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Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-22-182.ap-northeast-1.compute.internal>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-25-242.ap-northeast-1.compute.internal>

* exclude_master

* fix

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Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

* test

* blabla

* add

* reformat

* balbla

* rollback

* remove

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Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

* Abort previous CI

* script

* newtry

* more

* fix

* comment

* add notes

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Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

* enable sparse on windows and mac

* that was stupid

* let's see what's going on..

* [Sparse] Fix the import error on Mac OS.

When using template functions that are defined in source files from DGL,
the loader of MacOS somehow cannot find their definitions. This fix simply
avoids depending on template functions from DGL headers.

With this fix, the sparse tests all pass on the MAC environment.

* ok this is the problem

* make errors clearer

* uh

* test

* Update __init__.py

* disabling ddp on windows

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Co-authored-by: czkkkkkk <zekucai@gmail.com>

* [CI] updgrade pytorch version for benchmark CI

* update build arguments

* updage

* updage

* upgrade torch to 1.13

* updage docker image

* update cmake args

* try with cu116_torch112

* update build

* update

* update

* update

* update docker image

* update

* update

* update

* update

* final update

* fix continue running

* update

* update

* update

* upgrade CI pytorch version

* update

* revert tensorflow image

* old image (220816) backup

* Update Dockerfile.ci_gpu

Recently we find dist test often fails due to timeout. Root cause is not found yet.

* [Sparse] Add sparse matrix C++ implementation

* Add documentation

* Update

* Minor fix

* Move Python code to dgl/mock_sparse2

* Move headers to include

* lint

* Update

* Add dgl_sparse directory

* Move src code to dgl_sparse

* Add __init__.py in tests to avoid naming conflict

* Add dgl sparse so in Jenkinsfile

* Complete docstring & SparseMatrix basic op

* lint

* Disable win tests

* [CI] Enable DGL cugaph nightly CI

* fix build issue

* remove runtime

* [CI] refine stage logic

* refine

* refine

This reverts commit ec171c64.

* 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>

* [CI] only known devs are authorized to trigger CI

* fix if author is null

* add comments

* [CI] upgrade python version to 3.7.0

* do not upgrade for mxnet cpu due to seg fault

* fix test failure for mxnet

* [CI] upgrade python version to 3.7.0

* do not upgrade for mxnet cpu due to seg fault

* fix test failure for mxnet

* [CI] separate distributed tests from torch cpu tests

* remove TF related env

* [CI] add new stage specific forcuda related features based on nvidia+pytorch

* build and test for gpu_nv

* fix build failure

* fix unit tests

* make -j

* install cython beforehand

* copy cython lib

* test cugraph tests only

* fix typo

* separate test script for cugraph

* refactor build dgl shell