* 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 code for mxnet TAGCov
* Update some docs
* Fix some code
* Update docs dgl.nn.mxnet
* Update weight init
* Fix
* 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 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+.
* copy graph index to shared memory.
* fix.
* fix.
* fix.
* use a diff name for in-csr and out-csr.
* fix lint.
* remove print.
* add test.
* add comments.
* enable tutorial test in CI.
* extand DGLGraph graph_data.
* update doc.
* Revert "enable tutorial test in CI."
This reverts commit cd774067180922bb6ae979bde4aecbffc61c8147.
* accept DGLGraph in graph store.
* all pytorch examples
* scan through mxnet examples
* change reddit data
* tweak numerical range for unittest
* fix ci
* fix ci
* fix
* add seed to workaround
* fix gat code to use latest edge softmax module
* avoid transpose
* update README
* use edge_softmax op
* mxnet edge softmax op
* mxnet gat
* update README
* fix unittest
* fix ci
* fix mxnet nn test; relax criteria for prod reducer
* to simple
* WIP: multigraph flag
* graph index refactor; pass basic testing
* graph index refactor; pass basic testing
* fix bug in to_simple; pass torch test
* fix mx utest
* fix example
* fix lint
* fix ci
* poke ci
* poke ci
* WIP
* poke ci
* poke ci
* poke ci
* change ci workspace
* poke ci
* poke ci
* poke ci
* poke ci
* delete ci
* use enum for multigraph flag
xiang song(charlie.song)