* [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
* [DistTest] add basic pipeline for dist test across machines
* move launch remote cmd to separate file
* add test for rpc
* fix function naming rule
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Enable FP16 for GPU builds in CI
* Limit default GPU archs to pascal and above
* Disable FP16 dispatching for cuda architectures less than 60
* Fix linting
* Fix typos
* add ut
* add doc link
* install dep
* fix ci
* fix ut; more comments
* remove deprecated attributes in rdf datasets; fix label feature name
* address comments
* fix ut for other frameworks
* [Feature] add CSVDataset to load data from csv files
* add CSVDataset class file
* install pyyaml when running unit tests
* install pandas for unit tests
* utilize pydantic for YAML config check
* generate yaml and csv files during test
* make more keys as optional
* remove/rename several keys in yaml config and more tets though looks a bit clumsy
* fix test failure on mxnet
* pass /path/to/dataset instead of yaml path
* code refinement
* code refine
* change several yaml field such as feat and graph_id
* merge graph generation logic
* refine code
* Refactored_first_version
* DGLCSVDataset works for single heterograph
* add more tests
* fix test failure in mxnet
* add docstring
* use list comprehension for dict
* fix version in YAML
* refine data length assert
* use dict.pop directly
* remove ambiguous variable names
* refine graph id missing logic
* refine graph create call
* separate node/edge/graph data parser
* remove separator in DefaultDataParser
* refine validation error log for yaml field
* minor check
* refine code via dict.get()
* move load_from_csv into Node/Edge/GraphData
* move _parse_node/edge/graph_data into Node/Edge/GraphData
* refine id-related fields check
* check duplicate ntypes/etypes when load yaml
* refine docstring
* first commit
* some thoughts
* move around
* more commit
* more fixes
* now it uses torch allocator
* fix symbol export error
* fix
* fixes
* test fix
* add script
* building separate library per version
* fix for vs2019
* more fixes
* fix on windows build
* update jenkinsfile
* auto copy built dlls for windows
* lint and installation guide update
* fix
* specify conda environment
* set environment for ci
* fix
* fix
* fix
* fix again
* revert
* fix cmake
* fix
* switch to using python interpreter path
* remove scripts
* debug
* oops sorry
* Update index.rst
* Update index.rst
* copies automatically, no need for this
* do not print message if library not found
* tiny fixes
* debug on nightly
* replace add_compile_definitions to make CMake 3.5 happy
* fix linking to wrong lib for multiple pytorch envs
* changed building strategy
* fix nightly
* fix windows
* fix windows again
* setup bugfix
* address comments
* change README
* Several optimizations on DGL-KG:
1. Sorted positive edges for sampling which can reduce random
memory access during positive sampling
2. Asynchronous node embedding update
3. Balanced Relation Partition that gives balanced number of
edges in each partition. When there is no cross partition
relation, relation embedding can be pin into GPU memory
4. tunable neg_sample_size instead of fixed neg_sample_size
* Fix test
* Fix test and eval.py
* Now TransR is OK
* Fix single GPU with mix_cpu_gpu
* Add app tests
* Fix test script
* fix mxnet
* Fix sample
* Add docstrings
* Fix
* Default value for num_workers
* Upd
* upd
* unit graph that prefers coo queries
* auto detect coo preference
* forgot some functions
* disable lint on detect_prefer_coo
* reorg
* change comment
* lint
* fix
* move array_utils.h to src
* compact graph impl
* fix redundant copying in idhashmap
* docstring
* moving preference detection to C
* lint
* fix unit test & address comments
* hypersparse autorestrict
* docstring & fix
* revert copyto and asnumbits
* fix stupid bug
* lint
* leave a TODO for sorted COO
* fixing same node type mapping to different id in different graphs
* addresses comments
* made induced nodes a feautre column
* lint?
* upd
* fig edgebatch edges
* add test
* trigger
* Update README.md for pytorch PinSage example.
Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.
* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.
1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
test/pytorch/test_nn.py work on both CPU and GPU
* Fix style
* Delete unused code
* Make agnostic test only related to tests/backend
1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu
* Fix code style
* fix
* doc
* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.
* Fix syntex
* Remove rand
* Add TAGCN nn.module and example
* Now tagcn can run on CPU.
* Add unitest for TGConv
* Fix style
* For pubmed dataset, using --lr=0.005 can achieve better acc
* Fix style
* Fix some descriptions
* trigger
* Fix doc
* Add nn.TGConv and example
* Fix bug
* Update data in mxnet.tagcn test acc.
* Fix some comments and code
* delete useless code
* Fix namming
* Fix bug
* Fix bug
* Add test for mxnet TAGCov
* Add test code for mxnet TAGCov
* Update some docs
* Fix some code
* Update docs dgl.nn.mxnet
* Update weight init
* Fix
* reproduce the bug
* Fix concurrency bug reported at #755.
Also make test_shared_mem_store.py more deterministic.
* Update test_shared_mem_store.py
* Update dmlc/core
* Update Knowledge Graph CI with new Docker image
* Remove unused line_profierx
* Poke Jenkins
* Update test with exit code check and simplify docker
* Update Jenkinsfile to make app test a standalone stage
* Update kg_test
* Update Jenkinsfile
* Make some KG test parallel
* Update
* KG MXNet does not support ComplEx
* Update Jenkinsfile
* Update Jenkins file
* Change torch-1.2 to torch-1.2-cu92
* ci
* Update ubuntu_install_mxnet_cpu.sh
* Update ubuntu_install_mxnet_gpu.sh
* We only need to test train and eval script.
Delete some test code
* upd
* fig edgebatch edges
* add test
* trigger
* Update README.md for pytorch PinSage example.
Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.
* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.
1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
test/pytorch/test_nn.py work on both CPU and GPU
* Fix style
* Delete unused code
* Make agnostic test only related to tests/backend
1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu
* Fix code style
* fix
* doc
* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.
* Fix syntex
* Remove rand
* Add TAGCN nn.module and example
* Now tagcn can run on CPU.
* Add unitest for TGConv
* Fix style
* For pubmed dataset, using --lr=0.005 can achieve better acc
* Fix style
* Fix some descriptions
* trigger
* Fix doc
* Add nn.TGConv and example
* Fix bug
* Update data in mxnet.tagcn test acc.
* Fix some comments and code
* delete useless code
* Fix namming
* Fix bug
* Fix bug
* Add test for mxnet TAGCov
* Add test code for mxnet TAGCov
* Update some docs
* Fix some code
* Update docs dgl.nn.mxnet
* Update weight init
* Fix
* init version.
* change default value of regularization.
* avoid specifying adversarial_temperature
* use default eval_interval.
* remove original model.
* remove optimizer.
* set default value of num_proc
* set default value of log_interval.
* don't need to set neg_sample_size_valid.
* remove unused code.
* use uni_weight by default.
* unify model.
* rename model.
* remove unnecessary data sampler.
* remove the code for checkpoint.
* fix eval.
* raise exception in invalid arguments.
* remove RowAdagrad.
* remove unsupported score function for now.
* Fix bugs of kg
Update README
* Update Readme for mxnet distmult
* Update README.md
* Update README.md
* revert changes on dmlc
* add tests.
* update CI.
* add tests script.
* reorder tests in CI.
* measure performance.
* add results on wn18
* remove some code.
* rename the training script.
* new results on TransE.
* remove --train.
* add format.
* fix.
* use EdgeSubgraph.
* create PBGNegEdgeSubgraph to simplify the code.
* fix test
* fix CI.
* run nose for unit tests.
* remove unused code in dataset.
* change argument to save embeddings.
* test training and eval scripts in CI.
* check Pytorch version.
* fix a minor problem in config.
* fix a minor bug.
* fix readme.
* Update README.md
* Update README.md
* Update README.md
Xin Yao