GitLab

* 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

* update docker image

* specify lint version

* rm torch import from unified tests

* 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