wehub-resource-sync
78 lines
2.1 KiB
Markdown
78 lines
2.1 KiB
Markdown
DGL release and change logs
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==========
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Refer to the roadmap issue for the on-going versions and features.
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0.2
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---
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Major release that includes many features, bugfix and performance improvement.
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Speed of GCN model on Pubmed dataset has been improved by **4.19x**! Speed of
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RGCN model on Mutag dataset has been improved by **7.35x**! Important new
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feature: **graph sampling APIs**.
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Update details:
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# Model examples
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- [x] TreeLSTM w/ MXNet (PR #279 by @szha )
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- [x] GraphSage (@ZiyueHuang )
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- [x] Improve GAT model speed (PR #348 by @jermainewang )
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# Core system improvement
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- [x] Immutable CSR graph structure (PR #342 by @zheng-da )
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- [x] Finish remaining functionality (Issue #369, PR #404 by @yzh119)
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- [x] Nodeflow data structure (PR #361 by @zheng-da )
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- [x] Neighbor sampler (PR #322 )
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- [x] Layer-wise sampler (PR #362 by @GaiYu0 )
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- [x] Multi-GPU support by data parallelism (PR #356 #338 by @ylfdq1118 )
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- [x] More dataset:
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- [x] Reddit dataset loader (PR #372 by @ZiyueHuang )
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- [x] PPI dataset loader (PR #395 by @sufeidechabei )
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- [x] Mini graph classification dataset (PR #364 by @mufeili )
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- [x] NN modules (PR #406 by @jermainewang @mufeili)
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- [x] GraphConv layer
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- [x] Edge softmax layer
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- [x] Edge group apply API (PR #358 by @VoVAllen )
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- [x] Reversed graph and transform.py module (PR #331 by @mufeili )
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- [x] Max readout (PR #341 by @mufeili )
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- [x] Random walk APIs (PR #392 by @BarclayII )
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# Tutorial/Blog
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- [x] Batched graph classification in DGL (PR #360 by @mufeili )
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- [x] Understanding GAT (@sufeidechabei )
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# Project improvement
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- [x] Python lint check (PR #330 by @jermainewang )
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- [x] Win CI (PR #324 by @BarclayII )
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- [x] Auto doc build (by @VoVAllen )
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- [x] Unify tests for different backends (PR #333 by @BarclayII )
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0.1.3
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-----
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Bug fix
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* Compatible with Pytorch v1.0
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* Bug fix in networkx graph conversion.
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0.1.2
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-----
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First open release.
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* Basic graph APIs.
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* Basic message passing APIs.
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* Pytorch backend.
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* MXNet backend.
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* Optimization using SPMV.
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* Model examples w/ Pytorch:
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- GCN
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- GAT
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- JTNN
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- DGMG
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- Capsule
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- LGNN
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- RGCN
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- Transformer
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- TreeLSTM
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* Model examples w/ MXNet:
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- GCN
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- GAT
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- RGCN
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- SSE
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