* 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
* Code changes for the following
1. Generating node data at each process
2. Reading csv files using pyarrow
3. feature complete code.
* Removed some typo's because of which unit tests were failing
1. Change the file name to correct file name when loading edges from file
2. When storing node-features after shuffling, use the correct key to store the global-nids of node features which are received after transmitted.
* Code changes to address CI comments by reviewers
1. Removed some redundant code and added text in the doc-strings to describe the functionality of some functions.
2 function signatures and invocations now match w.r.t argument list
3. Added detailed description of the metadata json structure so that the users understand the the type of information present in this file and how it is used through out the code.
* Addressing code review comments
1. Addressed all the CI comments and some of the changes include simplifying the code related to the concatenation of lists and enhancing the docstrings of functions which are changed in this process.
* Update docstring's of two functions appropriately in response to code review comments
Removed "todo" from the docstring of the gen_nodedata function.
Added "todo" to the gen_dist_partitions function when node-id to partition-id's are read for the first time.
Removed 'num-node-weights' from the docstring for the get_dataset function and added schema_map docstring to the argument list.
* 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>
* Added code to support multiple-file-support feature and removed single-file-support code
1. Added code to read dataset in multiple-file-format
2. Removed code for single-file format
* added files missing in the previous commit
This commit includes dataset_utils.py, which reads the dataset in multiple-file-format, gloo_wrapper function calls to support exchanging dictionaries as objects and helper functions in utils.py
* Update convert_partition.py
Updated function call "create_metadata_json" file to include partition_id so that each rank only creates its own metadata object and later on these are accumulated on rank-0 to create graph-level metadata json file.
* addressing code review comments during the CI process
code changes resulting from the code review comments received during the CI process.
* Code reorganization
Addressing CI comments and code reorganization for easier understanding.
* Removed commented out line
removed commented out line.
* [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
* [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>
* * 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>
* code changes for bug fixes identified during mag_lsc dataset
1. Changed from call torch.Tensor() to torch.from_numpy() to address memory corruption issues when creating large tensors. Tricky thing is this works correctly for small tensors.
2. Changed dgl.graph() function call to include 'num_nodes" argument to specifically mention all the nodes in a graph partition.
* Update convert_partition.py
Moving the changes to the function "create_metadata_json" function to the "multiple-file-format" support, where this change is more appropriate. Since multiple machine testing was done with these code changes.
* Addressing review comments.
Removed space as suggested at the end of the line
* 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>
* * 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)
* [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
* 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>
Hongyi ZHANG