Optimization Strategies for Resource Constrained Environments
Hierarchical processing and technology combination programs are available to address hardware limitations:
- Chunking technology::
1. Utilization--config "small"The parameters first generate the subgraph
2. Adoption--chunk_size 5000Control the number of nodes in a single process
3. Usemerge_graphs.pyScript Post Splicing - Resource optimization::
- enable--low_memoryMode (to be modified)main.py(line 47)
- shallpython main.pychange intopython -O main.pyEnabling Optimized Compilation
- Limiting memory usage in Docker--memory=8g
Alternatives:
1. Cloud deployment: configure the environment in Google Colab, utilizing free T4 GPUs
2. Model downgrade: change togpt-3.5-turboAlternative to a larger model
3. Delayed generation: settings--interval 0.5Reducing the frequency of requests
Key configuration adjustments:
- modifyrequirements.txtneutralizationpython-igraphsubstitute fornetworkx
- existstart_launchers.pyDecrease innum_workersquantities
- Turn off visual intermediate processes to save memory
This answer comes from the articleGAG: Generating a Social Relationship Graph Using a Large Model to Simulate Human BehaviorThe
May not be reproduced without permission:AI productivity tools " How to overcome the performance bottleneck of running GraphAgent to generate large images on low configuration computers?
