Solution: Leveraging SkyPilot's GPU Scheduling and Cost Optimization Capabilities
Background: High-end GPUs such as NVIDIA A100 can have price differences of up to 300% in different cloud regions and often face out-of-stock issues.
- Core Programs
- Spot instances are automatically managed: Add when starting a task
--use-spotparameter, the system automatically uses Spot instances with a low price of 60-90% and reschedules them in case of an outage - Global Resource View: Implementation
sky show-gpusView real-time GPU type/price/inventory for all cloud regions - fault tolerance mechanism: The system automatically tries this when the preferred GPU is out of stock:
- Other regions on the same platform
- Other Cloud Service Providers
- Alternative GPU models with similar performance
- Spot instances are automatically managed: Add when starting a task
- Practice Recommendations
- Setting up alternative resources in YAML such as
accelerators: [A100:1, T4:2]Indicates priority A100, followed by 2 T4 replacements - For long missions, it is recommended to pair
--cloud spot-check-interval 300Parameters check Spot instance status every 5 minutes - utilization
resources.disk_sizeConfiguration of large-capacity temporary storage to avoid data loss due to zone change
- Setting up alternative resources in YAML such as
Effectiveness: actual tests show that this approach can reduce the cost of a 100-hour A100 training task from $300 to $50, and the task success rate remains above 98%.
This answer comes from the articleSkyPilot: an open-source framework for efficiently running AI and batch tasks in any cloudThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of scarce and expensive GPU resources to achieve stable operation of AI training tasks?
