* 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
* 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
* 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
xiang song(charlie.song)