GitLab

* change ci image

* fix

* force bash

* fix

* fix python version

* fix

* fix

* fix

* update gpu

* cuda

* jenkins

* fix build sh

* fix

* Revert "fix"

This reverts commit 6b091914b3ef6b4300fa662ee375aa4b27944f5c.

* try fix

* fix

* Revert "fix"

This reverts commit e42c3035fa4974c6b197aaba0748f7b45113ddaa.

* try fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix tests

* try fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix ctx problem

* fix many tests

* fix typo

* add backend

* move to pytorch folder

* fix?

* fix ci

* try skip

* try false

* try?

* try?

* Revert to 7d9a52f

* fix various

* fix lint

* Revert Jenkinsfile

* revert env

* revert env

* address comment

* remove file

* [Bug] Fix #1088

* fix

* add comment

* fix

* improve

* fix lint

* upd

* fix

* upd

* fix

* unit test

* To(device) should return updated object

* Add test case and doc

* Add another doc

* Add descriptions  and a test

* Pass test

* Update

* Update

* Update

* Fix

* CI style fix

* CI fix style

* Fix

* Try CI

* Fix test

* Update

* Update

* Update

* Update

* Update

* Try CI

* Small change for kvstore api

* fix ci

* fix ci

* Performance improvment for distributed kvstore

* 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

* [BUG] Fix #717

* fix mxnet 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 t...

* 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

* 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

* check the number of columns.

* add 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 ta...

* reduce test.

* increase timeout for unittest.

* edge sampler return head nodes and tail nodes.

* edge subgraph.

* use VecToIdArray

* example.

* fix compile error.

* [Hetero] Create heterograph from dictionary of edge lists

* rename

* address comments

* sort

* sort in parallel.

* fix a bug in sorting adj

* rename.

* add more comments.

* accelerate GetData

* fix tests.

* avoid sorting multiple times.

* add test.

* change back.

* sort.

* add sort_csr.

* Fix a bug.

* fix.

* revert modifcation.

* rename

* speed up EdgeIds.

* Add HAN

* Fix

* WIP; load raw ACM dataset

* DGL's own preprocessing with metapath coalescer

* various fixes

* comparison against simple logistic regression

* rename

* fix test

* 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

* metapath sampler

* lint & fixes

* lint x2

* lint x3

* fix windows

* remove max_cycle argument

* add todo note

* optimizer for sampling all negative edges.

* allow to disable checking false negative.

* fix lint.

* fix docstring.

* fix for comment.

* add comments.

* find the existence of negative edges.

* add comment.

* fix test.

* add transform

* lint

* lint

* fix

* fixmx

* fix

* add test

* fix typo

* fix default num_classes

* change to non-inplace operation

* fix lint

* fix

* WIP. remove graph arg in NodeBatch and EdgeBatch

* refactor: use graph adapter for scheduler

* WIP: recv

* draft impl

* stuck at bipartite

* bipartite->unitgraph; support dsttype == srctype

* pass test_query

* pass test_query

* pass test_view

* test apply

* pass udf message passing tests

* pass quan's test using builtins

* WIP: wildcard slicing

* new construct methods

* broken

* good

* add stack cross reducer

* fix bug; fix mx

* fix bug in csrmm2 when the CSR is not square

* lint

* removed FlattenedHeteroGraph class

* WIP

* prop nodes, prop edges, filter nodes/edges

* add DGLGraph tests to heterograph. Fix several bugs

* finish nx<->hetero graph conversion

* create bipartite from nx

* more spec on hetero/homo conversion

* silly fixes

* check node and edge types

* repr

* to api

* adj APIs

* inc

* fix some lints and bugs

* fix some lints

* hetero/homo conversion

* fix flat...

* 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...

* PBG negative edge sampler.

* add a positive edge to make it regular, handle last batch.

* exclude all positive edges in the parent graph.

* just uniformly sample negative nodes.

* fix lint.

* shuffle one-side nodes of positive edges.

* just uniformly sample negative nodes.

* change the data type.

* address comment.

* remove commented code.

* update

* speedup

* add some comments

* 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

*...

* upd

* add test

* fix

* upd

* merge

* hotfix

* upd

* fix

* upd

* up

* upd

* upd

* 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