microsoft / microsoft/Graphormer
Very slow training
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- Dominant language
- Python
- Stars
- 2.5k
- Forks
- 374
- PR merge metrics
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Description
I am training graphormer-slim on a custom dataset with 5K graphs and the loss is coming down nicely. However, the GPU utilization is close to zero while the CPU processes (16) are very busy, which means that the CPU is the bottleneck and I could be training much faster...
Is there anyway to make the CPU side faster?
details:
graphs in DGL format
50-100 nodes per graph
1-3 edges per node
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First steps
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Research direction
No files, tests, or entry points are named. Start by reproducing training on the described 5K-graph DGL dataset and profiling CPU work versus GPU utilization; done means identifying a specific CPU bottleneck and demonstrating faster training or clearly documenting the limiting step.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100