all demo use python-3 (dmlc#555)

examples/mxnet/gat/README.md

@@ -19,5 +19,5 @@ pip install requests
 
 ### Usage (make sure that DGLBACKEND is changed into mxnet)
 ```bash
-DGLBACKEND=mxnet python gat_batch.py --dataset cora --gpu 0 --num-heads 8
+DGLBACKEND=mxnet python3 gat_batch.py --dataset cora --gpu 0 --num-heads 8
 ```

examples/mxnet/rgcn/README.md

@@ -22,15 +22,15 @@ Example code was tested with rdflib 4.2.2 and pandas 0.23.4
 ### Entity Classification
 AIFB: accuracy 97.22% (DGL), 95.83% (paper)
 ```
-DGLBACKEND=mxnet python entity_classify.py -d aifb --testing --gpu 0
+DGLBACKEND=mxnet python3 entity_classify.py -d aifb --testing --gpu 0
 ```
 
 MUTAG: accuracy 76.47% (DGL), 73.23% (paper)
 ```
-DGLBACKEND=mxnet python entity_classify.py -d mutag --l2norm 5e-4 --n-bases 40 --testing --gpu 0
+DGLBACKEND=mxnet python3 entity_classify.py -d mutag --l2norm 5e-4 --n-bases 40 --testing --gpu 0
 ```
 
 BGS: accuracy 79.31% (DGL, n-basese=20, OOM when >20), 83.10% (paper)
 ```
-DGLBACKEND=mxnet python entity_classify.py -d bgs --l2norm 5e-4 --n-bases 20 --testing --gpu 0 --relabel
+DGLBACKEND=mxnet python3 entity_classify.py -d bgs --l2norm 5e-4 --n-bases 20 --testing --gpu 0 --relabel
 ```

examples/mxnet/sampling/README.md

@@ -15,44 +15,44 @@ pip install mxnet --pre
 ### Neighbor Sampling & Skip Connection
 cora: test accuracy ~83% with `--num-neighbors 2`, ~84% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000
 ```
 
 citeseer: test accuracy ~69% with `--num-neighbors 2`, ~70% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000
 ```
 
 pubmed: test accuracy ~78% with `--num-neighbors 3`, ~77% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000 --test-batch-size 5000
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000 --test-batch-size 5000
 ```
 
 reddit: test accuracy ~91% with `--num-neighbors 3` and `--batch-size 1000`, ~93% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_ns --dataset reddit-self-loop --num-neighbors 3 --batch-size 1000 --test-batch-size 5000 --n-hidden 64
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_ns --dataset reddit-self-loop --num-neighbors 3 --batch-size 1000 --test-batch-size 5000 --n-hidden 64
 ```
 
 
 ### Control Variate & Skip Connection
 cora: test accuracy ~84% with `--num-neighbors 1`, ~84% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000
 ```
 
 citeseer: test accuracy ~69% with `--num-neighbors 1`, ~70% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000
 ```
 
 pubmed: test accuracy ~79% with `--num-neighbors 1`, ~77% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000
 ```
 
 reddit: test accuracy ~93% with `--num-neighbors 1` and `--batch-size 1000`, ~93% by training on the full graph
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model gcn_cv --dataset reddit-self-loop --num-neighbors 1 --batch-size 10000 --test-batch-size 5000 --n-hidden 64
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model gcn_cv --dataset reddit-self-loop --num-neighbors 1 --batch-size 10000 --test-batch-size 5000 --n-hidden 64
 ```
 
 ### Control Variate & GraphSAGE-mean
@@ -61,7 +61,7 @@ Following [Control Variate](https://arxiv.org/abs/1710.10568), we use the mean p
 
 reddit: test accuracy 96.1% with `--num-neighbors 1` and `--batch-size 1000`, ~96.2% in [Control Variate](https://arxiv.org/abs/1710.10568) with `--num-neighbors 2` and `--batch-size 1000`
 ```
-DGLBACKEND=mxnet python examples/mxnet/sampling/train.py --model graphsage_cv --batch-size 1000 --test-batch-size 5000 --n-epochs 50 --dataset reddit --num-neighbors 1 --n-hidden 128 --dropout 0.2 --weight-decay 0
+DGLBACKEND=mxnet python3 examples/mxnet/sampling/train.py --model graphsage_cv --batch-size 1000 --test-batch-size 5000 --n-epochs 50 --dataset reddit --num-neighbors 1 --n-hidden 128 --dropout 0.2 --weight-decay 0
 ```
 
 ### Run multi-processing training

examples/mxnet/tree_lstm/README.md

@@ -21,7 +21,7 @@ The script will download the [SST dataset] (http://nlp.stanford.edu/sentiment/in
 
 ## Usage
 ```
-python train.py --gpu 0
+python3 train.py --gpu 0
 ```
 
 ## Speed Test

examples/pytorch/appnp/README.md

@@ -22,7 +22,7 @@ Results
 
 Run with following (available dataset: "cora", "citeseer", "pubmed")
 ```bash
-python train.py --dataset cora --gpu 0
+python3 train.py --dataset cora --gpu 0
 ```
 
 * cora: 0.8370 (paper: 0.850)

examples/pytorch/capsule/README.md

@@ -17,7 +17,7 @@ Training & Evaluation
 ----------------------
 ```bash
 # Run with default config
-python main.py
+python3 main.py
 # Run with train and test batch size 128, and for 50 epochs
-python main.py --batch-size 128 --test-batch-size 128 --epochs 50
+python3 main.py --batch-size 128 --test-batch-size 128 --epochs 50
 ```

examples/pytorch/dgi/README.md

@@ -20,15 +20,15 @@ How to run
 Run with following:
 
 ```bash
-python train.py --dataset=cora --gpu=0 --self-loop
+python3 train.py --dataset=cora --gpu=0 --self-loop
 ```
 
 ```bash
-python train.py --dataset=citeseer --gpu=0
+python3 train.py --dataset=citeseer --gpu=0
 ```
 
 ```bash
-python train.py --dataset=pubmed --gpu=0
+python3 train.py --dataset=pubmed --gpu=0
 ```
 
 Results

examples/pytorch/dgmg/README.md

@@ -10,8 +10,8 @@ Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia.
 
 ## Usage
 
-- Train with batch size 1: `python main.py`
-- Train with batch size larger than 1: `python main_batch.py`.
+- Train with batch size 1: `python3 main.py`
+- Train with batch size larger than 1: `python3 main_batch.py`.
 
 ## Performance
 

examples/pytorch/gat/README.md

@@ -23,19 +23,19 @@ How to run
 Run with following:
 
 ```bash
-python train.py --dataset=cora --gpu=0
+python3 train.py --dataset=cora --gpu=0
 ```
 
 ```bash
-python train.py --dataset=citeseer --gpu=0
+python3 train.py --dataset=citeseer --gpu=0
 ```
 
 ```bash
-python train.py --dataset=pubmed --gpu=0 --num-out-heads=8 --weight-decay=0.001
+python3 train.py --dataset=pubmed --gpu=0 --num-out-heads=8 --weight-decay=0.001
 ```
 
 ```bash
-python train_ppi.py --gpu=0
+python3 train_ppi.py --gpu=0
 ```
 
 Results

examples/pytorch/gcn/README.md

@@ -28,7 +28,7 @@ Results
 
 Run with following (available dataset: "cora", "citeseer", "pubmed")
 ```bash
-python train.py --dataset cora --gpu 0 --self-loop
+python3 train.py --dataset cora --gpu 0 --self-loop
 ```
 
 * cora: ~0.810 (0.79-0.83) (paper: 0.815)

examples/pytorch/gin/README.md

@@ -20,12 +20,12 @@ How to run
 An experiment on the GIN in default settings can be run with
 
 ```bash
-python main.py
+python3 main.py
 ```
 
 An experiment on the GIN in customized settings can be run with
 ```bash
-python main.py [--device 0 | --disable-cuda] --dataset COLLAB \
+python3 main.py [--device 0 | --disable-cuda] --dataset COLLAB \
                --graph_pooling_type max --neighbor_pooling_type sum
 ```
 
@@ -35,7 +35,7 @@ Results
 Run with following with the double SUM pooling way:
 (tested dataset: "MUTAG"(default), "COLLAB", "IMDBBINARY", "IMDBMULTI")
 ```bash
-python train.py --dataset MUTAB --device 0  \
+python3 train.py --dataset MUTAB --device 0  \
                 --graph_pooling_type sum --neighbor_pooling_type sum
 ```
 

examples/pytorch/graphsage/README.md

@@ -19,7 +19,7 @@ Results
 
 Run with following (available dataset: "cora", "citeseer", "pubmed")
 ```bash
-python graphsage.py --dataset cora --gpu 0
+python3 graphsage.py --dataset cora --gpu 0
 ```
 
 * cora: ~0.8470 

examples/pytorch/line_graph/README.md

@@ -22,12 +22,12 @@ How to run
 An experiment on the Stochastic Block Model in default settings can be run with
 
 ```bash
-python train.py
+python3 train.py
 ```
 
 An experiment on the Stochastic Block Model in customized settings can be run with
 ```bash
-python train.py --batch-size BATCH_SIZE --gpu GPU --n-communities N_COMMUNITIES \
+python3 train.py --batch-size BATCH_SIZE --gpu GPU --n-communities N_COMMUNITIES \
                 --n-features N_FEATURES --n-graphs N_GRAPH --n-iterations N_ITERATIONS \
                 --n-layers N_LAYER --n-nodes N_NODE --model-path MODEL_PATH --radius RADIUS
 ```

examples/pytorch/sampling/README.md

@@ -16,32 +16,32 @@ pip install torch requests
 ### Neighbor Sampling & Skip Connection
 cora: test accuracy ~83% with --num-neighbors 2, ~84% by training on the full graph
 ```
-python gcn_ns_sc.py --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+python3 gcn_ns_sc.py --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
 ```
 
 citeseer: test accuracy ~69% with --num-neighbors 2, ~70% by training on the full graph
 ```
-python gcn_ns_sc.py --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+python3 gcn_ns_sc.py --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
 ```
 
 pubmed: test accuracy ~76% with --num-neighbors 3, ~77% by training on the full graph
 ```
-python gcn_ns_sc.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+python3 gcn_ns_sc.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
 ```
 
 ### Control Variate & Skip Connection
 cora: test accuracy ~84% with --num-neighbors 1, ~84% by training on the full graph
 ```
-python gcn_cv_sc.py --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+python3 gcn_cv_sc.py --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
 ```
 
 citeseer: test accuracy ~69% with --num-neighbors 1, ~70% by training on the full graph
 ```
-python gcn_cv_sc.py --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+python3 gcn_cv_sc.py --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
 ```
 
 pubmed: test accuracy ~77% with --num-neighbors 1, ~77% by training on the full graph
 ```
-python gcn_cv_sc.py --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+python3 gcn_cv_sc.py --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
 ```
 

examples/pytorch/sgc/README.md

@@ -22,9 +22,9 @@ Results
 
 Run with following (available dataset: "cora", "citeseer", "pubmed")
 ```bash
-python sgc.py --dataset cora --gpu 0
-python sgc.py --dataset citeseer --weight-decay 5e-5 --n-epochs 150 --bias --gpu 0
-python sgc.py --dataset pubmed --weight-decay 5e-5 --bias --gpu 0
+python3 sgc.py --dataset cora --gpu 0
+python3 sgc.py --dataset citeseer --weight-decay 5e-5 --n-epochs 150 --bias --gpu 0
+python3 sgc.py --dataset pubmed --weight-decay 5e-5 --bias --gpu 0
 ```
 
 On NVIDIA V100

examples/pytorch/transformer/README.md

@@ -15,13 +15,13 @@ The folder contains training module and inferencing module (beam decoder) for Tr
 - For training:
 
     ```
-    python translation_train.py [--gpus id1,id2,...] [--N #layers] [--dataset DATASET] [--batch BATCHSIZE] [--universal]
+    python3 translation_train.py [--gpus id1,id2,...] [--N #layers] [--dataset DATASET] [--batch BATCHSIZE] [--universal]
     ```
 
 - For evaluating BLEU score on test set(by enabling `--print` to see translated text):
 
     ```
-    python translation_test.py [--gpu id] [--N #layers] [--dataset DATASET] [--batch BATCHSIZE] [--checkpoint CHECKPOINT] [--print] [--universal]
+    python3 translation_test.py [--gpu id] [--N #layers] [--dataset DATASET] [--batch BATCHSIZE] [--checkpoint CHECKPOINT] [--print] [--universal]
     ```
 
 Available datasets: `copy`, `sort`, `wmt14`, `multi30k`(default).

examples/pytorch/tree_lstm/README.md

@@ -24,7 +24,7 @@ pip install torch requests nltk
 
 ## Usage
 ```
-python train.py --gpu 0
+python3 train.py --gpu 0
 ```
 
 ## Speed