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Unverified Commit 9aa5ffca authored by Chao Ma's avatar Chao Ma Committed by GitHub
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[DEMO] Update demo of distributed sampler (#564) 

* update

* update

* update demo
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examples/mxnet/sampling/README.md
View file @ 9aa5ffca
...@@ -15,44 +15,44 @@ pip install mxnet --pre ...@@ -15,44 +15,44 @@ pip install mxnet --pre
### Neighbor Sampling & Skip Connection ### Neighbor Sampling & Skip Connection
cora: test accuracy ~83% with `--num-neighbors 2`, ~84% by training on the full graph cora: test accuracy ~83% with `--num-neighbors 2`, ~84% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 citeseer: test accuracy ~69% with `--num-neighbors 2`, ~70% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 pubmed: test accuracy ~78% with `--num-neighbors 3`, ~77% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 reddit: test accuracy ~91% with `--num-neighbors 3` and `--batch-size 1000`, ~93% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 ### Control Variate & Skip Connection
cora: test accuracy ~84% with `--num-neighbors 1`, ~84% by training on the full graph cora: test accuracy ~84% with `--num-neighbors 1`, ~84% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 citeseer: test accuracy ~69% with `--num-neighbors 1`, ~70% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 pubmed: test accuracy ~79% with `--num-neighbors 1`, ~77% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 reddit: test accuracy ~93% with `--num-neighbors 1` and `--batch-size 1000`, ~93% by training on the full graph
``` ```
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 DGLBACKEND=mxnet python3 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 ### Control Variate & GraphSAGE-mean
...@@ -61,7 +61,7 @@ Following [Control Variate](https://arxiv.org/abs/1710.10568), we use the mean p ...@@ -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` 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 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 DGLBACKEND=mxnet python3 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 ### Run multi-processing training
...@@ -73,5 +73,5 @@ python3 examples/mxnet/sampling/run_store_server.py --dataset reddit --num-worke ...@@ -73,5 +73,5 @@ python3 examples/mxnet/sampling/run_store_server.py --dataset reddit --num-worke
Run four workers to train GraphSage on the reddit dataset. Run four workers to train GraphSage on the reddit dataset.
``` ```
python3 ../incubator-mxnet/tools/launch.py -n 4 -s 1 --launcher local python3 examples/mxnet/sampling/multi_process_train.py --model graphsage_cv --batch-size 1000 --test-batch-size 5000 --n-epochs 1 --graph-name reddit --num-neighbors 1 --n-hidden 128 --dropout 0.2 --weight-decay 0 python3 ../incubator-mxnet/tools/launch.py -n 4 -s 1 --launcher local python3 multi_process_train.py --model graphsage_cv --batch-size 1000 --test-batch-size 5000 --n-epochs 1 --graph-name reddit --num-neighbors 1 --n-hidden 128 --dropout 0.2 --weight-decay 0
``` ```
examples/mxnet/sampling/dis_sampling/README.md
View file @ 9aa5ffca
...@@ -13,6 +13,10 @@ ...@@ -13,6 +13,10 @@
pip install mxnet --pre pip install mxnet --pre
``` ```
### Usage
To run the following demos, you need to start trainer and sampler process on different machines by changing the `--ip`. You can also change the number of sampler by the `--num-sampler` option.
### Neighbor Sampling & Skip Connection ### Neighbor Sampling & Skip Connection
#### cora #### cora
...@@ -21,12 +25,12 @@ Test accuracy ~83% with `--num-neighbors 2`, ~84% by training on the full graph ...@@ -21,12 +25,12 @@ Test accuracy ~83% with `--num-neighbors 2`, ~84% by training on the full graph
Trainer side: Trainer side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler side: Sampler side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### citeseer #### citeseer
...@@ -35,12 +39,12 @@ Test accuracy ~69% with `--num-neighbors 2`, ~70% by training on the full graph ...@@ -35,12 +39,12 @@ Test accuracy ~69% with `--num-neighbors 2`, ~70% by training on the full graph
Trainer side: Trainer side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler side: Sampler side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### pubmed #### pubmed
...@@ -49,12 +53,12 @@ Test accuracy ~78% with `--num-neighbors 3`, ~77% by training on the full graph ...@@ -49,12 +53,12 @@ Test accuracy ~78% with `--num-neighbors 3`, ~77% by training on the full graph
Trainer side: Trainer side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000 --test-batch-size 5000 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000 --test-batch-size 5000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler side: Sampler side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### reddit #### reddit
...@@ -63,12 +67,12 @@ Test accuracy ~91% with `--num-neighbors 2` and `--batch-size 1000`, ~93% by tra ...@@ -63,12 +67,12 @@ Test accuracy ~91% with `--num-neighbors 2` and `--batch-size 1000`, ~93% by tra
Trainer side: Trainer side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_ns --dataset reddit-self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000 --n-hidden 64 --ip 127.0.0.1:2049 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_ns --dataset reddit-self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 5000 --n-hidden 64 --ip 127.0.0.1:2049 --num-sampler 1
``` ```
Sampler side: Sampler side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_ns --dataset reddit-self-loop --num-neighbors 2 --batch-size 1000 --ip 127.0.0.1:2049 DGLBACKEND=mxnet python3 sampler.py --model gcn_ns --dataset reddit-self-loop --num-neighbors 2 --batch-size 1000 --ip 127.0.0.1:2049 --num-sampler 1
``` ```
### Control Variate & Skip Connection ### Control Variate & Skip Connection
...@@ -79,12 +83,12 @@ Test accuracy ~84% with `--num-neighbors 1`, ~84% by training on the full graph ...@@ -79,12 +83,12 @@ Test accuracy ~84% with `--num-neighbors 1`, ~84% by training on the full graph
Trainer side: Trainer side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler side: Sampler side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### citeseer #### citeseer
...@@ -93,24 +97,24 @@ Test accuracy ~69% with `--num-neighbors 1`, ~70% by training on the full graph ...@@ -93,24 +97,24 @@ Test accuracy ~69% with `--num-neighbors 1`, ~70% by training on the full graph
Trainer Side: Trainer Side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler Side: Sampler Side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### pubmed #### pubmed
Trainer Side: Trainer Side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler Side: Sampler Side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### reddit #### reddit
...@@ -119,12 +123,12 @@ Test accuracy ~93% with `--num-neighbors 1` and `--batch-size 1000`, ~93% by tra ...@@ -119,12 +123,12 @@ Test accuracy ~93% with `--num-neighbors 1` and `--batch-size 1000`, ~93% by tra
Trainer Side: Trainer Side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/train.py --model gcn_cv --dataset reddit-self-loop --num-neighbors 1 --batch-size 10000 --test-batch-size 5000 --n-hidden 64 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 train.py --model gcn_cv --dataset reddit-self-loop --num-neighbors 1 --batch-size 10000 --test-batch-size 5000 --n-hidden 64 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler Side: Sampler Side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model gcn_cv --dataset reddit-self-loop --num-neighbors 1 --batch-size 10000 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model gcn_cv --dataset reddit-self-loop --num-neighbors 1 --batch-size 10000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
### Control Variate & GraphSAGE-mean ### Control Variate & GraphSAGE-mean
...@@ -137,10 +141,10 @@ Test accuracy 96.1% with `--num-neighbors 1` and `--batch-size 1000`, ~96.2% in ...@@ -137,10 +141,10 @@ Test accuracy 96.1% with `--num-neighbors 1` and `--batch-size 1000`, ~96.2% in
Trainer side: Trainer side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_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 --ip 127.0.0.1:50051 --num-sampler 1 DGLBACKEND=mxnet python3 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 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
Sampler side: Sampler side:
``` ```
DGLBACKEND=mxnet python3 examples/mxnet/sampling/dis_sampling/sampler.py --model graphsage_cv --batch-size 1000 --dataset reddit --num-neighbors 1 --ip 127.0.0.1:50051 DGLBACKEND=mxnet python3 sampler.py --model graphsage_cv --batch-size 1000 --dataset reddit --num-neighbors 1 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
examples/pytorch/sampling/dis_sampling/README.md
View file @ 9aa5ffca
...@@ -13,6 +13,10 @@ Dependencies ...@@ -13,6 +13,10 @@ Dependencies
pip install torch requests pip install torch requests
`` ``
### Usage
To run the following demos, you need to start trainer and sampler process on different machines by changing the `--ip`. You can also change the number of sampler by the `--num-sampler` option.
### Neighbor Sampling & Skip Connection ### Neighbor Sampling & Skip Connection
#### cora #### cora
...@@ -26,7 +30,7 @@ DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset cora --self-loop --num-n ...@@ -26,7 +30,7 @@ DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset cora --self-loop --num-n
Sampler side: Sampler side:
``` ```
DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### citeseer #### citeseer
...@@ -40,7 +44,7 @@ DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset citeseer --self-loop --n ...@@ -40,7 +44,7 @@ DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset citeseer --self-loop --n
Sampler side: Sampler side:
``` ```
DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### pubmed #### pubmed
...@@ -54,7 +58,7 @@ DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset pubmed --self-loop --num ...@@ -54,7 +58,7 @@ DGLBACKEND=pytorch python3 gcn_ns_sc_train.py --dataset pubmed --self-loop --num
Sampler side: Sampler side:
``` ```
DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=pytorch python3 sampler.py --model gcn_ns --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
### Control Variate & Skip Connection ### Control Variate & Skip Connection
...@@ -70,7 +74,7 @@ DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset cora --self-loop --num-n ...@@ -70,7 +74,7 @@ DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset cora --self-loop --num-n
Sampler side: Sampler side:
``` ```
DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### citeseer #### citeseer
...@@ -84,7 +88,7 @@ DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset citeseer --self-loop --n ...@@ -84,7 +88,7 @@ DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset citeseer --self-loop --n
Sampler side: Sampler side:
``` ```
DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
#### pubmed #### pubmed
...@@ -98,6 +102,5 @@ DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset pubmed --self-loop --num ...@@ -98,6 +102,5 @@ DGLBACKEND=pytorch python3 gcn_cv_sc_train.py --dataset pubmed --self-loop --num
Sampler side: Sampler side:
``` ```
DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 DGLBACKEND=pytorch python3 sampler.py --model gcn_cv --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --ip 127.0.0.1:50051 --num-sampler 1
``` ```
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