GitLab

* 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

* minor update on golden example

* update

* update

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

* [Feature] extend sort_csr/csc_by_tag to edge

* fix test ffailure in tensorflow

* refine sorting by edges

* fix docstring

* remove unnecessary mem
Co-authored-by: Xin Yao <xiny@nvidia.com>

* Regolden graphsage example to guide others

* update golden

* update

* Update example and propagate to original folder

* Update to remove ^M (windows DOS) character

* update

* Merge file changes and update README

* Minor comment update
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

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

* try optimize CI

* fix go test; adjust timing report

* disable certain tests for mx/tf backends

* fix ut

* add pydantic

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

* * Workaround for graph data saving/loading compatibility problem in Column class.  There may be more places in DGL with the same issue, due to using Python serialization, instead of a more cohesive, comprehensive strategy.  This is just a local fix.

* Add checking for non-empty states

* Add unit test

* Handle the case of columns without storage
Co-authored-by: ndickson <ndickson@nvidia.com>
Co-authored-by: Xin Yao <xiny@nvidia.com>

* fix using alternative streams

* use a alternative stream for subgraph transferring

* fix StreamContext when stream is None

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

* Update nccl communicator for when NCCL is missing

* Use static_cast

* Add doc string

* Fix whitespace

* Resrtict unit test to GPU runs
Co-authored-by: Xin Yao <xiny@nvidia.com>

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Xin Yao <xiny@nvidia.com>

* [BugFix] fix build issue on mac OS

* refine

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* * Added missing specializations for `__half` of `DLDataTypeTraits`, `IndexSelect`, `Full`, `Scatter_`, `CSRGetData`, `CSRMM`, `CSRSum`, `IndexSelectCPUFromGPU`
* Fixed casting issue in `_LinearSearchKernel` that was preventing it from supporting `__half`
* Added `#if`'d out specializations of `CSRGEMM`, `CSRGEAM`, and `Xgeam`, which would require functions that aren't currently provided by cublas

* * Added more specific error messages for unimplemented FP16 specializations of Xgeam, CSRGEMM, and CSRGEAM

* * Added missing instantiation of DLDataTypeTraits<__half>::dtype

* * Fixed linter error
* Added clearer comment explaining why the cast to long long is necessary

* * Worked around a compile error in some particular setup, where __half can't be constructed on the host side

* * Fixed linter formatting errors

* * Changes to comments as recommended

* * Made recommended changes to logging errors in FP16 specializations
* Also changed the existing Xgeam function for unsupported data types from LOG(INFO) to LOG(FATAL)

* [BugFix] fix rpc-related build issue on mac OS

* add warning message

* add warning message

* [Dist] enable USE_EPOLL in default

* fix build issue on windows

* fix build issue on windows

* fix build issue on windows

* fix build issue on windows

* fix build issue on windows

* fix build issue

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

* Add uva by default to embedding

* More updates

* Update optimizer

* Add new uva functions

* Expose new pinned memory function

* Add unit tests

* Update formatting

* Fix unit test

* Handle auto UVA case when training is on CPU

* Allow per-embedding decisions for whether to use UVA

* Address spares_optim.py comments

* Remove unused templates

* Update unit test

* Use dgl allocate memory for pinning

* allow automatically unpin

* workaround for d2h copy with a different dtype

* fix linting

* update error message

* update copyright
Co-authored-by: Xin Yao <xiny@nvidia.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

Co-authored-by: Mufei Li <mufeili1996@gmail.com>
Co-authored-by: Xin Yao <xiny@nvidia.com>