[Feature][REVIEW] Enable DGL cugaph nightly CI (#4525)

* Added cugraph nightly scripts

* Removed nvcr.io//nvidia/pytorch:22.04-py3 reference
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

Jenkinsfile

@@ -296,9 +296,9 @@ pipeline {
               agent {
                 docker {
                   label "linux-cpu-node"
-                  image "nvcr.io/nvidia/pytorch:22.04-py3"
+                  image "rapidsai/cugraph_nightly_torch-cuda:11.5-base-ubuntu18.04-py3.9-pytorch1.11.0-rapids22.10"
                   args "-u root"
-                  alwaysPull false
+                  alwaysPull True
                 }
               }
               steps {
@@ -523,9 +523,9 @@ pipeline {
               agent {
                 docker {
                   label "linux-gpu-node"
-                  image "nvcr.io/nvidia/pytorch:22.04-py3"
+                  image "rapidsai/cugraph_nightly_torch-cuda:11.5-base-ubuntu18.04-py3.9-pytorch1.11.0-rapids22.10"
                   args "--runtime nvidia --shm-size=8gb"
-                  alwaysPull false
+                  alwaysPull True
                 }
               }
               stages {

tests/scripts/build_dgl.sh

@@ -7,12 +7,15 @@ if [ $# -ne 1 ]; then
     exit -1
 fi
 
-CMAKE_VARS="-DBUILD_CPP_TEST=ON -DUSE_OPENMP=ON -DBUILD_TORCH=ON"
+CMAKE_VARS="-DBUILD_CPP_TEST=ON -DUSE_OPENMP=ON"
 # This is a semicolon-separated list of Python interpreters containing PyTorch.
 # The value here is for CI.  Replace it with your own or comment this whole
 # statement for default Python interpreter.
 if [ "$1" != "cugraph" ]; then
-    CMAKE_VARS="$CMAKE_VARS -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
+    # We do not build pytorch for cugraph because currently building
+    # pytorch against all the supported cugraph versions is not supported
+    # See issue: https://github.com/rapidsai/cudf/issues/8510
+    CMAKE_VARS=" $CMAKE_VARS -DBUILD_TORCH=ON -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
 fi
 
 #This is implemented to detect underlying architecture and enable arch specific optimization.