Solution: Using SkyPilot's Multi-Cloud Task Deployment Feature
Background: Cloud Vendor Lock-in can limit subscriber choice in pricing, features and services, which SkyPilot effectively addresses by supporting 14+ cloud services through a unified interface.
- procedure
- Specify the desired cloud provider when installing SkyPilot, such as when you want to support both AWS and GCP:
pip install "skypilot[aws,gcp]" - In the YAML configuration file do not specify the
cloudfield, the system will automatically select the optimal cloud platform; or use the--cloud cheapestParameters actively require cost-optimized choices - For mission-critical jobs, the configuration can be added to the
clouds: [aws, gcp]Whitelisting, prohibiting the use of other clouds
- Specify the desired cloud provider when installing SkyPilot, such as when you want to support both AWS and GCP:
- Advanced Techniques
- pass (a bill or inspection etc)
sky show-gpusComparing GPU provisioning across clouds - utilization
sky status --cloud allView resource utilization for all cloud platforms - Configure multiple sets of cloud credentials in automated processes such as GitLab CI/CD for seamless switching
- pass (a bill or inspection etc)
Effectiveness: Users get 3-6.5x cost savings and automatic switching to other platforms in the event of a regional outage of one cloud service.
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 avoid lock-in with a single cloud vendor and enable flexible deployment of AI tasks in multi-cloud environments?
