GitLab

* reorg tutorials and api docs

* fix

* add env var

* Trigger CI

* simplification

* add doc

* several nn example

* appnp

* fix lint

* lint

* add dgi

* fix

* fix

* fix

* fff

* docs

* 111

* fix

* change init

* change result

* tiaocan+1

* fix

* fix lint

* fix

* fix

* upd

* upd

* upd

* upd

* lint

* upd

* upd

* upd

* upd
Co-authored-by: VoVAllen <VoVAllen@users.noreply.github.com>

* change download link to cdn

* Update env_var.rst

* update work with different backend section

* fix some warnings

* Update backend.rst

* Update index.rst
Co-authored-by: VoVAllen <VoVAllen@users.noreply.github.com>

* Add several splitting methods

* Update

* Update

* Update

* Update

* Update

* Fix

* Update

* Update

* Update

* Update

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Finally

* CI

* Update contribute.rst

* Update contribute.rst

* Update contribute.rst

* Update contribute.rst

* Edit for grammar and style

Improve the flow and readability

* Update docs/source/features/nn.rst

Better now? NN Modules as a title is vague

* grammatical updates

Edit pass for readability. 
Can you clarify: "are of different, but shapes that can be broadcast." Are they of different shapes, but both can be broadcast?

* Update docs/source/features/builtin.rst

Okay now? Check this for logic.

* Grammar and style edit pass

In the opening, it would be great to provide some rationale for why you recommend conda or pip.

* Update docs/source/install/index.rst
Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update docs/source/install/index.rst
Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update docs/source/install/index.rst
Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update

* Update

* Update

* Fix

* CI style fix

* CI fix style

* Fix

* Try CI

* Fix test

* Update

* Update

* Update

* Update

* upd

* damn it

* fuck

* fuck pylint

* fudge

* remove some comments about MXNet

* upd

* upd

* damn it

* damn it

* fuck

* fuck

* upd

* upd

* pylint bastard

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* Update

* Fix style

* Update

* Update

* Fix

* Update

* Update

* Refactor

* Add note

* Update

* CI

* minor spelling updates

* Update docs/source/features/builtin.rst

* heterograph tutorial skeleton

* [WIP][Tutorial] Heterogeneous graph tutorial

* fix

* update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* style fixes & undefined name fix

* transpose=False in test

* 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

* convert np.ndarray to backend tensor

* add datasets

* add qm7

* add dataset

* add dataset

* fix

* change ppi

* tu dataset

* add datasets

* fix

* fix

* fix

* fix

* add docstring

* docs

* doc

* add transform

* lint

* lint

* fix

* fixmx

* fix

* add test

* fix typo

* fix default num_classes

* change to non-inplace operation

* fix lint

* fix

* fix

* fix lint

* fixlint

* add transform

* lint

* lint

* fix

* fixmx

* fix

* add test

* fix typo

* fix default num_classes

* change to non-inplace operation

* fix lint

* 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

* Start implementing masked-mm kernel.

A...

* Add serialization

* add serialization

* add serialization

* lalalalalalalala

* lalalalalalalala

* serialize

* serialize

* nnn

* WIP: import tvm runtime node system

* WIP: object system

* containers

* tested basic container composition

* tested custom object

* tmp

* fix setattr bug

* tested object container return

* fix lint

* some comments about get/set state

* fix lint

* fix lint

* update cython

* fix cython

* ffi doc

* fix doc

* WIP: using object system for graph

* c++ side refactoring done; compiled

* remove stale apis

* fix bug in DGLGraphCreate; passed test_graph.py

* fix bug in python modify; passed utest for pytorch/cpu

* fix lint

* Add serialization

* Add serialization

* fix

* fix typo

* serialize with new ffi

* commit

* commit

* commit

* save

* save

* save

* save

* commit

* clean

* Delete tt2.py

* fix lint

* Add serialization

* fix lint 2

* fix lint

* fix lint

* fix lint

* fix lint

* Fix Lint

* Add serialization

* Change to Macro

* fix

* fix

* fix bugs

* refactor

* refactor

* updating dmlc-core to include force flag

* trying tempfile

* delete leaked pointer

* Fix assert

* fix assert

* add comment and test case

* add graph labels

* add load labels

* lint

* lint

* add graph labels

* lint

*  fix windows

* fix

* update dmlc-core to latest

* fix

* fix camel naming

* 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

* Test performance of udf

* trigger

* Fix doc

* Add nn.TGConv and example

* Update test code

* 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

* Minor opt for URRevel

* Delete test code

* Update code style and notes.

* Fix func name

* Update

* Update

* Update

* Update fix

* Update

* Update

* Refactor

* Update

* Update

* Update

* Update

* Update

* Update

* Fix style

* fix doc

* poke ci

* poke ci

* upd

* fix doc

* upd

* add title for all nn

* upd

* [Conda] Update license

* doc fixes

* 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

* initial commit

* second commit

* another commit

* change docstring

* migrating to dgl.nn

* fixes

* docs

* lint

* multiple fixes

* doc

* gat

* upd

* upd sage

* upd

* upd

* upd

* upd

* upd

* add gmmconv

* upd ggnn

* upd

* upd

* upd

* upd

* add citation examples

* add README

* fix cheb

* improve doc

* formula

* upd

* trigger

* lint

* lint

* upd

* add test for transform

* add test

* check

* upd

* improve doc

* shape check

* upd

* densechebconv, currently not correct (?)

* fix cheb

* fix

* upd

* upd sgc-reddit

* upd

* trigger

* upd

* upd

* upd

* upd

* upd

* passed test

* add note

* upd

* trigger

* slight change

* upd

* upd

* trigger

* fix

* simplify

* upd

* upd

* fudge

* upd

* trigger

* test partial

* upd

* trigger