Comments (1)
The memory cost is influenced by many factors. The most important factor is the number of historical facts. If your dataset has so many facts at one timestamp and you use a long history. The "cuda out of memory" problem may appear. The simplest way to this problem is to decrease the length of history snapshots you modeling. And maybe adding a negative sampling strategy to RE-GCN is also a common solution.
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Related Issues (19)
- Ask for codes HOT 1
- Adaptation to a "different" static graph HOT 3
- OSError: libcublas.so.10: cannot open shared object file: No such file or directory HOT 1
- OSError: [Errno 22] Invalid argument: '../models/ICEWS14s-uvrgcn-convtranse-ly2-dilate1-his3-weight:0.5-discount:1.0-angle:10-dp0.2|0.2|0.2|0.2-gpu0'
- Confusion about the experiment HOT 1
- command line for running on YAGO and wiki HOT 1
- The results for WIKI dataset are different from the paper HOT 3
- 你好,请问以下下面的错误怎么解决? HOT 1
- 关于论文中报告的数据集信息和代码公开的数据集统计信息不一致 HOT 1
- what are raw, filter, and RE-GCN w. GT
- Filtered Result HOT 1
- Confusion about the experimental setup HOT 8
- Confusion about dataset and experiment HOT 3
- Confusion about baseline HOT 1
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- confusion about dataset HOT 2
- confusion about parameter(skip_connect) HOT 1
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