* initial version from distributed training.
This is copied from multiprocessing training.
* modify for distributed training.
* it's runnable now.
* measure time in neighbor sampling.
* simplify neighbor sampling.
* fix a bug in distributed neighbor sampling.
* allow single-machine training.
* fix a bug.
* fix a bug.
* fix openmp.
* make some improvement.
* fix.
* add prepare in the sampler.
* prepare nodeflow async.
* fix a bug.
* get id.
* simplify the code.
* improve.
* fix partition.py
* fix the example.
* add more features.
* fix the example.
* allow one partition
* use distributed kvstore.
* do g2l map manually.
* fix commandline.
* a temp script to save reddit.
* fix pull_handler.
* add pytorch version.
* estimate the time for copying data.
* delete unused code.
* fix a bug.
* print id.
* fix a bug
* fix a bug
* fix a bug.
* remove redundent code.
* revert modify in sampler.
* fix temp script.
* remove pytorch version.
* fix.
* distributed training with pytorch.
* add distributed graph store.
* fix.
* add metis_partition_assignment.
* fix a few bugs in distributed graph store.
* fix test.
* fix bugs in distributed graph store.
* fix tests.
* remove code of defining DistGraphStore.
* fix partition.
* fix example.
* update run.sh.
* only read necessary node data.
* batching data fetch of multiple NodeFlows.
* simplify gcn.
* remove unnecessary code.
* use the new copy_from_kvstore.
* update training script.
* print time in graphsage.
* make distributed training runnable.
* use val_nid.
* fix train_sampling.
* add distributed training.
* add run.sh
* add more timing.
* fix a bug.
* save graph metadata when partition.
* create ndata and edata in distributed graph store.
* add timing in minibatch training of GraphSage.
* use pytorch distributed.
* add checks.
* fix a bug in global vs. local ids.
* remove fast pull
* fix a compile error.
* update and add new APIs.
* implement more methods in DistGraphStore.
* update more APIs.
* rename it to DistGraph.
* rename to DistTensor
* remove some unnecessary API.
* remove unnecessary files.
* revert changes in sampler.
* Revert "simplify gcn."
This reverts commit 0ed3a34ca714203a5b45240af71555d4227ce452.
* Revert "simplify neighbor sampling."
This reverts commit 551c72d20f05a029360ba97f312c7a7a578aacec.
* Revert "measure time in neighbor sampling."
This reverts commit 63ae80c7b402bb626e24acbbc8fdfe9fffd0bc64.
* Revert "add timing in minibatch training of GraphSage."
This reverts commit e59dc8957a414c7df5c316f51d78bce822bdef5e.
* Revert "fix train_sampling."
This reverts commit ea6aea9a4aabb8ba0ff63070aa51e7ca81536ad9.
* fix lint.
* add comments and small update.
* add more comments.
* add more unit tests and fix bugs.
* check the existence of shared-mem graph index.
* use new partitioned graph storage.
* fix bugs.
* print error in fast pull.
* fix lint
* fix a compile error.
* save absolute path after partitioning.
* small fixes in the example
* Revert "[kvstore] support any data type for init_data() (#1465)"
This reverts commit 87b6997bf2.
* fix a bug.
* disable evaluation.
* Revert "Revert "[kvstore] support any data type for init_data() (#1465)""
This reverts commit f5b8039c6326eb73bad8287db3d30d93175e5bee.
* support set and init data.
* support set and init data.
* Revert "Revert "[kvstore] support any data type for init_data() (#1465)""
This reverts commit f5b8039c6326eb73bad8287db3d30d93175e5bee.
* fix bugs.
* fix unit test.
* move to dgl.distributed.
* fix lint.
* fix lint.
* remove local_nids.
* fix lint.
* fix test.
* remove train_dist.
* revert train_sampling.
* rename funcs.
* address comments.
* address comments.
Use NodeDataView/EdgeDataView to keep track of data.
* address comments.
* address comments.
* revert.
* save data with DGL serializer.
* use the right way of getting shape.
* fix lint.
* address comments.
* address comments.
* fix an error in mxnet.
* address comments.
* add edge_map.
* add more test and fix bugs.
Co-authored-by: Zheng <dzzhen@186590dc80ff.ant.amazon.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-6-131.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-26-167.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-150.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-250.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-30-135.us-west-2.compute.internal>
* Deprecate multi-graph
* Handle heterograph and edge_ids
* lint
* Fix
* Remove multigraph in C++ end
* Fix lint
* Add some test and fix something
* Fix
* Fix
* upd
* Fix some test case
* Fix
* Fix
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Add weight based edge sampler
* Can run, edge weight work.
TODO: test node weight
* Fix node weight sample
* Fix y
* Update doc
* Fix syntex
* Fix
* Fix GPU test for sampler
* Fix test
* Fix
* Refactor EdgeSampler to act as class object not function that it
can record its own private states.
* clean
* Fix
* Fix
* Fix run bug on kg app
* update
* update test
* test
* Simply python API and fix some C code
* Fix
* Fix
* Fix syntex
* Fix
* Update API description
* add replacement for edge sampler
* Now edge sampler support replacement and no-replacement
* Fix
* Fix
* change kg/app to use edge sampler with replacement config
* Update replacement algo
* Fix syntax
* Update
* Update
Co-authored-by: Da Zheng <zhengda1936@gmail.com>
* Add weight based edge sampler
* Can run, edge weight work.
TODO: test node weight
* Fix node weight sample
* Fix y
* Update doc
* Fix syntex
* Fix
* Fix GPU test for sampler
* Fix test
* Fix
* Refactor EdgeSampler to act as class object not function that it
can record its own private states.
* clean
* Fix
* Fix
* Fix run bug on kg app
* update
* update test
* test
* Simply python API and fix some C code
* Fix
* Fix
* Fix syntex
* Fix
* Update API description
* 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
* new hetero RGCN
* bgs running
* fix gpu
* am dataset
* fix bug in label preparation
* Fix AM training; add result
* rm sym link
* new embed layer; mutag
* mutag matched; other fix
* minor fix
* dataset refactor
* new data loading
* rm old files
* refactor
* docstring
* include literal nodes in AIFB dataset
* address comments
* docstring
xiang song(charlie.song)