* Some functions used in the tutorial are deprecated
I'm receiving register_message_func and register_reduce_func are deprecated error. I have updated the code such that message and reduce functions are passed to g.send and g.recv
* Update 3_pagerank.py.bak
Co-authored-by: Zihao Ye <expye@outlook.com>
* traversal to new framework
* add new
* Fix compile
* Pass test
* keep old version
* lint
* lint
* Fix
* Fix
* Fix compatability with new master
* Fix test and tutorials
* Update according to comments
* Fix test
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.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>
* update work with different backend section
* fix some warnings
* Update backend.rst
* Update index.rst
Co-authored-by: VoVAllen <VoVAllen@users.noreply.github.com>
* [Doc] Tree-LSTM in DGL, edit pass for readability
Edit for grammar and style.
How about deleting the last line? "Besides, you..." It needs a transition or some context.
* Update tutorials/models/2_small_graph/3_tree-lstm.py
* Update tutorials/models/2_small_graph/3_tree-lstm.py
* Old wines new title, edit for grammar and style
new descriptive title here and an edit pass @aaronmarkham
* Update tutorials/models/4_old_wines/README.txt
* Edit for grammar and style
As with other tutorials, it would help the reader if you add a paragraph in the opening section about assumptions or prerequisites.
Does this refer to SageMaker Ground Truth feature? "...assigns its ground truth label..." If yes, phrase it thus: assigns its Amazon SageMaker Ground Truth label
* Update tutorials/basics/4_batch.py
* Update tutorials/basics/4_batch.py
* Update tutorials/basics/4_batch.py
topologies
* Update tutorials/basics/4_batch.py
Co-Authored-By: Aaron Markham <markhama@amazon.com>
* Edit for readability
Edit pass for grammar and style.
* A great value-add would be to provide assumptions and prerequisites in the opening section. This helps readers understand what they need to have in place in order to make use of your tutorial steps.
* The wikidata knowledge graph could be improved with a smaller font for the Zuckerberg circle.
* Update tutorials/hetero/1_basics.py
* Update tutorials/hetero/1_basics.py
Co-Authored-By: Aaron Markham <markhama@amazon.com>
* Update tutorials/hetero/1_basics.py
Co-Authored-By: Aaron Markham <markhama@amazon.com>
* Update tutorials/hetero/1_basics.py
Co-Authored-By: Aaron Markham <markhama@amazon.com>
* Update tutorials/hetero/1_basics.py
Co-Authored-By: Aaron Markham <markhama@amazon.com>
* Update tutorials/hetero/1_basics.py
Co-Authored-By: Aaron Markham <markhama@amazon.com>
Can you add a link for the download to this sentence: You can also `download <location?>` and run the different code examples...
As with other tutorial topics, it would be helpful to add your assumptions or information in the opening section about prerequisites.
* Edit for grammar and style
In the opening paragraph, it would be helpful to provide some overall scenario and the prerequisites you expect readers to have completed before they start here. "This tutorial assumes you have already..." Add a link to Install DGL topic perhaps and any other framework or IDE or even specialized knowledge. With this context, you help readers to succeed by setting expectations.
Better to group all tutorials (this, PageRank with, Batched Graph, Working with) at the same level in the left navigation rail and all as subsections of DGL Basics.
* Update tutorials/basics/2_basics.py
* 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
Zhou Fang