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Change log | ||
========== | ||
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Here list all notable changes in GraphVite library. | ||
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v0.2.0 - 2019-10-11 | ||
------------------- | ||
- Add scalable multi-GPU prediction for node embedding and knowledge graph embedding. | ||
Evaluation on link prediction is 4.6x faster than v0.1.0. | ||
- New demo dataset `math` and entity prediction evaluation for knowledge graph. | ||
- Support Kepler and Turing GPU architectures. | ||
- Automatically choose the best episode size with regrad to RAM limit. | ||
- Add template config files for applications. | ||
- Change the update of global embeddings from average to accumulation. Fix a serious | ||
numeric problem in the update. | ||
- Move file format settings from graph to application. Now one can customize formats | ||
and use comments in evaluation files. Add document for data format. | ||
- Separate GPU implementation into training routines and models. Routines are in | ||
`include/instance/gpu/*` and models are in `include/instance/model/*`. | ||
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v0.1.0 - 2019-08-05 | ||
------------------- | ||
- Multi-GPU training of large-scale graph embedding | ||
- 3 applications: node embedding, knowledge graph embedding and graph & | ||
high-dimensional data visualization | ||
- Node embedding | ||
- Model: DeepWalk, LINE, node2vec | ||
- Evaluation: node classification, link prediction | ||
- Knowledge graph embedding | ||
- Model: TransE, DistMult, ComplEx, SimplE, RotatE | ||
- Evaluation: link prediction | ||
- Graph & High-dimensional data visualization | ||
- Model: LargeVis | ||
- Evaluation: visualization(2D / 3D), animation(3D), hierarchy(2D) |
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@@ -17,6 +17,7 @@ conda-forge::easydict | |
six | ||
future | ||
imageio | ||
psutil | ||
scipy | ||
matplotlib | ||
pytorch | ||
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application: | ||
knowledge graph | ||
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resource: | ||
gpus: [0] | ||
cpu_per_gpu: 8 | ||
dim: 512 | ||
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graph: | ||
file_name: <math.train> | ||
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build: | ||
optimizer: | ||
type: Adam | ||
lr: 5.0e-3 | ||
weight_decay: 0 | ||
num_partition: auto | ||
num_negative: 8 | ||
batch_size: 100000 | ||
episode_size: 100 | ||
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train: | ||
model: RotatE | ||
num_epoch: 2000 | ||
margin: 9 | ||
sample_batch_size: 2000 | ||
adversarial_temperature: 2 | ||
log_frequency: 100 | ||
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evaluate: | ||
task: link prediction | ||
file_name: <math.test> | ||
filter_files: | ||
- <math.train> | ||
- <math.valid> | ||
- <math.test> | ||
target: tail | ||
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save: | ||
file_name: rotate_math.pkl |
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