GNN Algorithm Instructions

Pytorch on Angel provides the ability to run graph convolution network algorithm. We follow Pytorch-Geometric to define the graph convolution networks while using the parameter server of Angel to store the network structure and features of nodes.

Introduction

1. How to predict

change the input data path and output path
change actionType to predict
you can get hdfs://modelPath/xx.pt to local, then use it as training; Or you can use the hdfs path, and set --files hdfs://modelPath/xx.pt, in this way the torchModelPath can be remove

2. How to train incrementally

change the input data path and output path, or you can use the same data to train incrementally
set actionType as train
you can get hdfs://modelPath/xx.pt to local, then use it as training; Or you can use the hdfs path, and set --files hdfs://modelPath/xx.pt, in this way the torchModelPath can be remove

3. How to calculate the resource

In order to know how to set the resources, you should figure out where the data saved firstly, and then calculate how much data storage space, finally set 2~3 times of data storage. The detail calculation method refer to Resource Calculation Method

Example of GraphSage

GraphSage generates embeddings by sampling and aggregating features from a node’s local neighborhood. Here we give an example of how to run GraphSage algorithm beyond Pytorch on Angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
dense/low-sparse data:
```
python graphsage.py --input_dim 1433 --hidden_dim 128 --output_dim 7 --output_file graphsage_cora.pt
```
high-sparse data:
```
python graphsage.py --input_dim 32 --input_embedding_dim 8 --input_field_num 20 --encode one-hot --hidden_dim 128 --output_dim 7 --output_file graphsage_sparse.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of graphsage. After that, you will obtain a model file named "graphsage_cora.pt". Here we use the Cora dataset as an example, where the feature dimension for each node is 1433 with 7 different classes.
Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for graphsage, including the edge table, the node feature table and the node label table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

dense/low-sparse data:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "graphsage-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files graphsage_cora.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.GraphSageExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath\
      torchModelPath:graphsage_cora.pt featureDim:1433 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5\
      numPartitions:50 format:sparse samples:10 batchSize:128\
      predictOutputPath:$predictOutputPath embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

high-sparse data:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "graphsage-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files graphsage_sparse.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.GraphSageExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath\
      torchModelPath:graphsage_sparse.pt featureDim:32 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5 fieldNum:20 featEmbedDim:8 \
      numPartitions:50 format:sparse samples:10 batchSize:128\
      predictOutputPath:$predictOutputPath embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, graphsage_cora.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of DGI/Unsupervised GraphSage

Here we give an example of how to run DGI algorithm beyond Pytorch on Angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
for DGI pt model:
```
python dgi.py --input_dim 1433 --hidden_dim 128 --output_dim 128 --output_file dgi_cora.pt
```
for Unsupervised GraphSage pt model:
```
python unsupervised_graphsage.py --input_dim 1433 --hidden_dim 128 --output_dim 128 --output_file unsupervised_graphsage_cora.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of dgi. After that, you will obtain a model file named "dgi_cora.pt". Here we use the Cora dataset as an example, where the feature dimension for each node is 1433.
Detailed parameter introduction reference Python Model Parameters
Preparing input data There are two inputs required for dgi, including the edge table and the node feature table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel. The only difference between DGI and Unsupervised GraphSage is pt model, the submit scriptis same；

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "dgi-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files dgi_cora.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.unsupervised.cluster.DGIExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath\
      torchModelPath:dgi_cora.pt featureDim:1433 stepSize:0.01\
      optimizer:adam numEpoch:10 \
      numPartitions:50 format:sparse samples:10 batchSize:128\
      embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, dgi_cora.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of Relation GCN (RGCN)

Relation GCN is semi-supervised graph convolution network which can utilize the types of edges. The difference between RGCN and GCN is that each edge can has different types.

Here we give an example of using RGCN over pytorch on angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python rgcn.py --input_dim 32 --hidden_dim 16 --n_class 2 --output_file rgcn_mutag.pt --n_relations 46 --n_bases 30
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of rgcn. After that, you will obtain a model file named "rgcn_mutag.pt". Where n_class is the number of classes, n_relations is the number of types for edges and n_bases is a parameter of RGCN to avoid overfitting. Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for graphsage, including the edge table with type, the node feature table and the node label table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "rgcn-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files rgcn_mutag.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.RGCNExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath\
      torchModelPath:rgcn_mutag.pt featureDim:32 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5\
      numPartitions:50 format:sparse samples:10 batchSize:128\
      predictOutputPath:$predictOutputPath embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

edgePath: the input path (hdfs) of edge table, which contains src, dst and type

Notes:

The model file, rgcn_mutag.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of EdgeProp

EdgeProp is an end-to-end Graph Convolution Network (GCN)-based algorithm to learn the embeddings of the nodes and edges of a large-scale time-evolving graph. It consider not only node information and also edge side information.

Here we give an example of using EdgeProp over pytorch on angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python edgeprop.py --input_dim 23 --edge_input_dim 7 --hidden_dim 128 --output_dim 7 --output_file edgeprop_eth.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of edgeProp. After that, you will obtain a model file named "edgeprop_eth.pt". Where edge_input_dim is the dimension of edge feature, other parameters are same as GraphSAGE. Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for graphsage, including the edge table with edge feature, the node feature table and the node label table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "edgeprop-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files edgeprop_eth.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.EdgePropGCNExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath\
      torchModelPath:edgeprop_eth.pt featureDim:23 edgeFeatureDim:7 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5\
      numPartitions:50 format:sparse samples:10 batchSize:128\
      predictOutputPath:$predictOutputPath embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script.

edgePath: the input path (hdfs) of edge table, which contains src, dst and edge feature

Notes:

The model file, rgcn_mutag.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of GAT

Here we give an example of how to run GAT algorithm beyond Pytorch on Angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python gat.py --input_dim 32 --hidden_dim 128 --output_dim 11 --output_file gat_am.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of GAT. After that, you will obtain a model file named "gat_am.pt". Here we use the am dataset as an example, where the feature dimension for each node is 32 with 11 different classes. Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for graphsage, including the edge table, the node feature table and the node label table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "gat-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files gat_am.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.GATExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath\
      torchModelPath:gat_am.pt featureDim:32 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5\
      numPartitions:50 format:sparse samples:10 batchSize:128\
      predictOutputPath:$predictOutputPath embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, gat_am.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of HAN

HAN is a semi-supervised graph convolution network for heterogeneous graph. In order to capture the heterogeneous information, HAN defined two different attentions: node-level and semantic level. Here a simplified version of HAN is implemented, which accepts bipartite graph in the form of "user-item", where item nodes could have multiple types. In another words, the input graph has multiple meta-paths in the form of "user-item-user". HAN classifies user nodes, and outputs their embeddings if needed.

Here we give an example of using HAN over pytorch on angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
dense/low-sparse data:
```
python semi_han.py --m 64 --input_dim 32 --hidden_dim 16 --output_dim 2 --item_types 5  --output_file han.pt
```
high-sparse data:
```
python semi_han.py --m 64 --input_dim 32 --input_embedding_dim 8 --input_field_num 20 --encode one-hot --hidden_dim 16 --output_dim 2 --item_types 5 --output_file han_sparse.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of han. After that, you will obtain a model file named "han.pt". Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for han, including the edge table with type, the node feature table and the node label table.

HAN requires an edge file which contains three columns including the source node column, the destination column and the node type column. The third column indicates the destination nodes' types, each type indicates a meta-path of "A-B-A".

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel. dense /low-sparse data:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "han-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files han.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.HANExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath\
      torchModelPath:han.pt featureDim:32 temTypes:5 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5\
      numPartitions:50 format:sparse samples:10 batchSize:128\
      predictOutputPath:$predictOutputPath embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

high-sparse data:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "han-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files han.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.HANExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath\
      torchModelPath:han.pt featureDim:32 temTypes:5 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5 fieldNum:20 featEmbedDim:8 \
      numPartitions:50 format:sparse samples:10 batchSize:128\
      predictOutputPath:$predictOutputPath embeddingPath:$embeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

edgePath: the input path (hdfs) of edge table, which contains src, dst and type

Notes:

The model file, rgcn_mutag.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of Semi Bipartite GraphSage

Semi Bipartite GraphSage is a semi-supervised graph convolution network for Bipartite graph.

Here we give an example of using Semi Bipartite GraphSage over pytorch on angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
dense/low-sparse data:

python semi_bipartite_graphsage.py --input_user_dim 2 --input_item_dim 19 --hidden_dim 128 --output_dim 2 --output_file semi_bipartite_graphsage.pt --task_type classification

high-sparse data:

python semi_bipartite_graphsage.py --input_user_dim 10 --input_item_dim 10 --hidden_dim 128 --output_dim 2 --output_file semi_bipartite_graphsage_sparse.pt --task_type classification --input_user_field_num 3 --input_item_field_num 3 --input_user_embedding_dim 8 --input_item_embedding_dim 16

This script utilizes TorchScript to generate a model file which contains the dataflow graph of Semi Bipartite GraphSage. After that, you will obtain a model file named "semi_bipartite_graphsage.pt". Detailed parameter introduction reference Python Model Parameters

Preparing input data There are three inputs required for han, including the edge table, the user node feature table, the item node feature table and the label table for user node.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.
dense/low-sparse data:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "semi_bipartite_graphsage-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files semi_bipartite_graphsage.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.BiGCNExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath userFeaturePath:$featurePath labelPath:$labelPath\
      torchModelPath:semi_bipartite_graphsage.pt userFeatureDim:2 itemFeatureDim:19 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5\
      numPartitions:50 format:sparse userNumSamples:10 itemNumSamples:10 batchSize:128\
      predictOutputPath:$predictOutputPath userEmbeddingPath:$userEmbeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

high-sparse data:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "semi_bipartite_graphsage-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files semi_bipartite_graphsage_sparse.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.BiGCNExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath userFeaturePath:$featurePath labelPath:$labelPath\
      torchModelPath:semi_bipartite_graphsage_sparse.pt userFeatureDim:10 itemFeatureDim:10 stepSize:0.01\
      optimizer:adam numEpoch:10 testRatio:0.5 userFieldNum:3 itemFieldNum:3 userFeatEmbedDim:8 itemFeatEmbedDim:16\
      numPartitions:50 format:sparse userNumSamples:10 itemNumSamples:10 batchSize:128\
      predictOutputPath:$predictOutputPath userEmbeddingPath:$userEmbeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, semi_bipartite_graphsage.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of Unsupervised Bipartite GraphSage

Unsupervised Bipartite GraphSage is a unsupervised graph convolution network for Bipartite graph.

Here we give an example of using Unsupervised Bipartite GraphSage over pytorch on angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python unsupervised_bipartite_graphsage.py --input_user_dim 2 --input_item_dim 19 --hidden_dim 128 --output_dim 128 --output_file un_bipartite_graphsage.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of Unsupervised Bipartite GraphSage. After that, you will obtain a model file named "un_bipartite_graphsage.pt". Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for Unsupervised Bipartite GraphSage, including the edge table, the user node feature table，and item node feature table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "semi_bipartite_graphsage-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files unsupervised_bipartite_graphsage.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.unsupervised.cluster.BiGraphSageExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath userFeaturePath:$featurePath labelPath:$labelPath\
      torchModelPath:unsupervised_bipartite_graphsage.pt userFeatureDim:2 itemFeatureDim:19 stepSize:0.01\
      optimizer:adam numEpoch:10\
      numPartitions:50 format:sparse userNumSamples:10 itemNumSamples:10 batchSize:128\
      predictOutputPath:$predictOutputPath userEmbeddingPath:$userEmbeddingPath itemEmbeddingPath:$itemEmbeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, unsupervised_bipartite_graphsage.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of Unsupervised Heterogeneous Graph Attention Network(HGAT)

HGAT is a unsupervised graph attention convolution network for Bipartite graph.

Here we give an example of using HGAT over pytorch on angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command: dense/low-sparse data:

python unsupervised_heterogeneous_gat.py --input_user_dim 64 --input_item_dim 64 --hidden_dim 64 --output_dim 64 --output_file hgat_dense.pt --negative_size 32 --heads 2

high-sparse data:

python unsupervised_heterogeneous_gat.py --input_user_dim 32 --input_item_dim 32 --hidden_dim 8 --output_dim 64 --output_file hgat_sparse.pt --input_user_field_num 4 --input_item_field_num 2 --input_user_embedding_dim 8 --input_item_embedding_dim 16 --negative_size 32 --heads 2 --encode multi-hot

This script utilizes TorchScript to generate a model file which contains the dataflow graph of Unsupervised Bipartite GraphSage. After that, you will obtain a model file named "hgat_dense.pt or hgat_sparse.pt". Detailed parameter introduction reference Python Model Parameters

Preparing input data There are three inputs required for HGAT, including the edge table, the user feature node table，and item node feature table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel. dense/low-sparse data submit script:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "HGAT-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files hgat_dense.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.unsupervised.cluster.HGATExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath userFeaturePath:$featurePath labelPath:$labelPath\
      torchModelPath:hgat_dense.pt userFeatureDim:64 itemFeatureDim:64 stepSize:0.0001 decay:0.001\
      optimizer:adam numEpoch:10 testRatio:0.5 \
      numPartitions:50 format:dense userNumSamples:5 itemNumSamples:5 batchSize:128\
      predictOutputPath:$predictOutputPath userEmbeddingPath:$userEmbeddingPath itemEmbeddingPath:$itemEmbeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

high-sparse data submit script:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "HGAT-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files hgat_sparse.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.unsupervised.cluster.HGATExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath userFeaturePath:$featurePath labelPath:$labelPath\
      torchModelPath:hgat_sparse.pt userFeatureDim:25000000 itemFeatureDim:80000 stepSize:0.0001 decay:0.001 fieldMultiHot:true \
      optimizer:adam numEpoch:10 testRatio:0.5 userFieldNum:4 itemFieldNum:2 userFeatEmbedDim:8 itemFeatEmbedDim:16\
      numPartitions:50 format:sparse userNumSamples:5 itemNumSamples:5 batchSize:128\
      predictOutputPath:$predictOutputPath userEmbeddingPath:$userEmbeddingPath itemEmbeddingPath:$itemEmbeddingPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, hgat_sparse.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of INDUCTIVE MATRIX COMPLETION BASED ON GRAPH NEURAL NETWORKS(IGMC)

IGMC IGMC trains a graph neural network (GNN) based purely on 1-hop subgraphs around (user, item) pairs generated from the rating matrix and maps these subgraphs to their corresponding ratings

Here we give an example of using IGMC over pytorch on angel.

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
classification:
```
supervised_igmc.py --input_user_dim 23 --input_item_dim 18 --hidden_dim 32 --edge_types 5 --output_dim 5 --output_file igmc_ml_class.pt
```
regression:
```
python supervised_igmc.py --input_user_dim 23 --input_item_dim 18 --hidden_dim 32 --edge_types 5 --output_dim 5 --method regression --output_file igmc_ml_reg.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of Unsupervised Bipartite GraphSage. After that, you will obtain a model file named "igmc_ml_class.pt or igmc_ml_reg.pt". Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for IGMC, including the edge table(with rating), the node feature table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

The only difference between classification job and regression job is pt model, the submit scriptis same:

source ./spark-on-angel-env.sh  
$SPARK_HOME/bin/spark-submit \
      --master yarn-cluster\
      --conf spark.ps.instances=5 \
      --conf spark.ps.cores=1 \
      --conf spark.ps.jars=$SONA_ANGEL_JARS \
      --conf spark.ps.memory=5g \
      --conf spark.ps.log.level=INFO \
      --conf spark.driver.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraJavaOptions=-Djava.library.path=$JAVA_LIBRARY_PATH:.:./torch/angel_libtorch \
      --conf spark.executor.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.driver.extraLibraryPath=./torch/angel_libtorch \
      --conf spark.executorEnv.OMP_NUM_THREADS=2 \
      --conf spark.executorEnv.MKL_NUM_THREADS=2 \
      --queue $queue \
      --name "IGMC-angel" \
      --jars $SONA_SPARK_JARS  \
      --archives angel_libtorch.zip#torch\  #path to c++ library files
      --files igmc_ml_class.pt \   #path to pytorch script model
      --driver-memory 5g \
      --num-executors 5 \
      --executor-cores 1 \
      --executor-memory 5g \
      --class com.tencent.angel.pytorch.example.supervised.cluster.IGMCExample \
      ./pytorch-on-angel-${VERSION}.jar \   # jar from Compiling java submodule
      edgePath:$edgePath userFeaturePath:$userFeaturePath itemFeaturePath:$itemFeaturePath\
      torchModelPath:igmc_ml_class.pt userFeatureDim:23 itemFeatureDim:18 stepSize:0.0001 decay:0.001\
      optimizer:adam numEpoch:10 testRatio:0.5 \
      numPartitions:50 format:dense batchSize:128\
      predictOutputPath:$predictOutputPath outputModelPath:$outputModelPath\
      actionType:train numBatchInit:5

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

edgePath: the input path (hdfs) of edge table, which contains src, dst and type

Notes:

The model file, igmc_ml_class.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.

Example of GAMLP

GAMLP Following the routine of decoupled GNNs, the feature propagation in GAMLP is executed during pre-computation, which helps it maintain high scalability.

GAMLP contains two independent modules:

1.GAMLP-aggregator, feature propagation aggregation module. This module only needs to be run once in the preprocessing stage as the feature input for subsequent GNN model training; the module can also be run as an independent component as a feature propagation aggregation component common to other GNN algorithms
2.GAMLP-training, the GNN model training module, loads the features of the aggregation module for training. In this training stage, it is no longer necessary to do node sampling, pulling node feature aggregation and other communication overhead operations, which greatly improves the model training efficiency.

Here we give an example of using GAMLP over pytorch on angel.

GAMLP-aggregator

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python aggregator.py --aggregation_type mean --output_file aggregator.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of GAMLP-aggregator. After that, you will obtain a model file named "aggregator.pt". Here we use the Cora dataset as an example, where the feature dimension for each node is 1433 with 7 different classes.
Detailed parameter introduction reference Python Model Parameters
Preparing input data There are two inputs required for GAMLP-aggregator, including the edge table and the node feature table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

dense/low-sparse data:

source ./spark-on-angel-env.sh  
 $SPARK_HOME/bin/spark-submit \
 --master yarn-cluster\
 --conf spark.hadoop.hadoop.job.ugi=tdwadmin,supergroup\
 --conf spark.tdw.authentication=usp:usp@all2012\
 --conf spark.yarn.allocation.am.maxMemory=55g \
 --conf spark.yarn.allocation.executor.maxMemory=55g \
 --conf spark.driver.maxResultSize=20g \
 --conf spark.kryoserializer.buffer.max=2000m\
 --conf spark.submitter=lucytjia \
 --conf spark.ps.instances=20 \
 --conf spark.ps.cores=20 \
 --conf spark.ps.jars=$SONA_ANGEL_JARS \
 --conf spark.ps.memory=10g \
 --conf spark.ps.log.level=INFO \
 --conf spark.hadoop.angel.ps.jvm.direct.factor.use.direct.buff=0.20 \
 --conf spark.hadoop.angel.ps.backup.interval.ms=2000000000 \
 --conf spark.hadoop.angel.netty.matrixtransfer.max.message.size=209715200 \
 --conf spark.hadoop.angel.matrixtransfer.request.timeout.ms=240000 \
 --conf spark.hadoop.angel.ps.request.resource.use.minibatch=true \
 --conf spark.hadoop.angel.ps.router.type=range \
 --conf spark.executor.extraLibraryPath=./torch/torch-lib \
 --conf spark.driver.extraLibraryPath=./torch/torch-lib\
 --conf spark.executorEnv.OMP_NUM_THREADS=2 \
 --conf spark.executorEnv.MKL_NUM_THREADS=2 \
 --queue $queue \
 --name "haggregator" \
 --jars $SONA_SPARK_JARS  \
 --archives $torch#torch\
 --files aggregator.pt\
 --driver-memory 45g \
 --num-executors 50 \
 --executor-cores 20 \
 --executor-memory 10g \
 --class com.tencent.angel.pytorch.examples.supervised.cluster.AggregatorExample \
 ./pytorch-on-angel-${VERSION}.jar \ # jar from Compiling java submodule
 edgePath:$edgePath featurePath:$featurePath hops:4 sampleMethod:aliasTable \
 upload_torchModelPath:aggregator.pt featureDim:1433 sep:tab \
 numPartitions:100 format:dense samples:10 batchSize:128 numBatchInit:128 \
 predictOutputPath:$predictOutputPath periods:10 \
 checkpointInterval:10 psNumPartition:100 useBalancePartition:false

GAMLP-training

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python gamlp.py --input_dim 1433 --hidden_dim 128 --output_dim 7 --hops 4 --output_file gamlp.pt
```
Note that the --hops must be same with the GAMLP-aggregator module. This script utilizes TorchScript to generate a model file which contains the dataflow graph of GAMLP-aggregator. After that, you will obtain a model file named "aggregator.pt". Here we use the Cora dataset as an example, where the feature dimension for each node is 1433 with 7 different classes.
Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for GAMLP-training, including the edge table, the node feature table and the node label talbe. Note that the feature comes from the predictOutputPath of the previous GAMLP-aggregator module. The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

dense/low-sparse data:

 --master yarn-cluster \
 --conf spark.hadoop.hadoop.job.ugi=tdwadmin,supergroup\
 --conf spark.tdw.authentication=usp:usp@all2012\
 --conf spark.yarn.allocation.am.maxMemory=55g \
 --conf spark.yarn.allocation.executor.maxMemory=55g \
 --conf spark.driver.maxResultSize=20g \
 --conf spark.kryoserializer.buffer.max=2000m\
 --conf spark.submitter=lucytjia \
 --conf spark.ps.instances=2 \
 --conf spark.ps.cores=2 \
 --conf spark.ps.jars=$SONA_ANGEL_JARS \
 --conf spark.ps.memory=10g \
 --conf spark.ps.log.level=INFO \
 --conf spark.hadoop.angel.ps.jvm.direct.factor.use.direct.buff=0.20 \
 --conf spark.hadoop.angel.ps.backup.interval.ms=2000000000 \
 --conf spark.hadoop.angel.netty.matrixtransfer.max.message.size=209715200 \
 --conf spark.hadoop.angel.matrixtransfer.request.timeout.ms=240000 \
 --conf spark.hadoop.angel.ps.request.resource.use.minibatch=true \
 --conf spark.hadoop.angel.ps.router.type=hash \
 --conf spark.executor.extraLibraryPath=./torch/torch-lib \
 --conf spark.driver.extraLibraryPath=./torch/torch-lib\
 --conf spark.executorEnv.OMP_NUM_THREADS=2 \
 --conf spark.executorEnv.MKL_NUM_THREADS=2 \
 --queue $queue \
 --name "gamlp" \
 --jars $SONA_SPARK_JARS  \
 --archives $torch#torch\
 --files gamlp.pt\
 --driver-memory 45g \
 --num-executors 2 \
 --executor-cores 2 \
 --executor-memory 10g \
 --class com.tencent.angel.pytorch.examples.supervised.cluster.GAMLPExample \
 ./pytorch-on-angel-${VERSION}.jar \
 edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath hops:4\
 featureDim:1433 sep:tab labelsep:tab stepSize:0.001\
 optimizer:adam numEpoch:100 testRatio:0.4 validatePeriods:1 evals:acc,f1 \
 numPartitions:10 format:dense samples:10 batchSize:128 numBatchInit:128 \
 embeddingPath:$output periods:10 outputModelPath:$outputModelPath \
 checkpointInterval:10 predictOutputPath:$predictOutputPath psNumPartition:10 useBalancePartition:false

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, aggregator.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.
The --hops must be same in all parameter settings.

Example of HGAMLP

HGAMLP is an extension of GAMLP for heterogeneous graphs, it contains two independent modules:

1.HGAMLP-aggregator, Feature propagation aggregation module for heterogeneous graphs. According to the metapaths passed in by the user, aggregate different types of nodes, and output the aggregated features of each type of nodes.
2.HGAMLP-training, the GNN model training module, which is same as GAMLP-training.

Here we give an example of using HGAMLP over pytorch on angel.

GAMLP-aggregator

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python aggregator.py --aggregation_type mean --output_file aggregator.pt
```
This script utilizes TorchScript to generate a model file which contains the dataflow graph of GAMLP-aggregator. After that, you will obtain a model file named "aggregator.pt". Here we use the Cora dataset as an example, where the feature dimension for each node is 1433 with 7 different classes.
Detailed parameter introduction reference Python Model Parameters
Preparing input data There are two inputs required for HGAMLP-aggregator, including the edge table and the node feature table.

The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

dense/low-sparse data:

source ./spark-on-angel-env.sh  
 $SPARK_HOME/bin/spark-submit \
 --master yarn-cluster\
 --conf spark.hadoop.hadoop.job.ugi=tdwadmin,supergroup\
 --conf spark.tdw.authentication=usp:usp@all2012\
 --conf spark.yarn.allocation.am.maxMemory=55g \
 --conf spark.yarn.allocation.executor.maxMemory=55g \
 --conf spark.driver.maxResultSize=20g \
 --conf spark.kryoserializer.buffer.max=2000m\
 --conf spark.submitter=lucytjia \
 --conf spark.ps.instances=20 \
 --conf spark.ps.cores=20 \
 --conf spark.ps.jars=$SONA_ANGEL_JARS \
 --conf spark.ps.memory=10g \
 --conf spark.ps.log.level=INFO \
 --conf spark.hadoop.angel.ps.jvm.direct.factor.use.direct.buff=0.20 \
 --conf spark.hadoop.angel.ps.backup.interval.ms=2000000000 \
 --conf spark.hadoop.angel.netty.matrixtransfer.max.message.size=209715200 \
 --conf spark.hadoop.angel.matrixtransfer.request.timeout.ms=240000 \
 --conf spark.hadoop.angel.ps.request.resource.use.minibatch=true \
 --conf spark.hadoop.angel.ps.router.type=range \
 --conf spark.executor.extraLibraryPath=./torch/torch-lib \
 --conf spark.driver.extraLibraryPath=./torch/torch-lib\
 --conf spark.executorEnv.OMP_NUM_THREADS=2 \
 --conf spark.executorEnv.MKL_NUM_THREADS=2 \
 --queue $queue \
 --name "haggregator" \
 --jars $SONA_SPARK_JARS  \
 --archives $torch#torch\
 --files aggregator.pt\
 --driver-memory 45g \
 --num-executors 50 \
 --executor-cores 20 \
 --executor-memory 10g \
 --class com.tencent.angel.pytorch.examples.supervised.cluster.HeteAggregatorExample  \
 ./pytorch-on-angel-${VERSION}.jar \ # jar from Compiling java submodule
 edgePaths:$edgePaths featurePaths:$featurePaths metapaths:$metapaths isWeighted:false \
 upload_torchModelPath:aggregator.pt sep:tab featureSep:tab featureDims:$featureDims aggregator_in_scala:true \
 numPartitions:2 format:dense samples:10 batchSize:128 numBatchInit:128 useWeightedAggregate:true \
 embeddingOutputPaths:$embeddingOutputPaths periods:10 sampleMethod:randm \
 checkpointInterval:10 psNumPartition:2 useBalancePartition:false

HGAMLP-training

Generate pytorch sciprt model First, go to directory of python/graph and execute the following command:
```
python gamlp.py --input_dim 1433 --hidden_dim 128 --output_dim 7 --hops 4 --output_file gamlp.pt
```
Note that the --hops must be same with the HGAMLP-aggregator metapaths length. This script utilizes TorchScript to generate a model file which contains the dataflow graph of GAMLP-aggregator. After that, you will obtain a model file named "aggregator.pt". Here we use the Cora dataset as an example, where the feature dimension for each node is 1433 with 7 different classes.
Detailed parameter introduction reference Python Model Parameters
Preparing input data There are three inputs required for GAMLP-training, including the edge table, the node feature table and the node label talbe. Note that the feature comes from the predictOutputPath of the previous GAMLP-aggregator module. The detail info see Data Format

Submit model to cluster After obtaining the model file and the inputs, we can submit a task through Spark on Angel.

dense/low-sparse data:

 --master yarn-cluster \
 --conf spark.hadoop.hadoop.job.ugi=tdwadmin,supergroup\
 --conf spark.tdw.authentication=usp:usp@all2012\
 --conf spark.yarn.allocation.am.maxMemory=55g \
 --conf spark.yarn.allocation.executor.maxMemory=55g \
 --conf spark.driver.maxResultSize=20g \
 --conf spark.kryoserializer.buffer.max=2000m\
 --conf spark.submitter=lucytjia \
 --conf spark.ps.instances=2 \
 --conf spark.ps.cores=2 \
 --conf spark.ps.jars=$SONA_ANGEL_JARS \
 --conf spark.ps.memory=10g \
 --conf spark.ps.log.level=INFO \
 --conf spark.hadoop.angel.ps.jvm.direct.factor.use.direct.buff=0.20 \
 --conf spark.hadoop.angel.ps.backup.interval.ms=2000000000 \
 --conf spark.hadoop.angel.netty.matrixtransfer.max.message.size=209715200 \
 --conf spark.hadoop.angel.matrixtransfer.request.timeout.ms=240000 \
 --conf spark.hadoop.angel.ps.request.resource.use.minibatch=true \
 --conf spark.hadoop.angel.ps.router.type=hash \
 --conf spark.executor.extraLibraryPath=./torch/torch-lib \
 --conf spark.driver.extraLibraryPath=./torch/torch-lib\
 --conf spark.executorEnv.OMP_NUM_THREADS=2 \
 --conf spark.executorEnv.MKL_NUM_THREADS=2 \
 --queue $queue \
 --name "gamlp" \
 --jars $SONA_SPARK_JARS  \
 --archives $torch#torch\
 --files gamlp.pt\
 --driver-memory 45g \
 --num-executors 2 \
 --executor-cores 2 \
 --executor-memory 10g \
 --class com.tencent.angel.pytorch.examples.supervised.GAMLPExample \
 ./pytorch-on-angel-${VERSION}.jar \
 edgePath:$edgePath featurePath:$featurePath labelPath:$labelPath hops:4\
 featureDim:1433 sep:tab labelsep:tab stepSize:0.001\
 optimizer:adam numEpoch:100 testRatio:0.4 validatePeriods:1 evals:acc,f1 \
 numPartitions:10 format:dense samples:10 batchSize:128 numBatchInit:128 \
 embeddingPath:$output periods:10 outputModelPath:$outputModelPath \
 checkpointInterval:10 predictOutputPath:$predictOutputPath psNumPartition:10 useBalancePartition:false

Here we give a short description for the parameters in the submit script. Detailed parameters and the output result see details

Notes:

The model file, aggregator.pt and gamlp.pt, should be uploaded to Spark Driver and each Executor. Therefore, we need use --files to upload the model file.
The --hops must be same in all parameter settings.

FAQ

If you want to use GAT or HGAT, pytorch >= v1.5.0.
If you found loss is NAN or does not converge, you can decrease the learning rate, such as: 0.001,0.0001 or lower.
If you encounter the error file not found model.json, please check the version of model.pt and the version of pytorch, whether them are matched.
If you encounter the error java.lang.UnsupportedOperationException: empty collection, please check whether the input data is empty.
If you encounter the error ERROR AngelYarnClient: submit application to yarn failed., ps did not apply for resources, change another cluster or try later.
If you encounter the error java.lang.UnsatisfiedLinkError: no torch_angel in java.library.path, please check whether the torch path is correct.

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graph.md

GNN Algorithm Instructions

Introduction

1. How to predict

2. How to train incrementally

3. How to calculate the resource

Example of GraphSage

Example of DGI/Unsupervised GraphSage

Example of Relation GCN (RGCN)

Example of EdgeProp

Example of GAT

Example of HAN

Example of Semi Bipartite GraphSage

Example of Unsupervised Bipartite GraphSage

Example of Unsupervised Heterogeneous Graph Attention Network(HGAT)

Example of INDUCTIVE MATRIX COMPLETION BASED ON GRAPH NEURAL NETWORKS(IGMC)

Example of GAMLP

GAMLP-aggregator

GAMLP-training

Example of HGAMLP

GAMLP-aggregator

HGAMLP-training

FAQ

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graph.md

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GNN Algorithm Instructions

Introduction

1. How to predict

2. How to train incrementally

3. How to calculate the resource

Example of GraphSage

Example of DGI/Unsupervised GraphSage

Example of Relation GCN (RGCN)

Example of EdgeProp

Example of GAT

Example of HAN

Example of Semi Bipartite GraphSage

Example of Unsupervised Bipartite GraphSage

Example of Unsupervised Heterogeneous Graph Attention Network(HGAT)

Example of INDUCTIVE MATRIX COMPLETION BASED ON GRAPH NEURAL NETWORKS(IGMC)

Example of GAMLP

GAMLP-aggregator

GAMLP-training

Example of HGAMLP

GAMLP-aggregator

HGAMLP-training

FAQ