Merge branch 'master' into patch-1

dmlc · Oct 30, 2023 · 7c9a773 · 7c9a773
2 parents 2363647 + 9e26c8e
commit 7c9a773
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Showing 9 changed files with 23 additions and 24 deletions.
diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.8)
+cmake_minimum_required(VERSION 3.18)
 ########################################
 # Borrowed and adapted from TVM project
 ########################################

diff --git a/Jenkinsfile b/Jenkinsfile
@@ -317,7 +317,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-cpu"
-                  image "dgllib/dgl-ci-cpu:v230711"
+                  image "dgllib/dgl-ci-cpu:v231027_1900"
                   args "-u root"
                   alwaysPull true
                 }
@@ -336,7 +336,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-cpu"
-                  image "dgllib/dgl-ci-gpu:cu116_v230711"
+                  image "dgllib/dgl-ci-gpu:cu116_v231027_1900"
                   args "-u root"
                   alwaysPull true
                 }
@@ -391,7 +391,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-cpu"
-                  image "dgllib/dgl-ci-cpu:v230711"
+                  image "dgllib/dgl-ci-cpu:v231027_1900"
                   args "-u root"
                   alwaysPull true
                 }
@@ -410,7 +410,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-gpu"
-                  image "dgllib/dgl-ci-gpu:cu116_v230711"
+                  image "dgllib/dgl-ci-gpu:cu116_v231027_1900"
                   args "-u root --runtime nvidia"
                   alwaysPull true
                 }
@@ -463,7 +463,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-gpu"
-                  image "dgllib/dgl-ci-gpu:cu116_v230711"
+                  image "dgllib/dgl-ci-gpu:cu116_v231027_1900"
                   args "-u root --runtime nvidia"
                   alwaysPull true
                 }
@@ -488,7 +488,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-cpu"
-                  image "dgllib/dgl-ci-cpu:v230711"
+                  image "dgllib/dgl-ci-cpu:v231027_1900"
                   args "-u root --shm-size=4gb"
                   alwaysPull true
                 }
@@ -541,7 +541,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-gpu"
-                  image "dgllib/dgl-ci-gpu:cu116_v230711"
+                  image "dgllib/dgl-ci-gpu:cu116_v231027_1900"
                   args "-u root --runtime nvidia --shm-size=8gb"
                   alwaysPull true
                 }
@@ -570,7 +570,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-cpu"
-                  image "dgllib/dgl-ci-cpu:v230711"
+                  image "dgllib/dgl-ci-cpu:v231027_1900"
                   args "-u root --shm-size=4gb"
                   alwaysPull true
                 }
@@ -618,7 +618,7 @@ pipeline {
               agent {
                 docker {
                   label "dgl-ci-linux-cpu"
-                  image "dgllib/dgl-ci-cpu:v230711"
+                  image "dgllib/dgl-ci-cpu:v231027_1900"
                   args "-u root"
                   alwaysPull true
                 }

diff --git a/docker/install/conda_env/mxnet_cpu.yml b/docker/install/conda_env/mxnet_cpu.yml
@@ -7,7 +7,7 @@ dependencies:
     - pytest
     - nose
     - numpy
-    - cython
+    - cython==0.29
     - scipy
     - networkx
     - matplotlib

diff --git a/docker/install/conda_env/mxnet_gpu.yml b/docker/install/conda_env/mxnet_gpu.yml
@@ -7,7 +7,7 @@ dependencies:
     - pytest
     - nose
     - numpy
-    - cython
+    - cython==0.29
     - scipy
     - networkx
     - matplotlib

diff --git a/docker/install/conda_env/tensorflow_cpu.yml b/docker/install/conda_env/tensorflow_cpu.yml
@@ -7,7 +7,7 @@ dependencies:
     - pytest
     - nose
     - numpy
-    - cython
+    - cython==0.29
     - scipy
     - networkx
     - matplotlib

diff --git a/docker/install/conda_env/tensorflow_gpu.yml b/docker/install/conda_env/tensorflow_gpu.yml
@@ -7,7 +7,7 @@ dependencies:
     - pytest
     - nose
     - numpy
-    - cython
+    - cython==0.29
     - scipy
     - networkx
     - matplotlib

diff --git a/docker/install/ubuntu_install_build.sh b/docker/install/ubuntu_install_build.sh
@@ -1,6 +1,6 @@
-# install cmake 3.15, cmake>=3.12 is required for CUDA 10.1
-version=3.15
-build=5
+# Install cmake with minimum required version.
+version=3.18
+build=0
 mkdir ~/temp
 cd ~/temp
 wget https://cmake.org/files/v$version/cmake-$version.$build-Linux-x86_64.sh 

diff --git a/graphbolt/CMakeLists.txt b/graphbolt/CMakeLists.txt
@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.18)
 project(graphbolt C CXX)
 set (CMAKE_CXX_STANDARD 17)
 
@@ -57,9 +57,8 @@ target_include_directories(${LIB_GRAPHBOLT_NAME} PRIVATE ${BOLT_DIR}
                            "../third_party/pcg/include")
 target_link_libraries(${LIB_GRAPHBOLT_NAME} "${TORCH_LIBRARIES}")
 
-# TODO: upgrade to 17 for consistency with CXX standard once our linux CI supports it.
 if(USE_CUDA)
-  set_target_properties(${LIB_GRAPHBOLT_NAME} PROPERTIES CUDA_STANDARD 14)
+  set_target_properties(${LIB_GRAPHBOLT_NAME} PROPERTIES CUDA_STANDARD 17)
 endif()
 
 # The Torch CMake configuration only sets up the path for the MKL library when

diff --git a/python/dgl/distributed/nn/pytorch/sparse_emb.py b/python/dgl/distributed/nn/pytorch/sparse_emb.py
@@ -60,10 +60,10 @@ class DistEmbedding:
 
     Note
     ----
-    When a ``DistEmbedding``  object is used when the deep learning framework is recording
-    the forward computation, users have to invoke
-    py:meth:`~dgl.distributed.optim.SparseAdagrad.step` afterwards. Otherwise, there will be
-    some memory leak.
+    When a ``DistEmbedding``  object is used in the forward computation, users
+    have to invoke
+    :py:meth:`~dgl.distributed.optim.SparseAdagrad.step` afterwards. Otherwise,
+    there will be some memory leak.
     """
 
     def __init__(