Three effective solutions to model training resource constraints
Qlib provides flexible solutions for different scenarios with resource constraints:
- Lightweight model selection: Priority use
LGBModeland other decision tree-based models, with 5-10 times faster training speed compared to deep learning models, and 80% lower memory consumption. can be achieved bynum_threadsparameter controls the number of CPU cores used. - Cloud Deployment Solutions: Deploy Qlib-Server on AWS or Azure to leverage cloud service elastic compute resources. docker images.
microsoft/qlib-serverAll dependencies have been pre-installed and are ready for distributed training upon startup. - Incremental training techniques: For ongoing learning needs, the
model.fitThe incremental training mode, which loads only the latest data each time, avoids repeated processing of the full amount of data.Qlib's DatasetH class naturally supports this training method.
Resource optimization suggestion: use small sample data (e.g., the first 100 stocks) to quickly iterate the algorithm logic during the local development phase, and then switch to the full amount of data during complete training. At the same time, reasonable settingsearly_stopping_roundsAvoid invalid calculations.
This answer comes from the articleQlib: an AI quantitative investment research tool developed by MicrosoftThe
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