* 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
* new jenkins script
* fix ci
* poke ci
* new config
* new config
* new config
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* update docker image; poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* poke ci
* update image
* update image
* fix
* Windows CI support
* typo
* typo*2
* missed sh
* typo*3
* missed dir
* disable unit test in mxnet tutorial.
* retry socket connection.
* roll back to set_np_compat
* try to fix multi-processing test hangs when it fails.
* fix test.
* fix.
* Jenkins build & test on Windows
* oops
* still running nohup on Windows slaves
* ooops again
* squishing vcvars and cmake
* another try
* reverting back
* --user
* switching to msbuild
* made the graph size in cache testing bigger
* put commands into script files
* oooops
* remove loading backend in sse
* build mxnet in a second run
* a third run to fix everything
* fix docstring in sampler
* fix docstring in transformer
* README for doc build
* some reordering and printing
* use filename_pattern instead of ignore_pattern to reduce warning at the cost of redundant work...
* add env var to use mxnet in Jenkinsfile
* remove step 3, it's already fixed by previous commit
* add sse tutorial
* add mxnet tutorial ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* Fix ci
* Fix ci
* Fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* fix ci
* Fix CI
Fix CI image
* permission fix
* fix a bug in the code.
* small fix
* fix doc
* fix ci
* shorten the iters
* fix
* remove extra file
* add load_backend api to dynamically switch to another backend
* try fix
* fix tutorial
* fix tutorial
* fix bug in tutorial
* change ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* update ci
* nx package
* update ci
* update ci
* update ci
* fix
* mx dockerfile by zhengda
* python3.6->3.5
* update ci image
* add tutorial test
* fix ci
* fix ssl problem
* minor change
* small fix on traversal utest
* fix syntax
* add matplotlib in image
* fix
* update ci
* update ci
* Add SH tutorials
* setup sphinx-gallery; work on graph tutorial
* draft dglgraph tutorial
* update readme to include document url
* rm obsolete file
* Draft the message passing tutorial
* Capsule code (#102)
* add capsule example
* clean code
* better naming
* better naming
* [GCN]tutorial scaffold
* fix capsule example code
* remove previous capsule example code
* graph struc edit
* modified: 2_graph.py
* update doc of capsule
* update capsule docs
* update capsule docs
* add msg passing prime
* GCN-GAT tutorial Section 1 and 2
* comment for API improvement
* section 3
* Tutorial API change (#115)
* change the API as discusses; toy example
* enable the new set/get syntax
* fixed pytorch utest
* fixed gcn example
* fixed gat example
* fixed mx utests
* fix mx utest
* delete apply edges; add utest for update_edges
* small change on toy example
* fix utest
* fix out in degrees bug
* update pagerank example and add it to CI
* add delitem for dataview
* make edges() return form that is compatible with send/update_edges etc
* fix index bug when the given data is one-int-tensor
* fix doc
* multigraph support on graph index
* more tests
* multigraph flag, bugfix on clear & copy
* networkx interfaces
* including graph index tests in Jenkins
* node subgraph test
* edge subgraphs
* removing duplicates in pred/succ
* more explicit test and doc
* query source and destination from edge id
* subgraphindex
* renaming has_edge to has_edge_between, apply_edges adding eid
* send_on and send_and_recv_on
* DGLGraph edge subgraph
* merged send_on and send_and_recv_on
* change request
* removing hashmap
* creating multigraph by flag; mingw support
* changes per request
* reverting networkx auto multigraph discovery
* notes on send/send_and_recv on multigraphs
* changing test reducer from sum to max
* added a fixme note in spmv scheduler
* Test CPP branch CI (#2)
* Fix batching node-only graphs (#62)
* fixing batching with graphs with no edges
* oops forgot test
* fix readme
* Docker and Jenkins (#1)
* docker ci cpu
* install python packages
* docker ci gpu
* add readme
* use dgl cpu image
* run command in container as root
* use python3
* fix test case
* remove nose from docker file
* docker folder readme
* parallelize cpu and gpu
* top level stages
* comment out python2 related installation
* fix
* remove igraph
* building for cpp
* change building order
* export env in test stage
* withEnv
* run docker container as root
* fix test cases
* fix test cases
* minor
* remove old build
Jinjing Zhou