Comments (1)
Hi Donnie,
wn18 has more entities than fb15k, so it takes more GPU memory. To reproduce the results on wn18, the recommended command is in best_config.sh:
bash run.sh train RotatE wn18 0 0 512 1024 500 12.0 0.5 0.0001 80000 8 -de
It's equivalent to:
CUDA_VISIBLE_DEVICES=$GPU_DEVICE python -u codes/run.py --do_train
--cuda
--do_valid
--do_test
--data_path data/wn18
--model RotatE
-n 1024 -b 512 -d 500
-g 12.0 -a 0.5 -adv
-lr 0.0001 --max_steps 80000
-save $SAVE --test_batch_size 8
-de
from knowledgegraphembedding.
Related Issues (20)
- GPU Out Of Memory HOT 2
- Pretrained models
- RuntimeError: The size of tensor a (2000) must match the size of tensor b (1000) at non-singleton dimension 2 HOT 3
- confused about implementation of DistMult HOT 1
- how do you generate the embedding? Is it a random walk or something? I mean what is the main algorithm for embedding. HOT 1
- How to split the train/valid/test HOT 1
- When I train a more huge dataset by model RotatE, always show the question "CUDA out of memory" ? HOT 1
- N-1 and 1-N relations HOT 3
- hi,a priblem about multi GPU training HOT 1
- confused about implementation of 1-1, 1-n, n-1, n-n HOT 1
- regarding RELATION CATEGORY in FB15k HOT 1
- Regarding the direction of the rotation HOT 1
- Init embedding random OR by bert encode from entity description text HOT 1
- CUDA out of memory (resolved) and method to make RotatE run faster HOT 2
- L3 regularization
- Memory consumption issue HOT 1
- How to designate dimension of embeddings
- Three questions HOT 1
- Why start = 4
- "IndexError: index out of range in self" when I train on my own knowledge graph. Can anyone help me with this? Thanks a lot!
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