data.dgl.ai/dgllife/dgmg/dgmg_model_zoo_example2.png)
 
 ## Binding affinity prediction
 

examples/pytorch/model_zoo/chem/generative_models/dgmg/README.md

@@ -83,7 +83,7 @@ You can also use your own dataset with additional arguments
 
 We can monitor the training process with tensorboard as below:
 
-![](https://s3.us-east-2.amazonaws.com/dgl.ai/model_zoo/drug_discovery/dgmg/tensorboard.png)
+![](https://data.dgl.ai/dgllife/dgmg/tensorboard.png)
 
 To use tensorboard, you need to install [tensorboardX](https://github.com/lanpa/tensorboardX) and 
 [TensorFlow](https://www.tensorflow.org/). You can lunch tensorboard with `tensorboard --logdir=.`
@@ -130,9 +130,7 @@ and compare their property distributions against the training molecule property
 
 ![](https://s3.us-east-2.amazonaws.com/dgl.ai/model_zoo/drug_discovery/dgmg/DGMG_ZINC_canonical_dist.png)
 
-Download it with `wget https://s3.us-west-2.amazonaws.com/dgl-data/dglls/dgmg/eval_jupyter.ipynb` from the s3
-bucket in U.S. or `wget https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dglls/dgmg/eval_jupyter.ipynb` from the 
-s3 bucket in China.
+You can download the notebook with `wget https://data.dgl.ai/dgllife/dgmg/eval_jupyter.ipynb`.
 
 ### Pre-trained models
 

examples/pytorch/model_zoo/chem/generative_models/jtnn/README.md

@@ -88,7 +88,9 @@ Below gives the statistics of pre-trained `JTNN_ZINC` model.
 
 ### Visualization
 
-Here we draw some "neighbor" of a given molecule, by adding noises on the intermediate representations. Detailed script can be found [here](https://s3.us-east-2.amazonaws.com/dgl.ai/model_zoo/drug_discovery/jtnn/viz_neighbor_mol.ipynb). Please put this script at the current directory (`examples/pytorch/model_zoo/chem/generative_models/jtnn/`).
+Here we draw some "neighbor" of a given molecule, by adding noises on the intermediate representations. 
+You can download the script with `wget https://data.dgl.ai/dgllife/jtnn_viz_neighbor_mol.ipynb`. 
+Please put this script at the current directory (`examples/pytorch/model_zoo/chem/generative_models/jtnn/`).
 
 #### Given Molecule
 ![image](https://user-images.githubusercontent.com/8686776/63773593-0d37da00-c90e-11e9-8933-0abca4b430db.png)

examples/pytorch/model_zoo/chem/generative_models/jtnn/jtnn/datautils.py

@@ -32,7 +32,7 @@ class JTNNDataset(Dataset):
     def __init__(self, data, vocab, training=True):
         self.dir = get_download_dir()
         self.zip_file_path='{}/jtnn.zip'.format(self.dir)
-        download(_get_dgl_url('dglls/jtnn.zip'), path=self.zip_file_path)
+        download(_get_dgl_url('dgllife/jtnn.zip'), path=self.zip_file_path)
         extract_archive(self.zip_file_path, '{}/jtnn'.format(self.dir))
         print('Loading data...')
         if data in ['train', 'test']:

examples/pytorch/model_zoo/chem/property_prediction/README.md

@@ -113,7 +113,7 @@ on the training and validation set for reference.
 We provide a jupyter notebook for performing the visualization and you can download it with 
 `wget https://s3.us-east-2.amazonaws.com/dgl.ai/model_zoo/drug_discovery/AttentiveFP/atom_weight_visualization.ipynb`.
 
-![](https://s3.us-east-2.amazonaws.com/dgl.ai/model_zoo/drug_discovery/AttentiveFP/vis_example.png)
+![](https://s3.us-west-2.amazonaws.com/dgl-data/dgllife/attentive_fp_vis_example.png)
 
 ## Dataset Customization