GitLab

* 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

* 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

* networkx >= 2.4 will break our examples

* Update tutorials/requirements

* fix selfloop edges

* upd version

* init

* rm data

* README

* fix rating values that are not decimal

* rm stale codes

* small fix

* upd

* rewrite decoder

* fix many

* many fix; performance matched

* upd; handle sparse input

* upd

* address comments

* more docstring; download data automatically

* shared param mode

* update

* speedup

* add some comments

* upd

* add test

* fix

* upd

* merge

* hotfix

* upd

* 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

* 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

* refactor

* upd mpnn

* rgcn module

* support id input

* WIP: model codes

* use faster index select

* dropout

* self loop

* WIP: link prediction

* fix lint

* WIP: docs

* docstring

* docstring

* merge two child classes

* mxnet rgcn module

* fix lint

* fix lint

* fix rename bug

* add uniform edge sampler

* fix fn name

* docstring

* fix mxnet rgcn module

* fix mx rgcn

* enable test on cuda

* Add early stop

* add mxnet version

* Poke ci

* copy graph index to shared memory.

* fix.

* fix.

* fix.

* use a diff name for in-csr and out-csr.

* fix lint.

* remove print.

* add test.

* add comments.

* enable tutorial test in CI.

* extand DGLGraph graph_data.

* update doc.

* Revert "enable tutorial test in CI."

This reverts commit cd774067180922bb6ae979bde4aecbffc61c8147.

* accept DGLGraph in graph store.

* add tests.

* distinguish layer-local nid and nodeflow nid.

* use numpy assert_array_equal and assert_allclose

* fix map_from_parent_nid

* fix test

* fix test.

* renmae remap.

* update doc.

* update doc.

* update doc.

* fix test.

* fix test.

* all pytorch examples

* scan through mxnet examples

* change reddit data

* tweak numerical range for unittest

* fix ci

* fix ci

* fix

* add seed to workaround

* fix gat code to use latest edge softmax module

* avoid transpose

* update README

* use edge_softmax op

* mxnet edge softmax op

* mxnet gat

* update README

* fix unittest

* fix ci

* fix mxnet nn test; relax criteria for prod reducer

* to simple

* WIP: multigraph flag

* graph index refactor; pass basic testing

* graph index refactor; pass basic testing

* fix bug in to_simple; pass torch test

* fix mx utest

* fix example

* fix lint

* fix ci

* poke ci

* poke ci

* WIP

* poke ci

* poke ci

* poke ci

* change ci workspace

* poke ci

* poke ci

* poke ci

* poke ci

* delete ci

* use enum for multigraph flag

* Update README.md for the demo of distributed sampler

* Update README.MD of demo for distributed sampler

* update

* update

* update demo

* load mxnet csr.

* enable load large csr.

* fix

* fix.

* fix int overflow.

* fix test.

* fix.

* use inplace.

* move to shared memory graph store.

* fix.

* add more unit tests.

* fix.

* fix test.

* fix test.

* disable test.

* fix.

* update

* update

* re-use single-machine code

* update

* use relative path

* update

* update

* update

* add __init__.py

* add __init__.py

* import sys, os

* fix typo

* update

* update

* update

* update

* update num_hops

* fix bug

* update

* report numbers of distributed training in AMLC giant graph paper

* add init_ndata and init_edata in DGLGraph.

* adjust SharedMemoryGraph API.

* print warning.

* fix comment.

* update example

* fix.

* fix examples.

* add unit tests.

* add comments.

* fix.

* add comment.

* remove.

* temp fix.

* initialize for shared memory.

* fix graphsage.

* fix gcn.

* add more unit tests.

* add more tests.

* avoid creating shared-memory exclusively.

* redefine remote initializer.

* improve initializer.

* fix unit test.

* fix lint.

* fix lint.

* initialize data in the graph store server properly.

* fix test.

* fix test.

* fix test.

* small fix.

* add comments.

* cleanup server.

* test graph store with a random port.

* print.

* print to stderr.

* test1

* test2

* remove comment.

* adjust the initializer signature.

* reorganize sampling code.

* add multi-process training.

* speed up gcn_cv

* fix graphsage_cv.

* add new API in graph store.

* update barrier impl.

* support both local and distributed training.

* fix multiprocess train.

* fix.

* fix barrier.

* add script for loading data.

* multiprocessing sampling.

* accel training.

* replace pull with spmv for speedup.

* nodeflow copy from parent with context.

* enable GPU.

* fix a bug in graph store.

* enable multi-GPU training.

* fix lint.

* add comments.

* rename to run_store_server.py

* fix gcn_cv.

* fix a minor bug in sampler.

* handle error better in graph store.

* improve graphsage_cv for distributed mode.

* update README.

* fix.

* update.

* Change Distributed Sampler API

* fix lint

* fix lint

* update demo

* update

* update

* update

* update demo

* update demo

* reorganize sampling code.

* speedup gcn_ns.

* speed up gcn_cv

* fix graphsage_cv.

* undo the modification.

* accel training.

* update readme.

* Refactoring network API

* update demo

* update

* update demo

* update demo

* add num_sender

* update

* fix lint

* fix lint

* fix lint

* update

* Update gcn_mp.py

* Update train.py

* Update train.py

* Update train.py

* Update gat_batch.py

* Update gat_batch.py

* Update gcn_mp.py

* add C++ rpc infrastructure and distributed sampler

* update

* update lint

* update lint

* update lint

* update

* update

* update

* updare

* update

* update

* update

* update serialize and unittest

* update serialize

* lint

* update

* update

* update

* update

* update

* update

* update unittest

* put Finalize() to __del__

* update unittest

* update

* delete buffer in Finalize

* update unittest

* update unittest

* update unittest

* update unittest

* update

* update

* fix small bug

* windows socket impl

* update API

* fix bug in serialize

* fix bug in serialzie

* set parent graph

* update

* update

* update

* update

* update

* update

* fix lint

* fix lint

* fix

* fix windows compilation error

* fix windows error

* change API to lower-case

* update test

* fix typo

* update

* add SamplerPool

* add SamplerPool

* update

* update test

* update

* update

* update

* update

* add example

* update

* update

* add distributed sampler demo

* add index

* update demo of distributed sampler

* fix lower-case

* print subg index

* update README.md

* update

* remove --gpu args

* enable cython

* add helper function and data structure for void_p vector return

* move sampler from graph index to contrib.sampling

* WIP

* WIP

* refactor layer sampling

* pass tests

* fix lint

* fix graphsage

* remove comments

* pickle test

* fix comments

* update dev guide for cython build

* don't return aux_info.

* fix sampler test.

* fix sse.

* fix.

* add comment.