GitLab

* Fixed device type in inference

* change buffer_device instead

* Update

* Update functional.py

* Update

* Update test_transform.py

* Update

* Update functional.py

* Update functional.py

* Update functional.py

* Update functional.py

* Update

* Update

* Update functional.py

* Update functional.py

* Update functional.py

* Update functional.py

* Update module.py

* Update test_transform.py

* Update test_transform.py
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

Co-authored-by: Xin Yao <xiny@nvidia.com>

* [Log] fix confusing error log in TCPSocket::Bind()

* fix lint

* Modify to repro crash

* Revert to orig. scenario and add fix

* Update
Co-authored-by: Xin Yao <xiny@nvidia.com>

* [Feature] Add CUDA Weighted Randomwalk Sampling

* [Feature] Add CUDA Weighted Randomwalk Sampling

* [Feature] Add CUDA Weighted Randomwalk Sampling

* [Feature] Add CUDA Weighted Randomwalk Sampling

* fix empty prob array && enable non-uniform for restart && enable unit tests

* update doc and guide for randomwalk and pinsage

* update comments
Co-authored-by: zhenliangqiu <ubuntu@ip-172-31-24-245.ap-southeast-1.compute.internal>
Co-authored-by: xiny <xiny@nvidia.com>

Signed-off-by: Serge Panev <spanev@nvidia.com>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

* Refactor gat example

* Add ppi support

* Minor update

* Update

* Update

* Change valid_xxx to val_xxx

* Readme Update

* Update
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-53-142.us-west-2.compute.internal>

* upload PSS

* upload PSS

* upload PSS

* pss code reformat

* fix bug

* update README

* update train bash

* remove vit

* update README

* delete InfoPlotter

* delete Smooth_AP_loss.py

* update README

* update README
Co-authored-by: Tianjun Xiao <xiaotj1990327@gmail.com>

Signed-off-by: Serge Panev <spanev@nvidia.com>
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

* Fix node_classification.py

* Minor update
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>

Signed-off-by: Serge Panev <spanev@nvidia.com>
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

* [Doc] fix docstring typo

* Update sparse_emb.py

* Update sparse_emb.py

* update link

* Update

* Update

* Update

* Update
Co-authored-by: Ubuntu <ubuntu@ip-172-31-53-142.us-west-2.compute.internal>
Co-authored-by: Xin Yao <xiny@nvidia.com>

* Fix test_csrmm for tensor core

* unset allow tf32 flag

* update test unified tensor

* skip fp16 for CPU

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* record stream when using another cuda stream for data transfer

* fix linting

* fix None stream

Co-authored-by: Xin Yao <xiny@nvidia.com>

* feature: add a parse parameter degree_as_nlabel for pytorch-gin demo

* fix some typo

* [fix]: allow to benchmark all of the 9 dataset.

* [Feature] add epoch number to log

* [Feature]:simply list the command lines for all datasets (https://github.com/dmlc/dgl/pull/3676#discussion_r790270705) and run a test.

* Update README.md

* update: DGL-powered projects-DGLD

[DGLD](https://github.com/EagleLab-ZJU/DGLD

) is an open-source library for Deep Graph Anomaly Detection based on pytorch and DGL.  It provides unified interface of popular graph anomaly detection methods, including the data loader, data augmentation, model training and evaluation. Also, the widely used modules are well organized so that developers and researchers can quickly implement their own designed models.
Co-authored-by: Ubuntu <ubuntu@ip-172-31-10-175.ap-northeast-1.compute.internal>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

* dummy

* upgrade torch from 1.8.1 to 1.9.0 for asv

* update torch download link

* remove version for torchvision and torch text

* revert unnecessary change

* fix several test failure

* fix

* remove am dataset

* fix test failure

* fix test failure

* fix rgcn failures

* First update to fix two examples

* Update to fix RGCN/graphsage example and dataloader

* Update

* [Dist] enable to specify sort_etype for sample_etype_neighbours

* fix lint

* pass argument instead of env

* fix lint and doc string

* refine args

* remove unnecessary lines

* debug only

* debug add sort time log

* change interface

* fix typo
Co-authored-by: Xin Yao <xiny@nvidia.com>

* [Dist] format dtypes when loading graph in server

* add test

* refine

* add comments

Co-authored-by: Mufei Li <mufeili1996@gmail.com>

* Fix arma example

* Update
Co-authored-by: Xin Yao <xiny@nvidia.com>

Signed-off-by: Serge Panev <spanev@nvidia.com>
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

Co-authored-by: Xin Yao <xiny@nvidia.com>

* Added to_cugraph and from_cugraph functionality

* fix from_cugraph example

* Addressed Reviews

* Fix linting

* Apply suggestions to docstrings from code review
Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>

* Add API docs and remove `from_cugraph` alias

* move cugraph tests to  tests/cugraph/test_basics.py

* remove ununsed imports from test_basics.py

* remove pytest.importorskip as no longer needed
Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* Refactor GCN example

* Refactor GCN based on graphsage

* Readme update

* Minor update

* update

* Remove user-defined GCN implementation

* README update

* Update

* Update CONTRIBUTORS.md

* update task_example_test
Co-authored-by: Xin Yao <xiny@nvidia.com>

* [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