A three-tier optimization strategy for cost control
Costs can be significantly reduced by optimizing the combination of resource allocation, training strategy, and monitoring and management:
- Optimization of resource allocation::
- Use a single GPU configuration (e.g. T4 16G) for pre-testing, then switch to multiple cards for formal training.
- Utilize "evaluation tools" to verify the effect of small samples first to avoid ineffective training.
- Training process optimization::
- Train with mixed precision (add torch.cuda.amp auto-hybridization module to code)
- Set the Early Stopping mechanism to monitor loss changes and automatically terminate the task if the threshold is exceeded.
- Reduce GPU memory footprint using gradient accumulation for large-scale data
- Resource monitoring and management::
- Regularly check the GPU hourly consumption report in the Billing Manager.
- Setting up usage alerts (three alerts of 10/20/30 hours per month)
- Avoid double counting by utilizing the breakpoint function of "Task Management".
Advanced Solution: For long-term tasks, you can use bidding instances (need to be turned on in the "Cloud Training" advanced settings), and the cost can be reduced by 40-60%.
This answer comes from the articleVolcano Ark: Big Model Training and Cloud Computing Service, Sign Up for $150 Equivalent ArithmeticThe
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