deprecate DGL-KE. (#1432)

* deprecate DGL-KE.

* Update README.md

* remove from CI.

* remove tests.
Co-authored-by: Zheng <dzzhen@3c22fb0ed81b.ant.amazon.com>

Jenkinsfile

@@ -69,14 +69,6 @@ def unit_test_win64(backend, dev) {
   }
 }
 
-def kg_test_linux(backend, dev) {
-  init_git()
-  unpack_lib("dgl-${dev}-linux", dgl_linux_libs)
-  timeout(time: 20, unit: 'MINUTES') {
-    sh "bash tests/scripts/task_kg_test.sh ${backend} ${dev}"
-  }
-}
-
 def example_test_linux(backend, dev) {
   init_git()
   unpack_lib("dgl-${dev}-linux", dgl_linux_libs)
@@ -370,61 +362,6 @@ pipeline {
         }
       }
     }
-    stage("App") {
-      parallel {
-        stage("Knowledge Graph CPU") {
-          agent { 
-            docker {
-              label "linux-cpu-node"
-              image "dgllib/dgl-ci-cpu:conda" 
-            }
-          }
-          stages {
-            stage("Torch test") {
-              steps {
-                kg_test_linux("pytorch", "cpu")
-              }
-            }
-            stage("MXNet test") {
-              steps {
-                kg_test_linux("mxnet", "cpu")
-              }
-            }
-          }
-          post {
-            always {
-              cleanWs disableDeferredWipeout: true, deleteDirs: true
-            }
-          }
-        }
-        stage("Knowledge Graph GPU") {
-          agent {
-            docker {
-              label "linux-gpu-node"
-              image "dgllib/dgl-ci-gpu:conda"
-              args "--runtime nvidia"
-            }
-          }
-          stages {
-            stage("Torch test") {
-              steps {
-                kg_test_linux("pytorch", "gpu")
-              }
-            }
-            stage("MXNet test") {
-              steps {
-                kg_test_linux("mxnet", "gpu")
-              }
-            }
-          }
-          post {
-            always {
-              cleanWs disableDeferredWipeout: true, deleteDirs: true
-            }
-          }
-        }
-      }
-    }
   }
   post {
     always {

apps/kg/README.md

 # DGL - Knowledge Graph Embedding
 
+**Note: DGL-KE is moved to [here](https://github.com/awslabs/dgl-ke). DGL-KE in this folder is deprecated.**
+
 
 ## Introduction
 

tests/scripts/task_kg_test.sh

-#!/bin/bash
-. /opt/conda/etc/profile.d/conda.sh
-KG_DIR="./apps/kg/"
-
-function fail {
-    echo FAIL: $@
-    exit -1
-}
-
-function usage {
-    echo "Usage: $0 backend device"
-}
-
-# check arguments
-if [ $# -ne 2 ]; then
-    usage
-    fail "Error: must specify device and bakend"
-fi
-
-if [ "$2" == "cpu" ]; then
-    dev=-1
-elif [ "$2" == "gpu" ]; then
-    export CUDA_VISIBLE_DEVICES=0
-    dev=0
-else
-    usage
-    fail "Unknown device $2"
-fi
-
-export DGLBACKEND=$1
-export DGL_LIBRARY_PATH=${PWD}/build
-export PYTHONPATH=${PWD}/python:$KG_DIR:$PYTHONPATH
-export DGL_DOWNLOAD_DIR=${PWD}
-conda activate ${DGLBACKEND}-ci
-# test
-
-pushd $KG_DIR> /dev/null
-
-python3 -m pytest tests/test_score.py || fail "run test_score.py on $1"
-
-if [ "$2" == "cpu" ]; then
-    # verify CPU training DistMult
-    python3 train.py --model DistMult --dataset FB15k --batch_size 128 \
-        --neg_sample_size 16 --hidden_dim 100 --gamma 500.0 --lr 0.1 --max_step 100 \
-        --batch_size_eval 16 --valid --test -adv --eval_interval 30 --eval_percent 0.01 \
-        --save_emb DistMult_FB15k_emb --data_path /data/kg || fail "run DistMult on $2"
-
-    # verify saving training result
-    python3 eval.py --model_name DistMult --dataset FB15k --hidden_dim 100 \
-        --gamma 500.0 --batch_size 16 --model_path DistMult_FB15k_emb/ \
-        --eval_percent 0.01 --data_path /data/kg || fail "eval DistMult on $2"
-elif [ "$2" == "gpu" ]; then
-    # verify GPU training DistMult
-    python3 train.py --model DistMult --dataset FB15k --batch_size 128 \
-        --neg_sample_size 16 --hidden_dim 100 --gamma 500.0 --lr 0.1 --max_step 100 \
-        --batch_size_eval 16 --gpu 0 --valid --test -adv --eval_interval 30 --eval_percent 0.01 \
-        --data_path /data/kg || fail "run DistMult on $2"
-
-    # verify mixed CPU GPU training
-    python3 train.py --model DistMult --dataset FB15k --batch_size 128 \
-        --neg_sample_size 16 --hidden_dim 100 --gamma 500.0 --lr 0.1 --max_step 100 \
-        --batch_size_eval 16 --gpu 0 --valid --test -adv --mix_cpu_gpu --eval_percent 0.01 \
-        --save_emb DistMult_FB15k_emb --data_path /data/kg || fail "run mix with async CPU/GPU DistMult"
-
-    # verify saving training result
-    python3 eval.py --model_name DistMult --dataset FB15k --hidden_dim 100 \
-        --gamma 500.0 --batch_size 16 --gpu 0 --model_path DistMult_FB15k_emb/ \
-        --eval_percent 0.01 --data_path /data/kg || fail "eval DistMult on $2"
-
-    if [ "$1" == "pytorch" ]; then
-        # verify mixed CPU GPU training with async_update
-        python3 train.py --model DistMult --dataset FB15k --batch_size 128 \
-            --neg_sample_size 16 --hidden_dim 100 --gamma 500.0 --lr 0.1 --max_step 100 \
-            --batch_size_eval 16 --gpu 0 --valid --test -adv --mix_cpu_gpu --eval_percent 0.01 \
-            --async_update --data_path /data/kg || fail "run mix CPU/GPU DistMult"
-
-        # verify mixed CPU GPU training with random partition
-        python3 train.py --model DistMult --dataset FB15k --batch_size 128 \
-            --neg_sample_size 16 --hidden_dim 100 --gamma 500.0 --lr 0.1 --max_step 100 \
-            --batch_size_eval 16 --num_proc 2 --gpu 0 --valid --test -adv --mix_cpu_gpu \
-            --eval_percent 0.01 --async_update --force_sync_interval 100 \
-            --data_path /data/kg || fail "run multiprocess async CPU/GPU DistMult"
-
-        # verify mixed CPU GPU training with random partition async_update
-        python3 train.py --model DistMult --dataset FB15k --batch_size 128 \
-            --neg_sample_size 16 --hidden_dim 100 --gamma 500.0 --lr 0.1 --max_step 100 \
-            --batch_size_eval 16 --num_proc 2 --gpu 0 --valid --test -adv --mix_cpu_gpu \
-            --eval_percent 0.01 --rel_part --async_update --force_sync_interval 100 \
-            --data_path /data/kg || fail "run multiprocess async CPU/GPU DistMult"
-
-        # multi process training TransR
-        python3 train.py --model TransR --dataset FB15k --batch_size 128 \
-            --neg_sample_size 16 --hidden_dim 100 --gamma 500.0 --lr 0.1 --max_step 100 \
-            --batch_size_eval 16 --num_proc 2 --gpu 0 --valid --test -adv --eval_interval 30 \
-            --eval_percent 0.01 --data_path /data/kg --mix_cpu_gpu --rel_part --async_update \
-            --save_emb TransR_FB15k_emb || fail "run multiprocess TransR on $2"
-
-        python3 eval.py --model_name TransR --dataset FB15k --hidden_dim 100 \
-            --gamma 500.0 --batch_size 16 --num_proc 2 --gpu 0 --model_path TransR_FB15k_emb/ \
-            --eval_percent 0.01 --mix_cpu_gpu --data_path /data/kg || fail "eval multiprocess TransR on $2"
-    fi
-fi
-
-popd > /dev/null