SkyPilot abstracts away cloud infrastructure burdens so you can quickly launch, queue, and run AI jobs and clusters on any cloud or region for maximum cost savings and GPU availability.
SkyPilot abstracts away cloud infrastructure complexity so you can quickly launch, queue, and run ML jobs and clusters on any cloud or region you have access to.
It avoids vendor lock-in and enables maximum cost savings and GPU availability.
- 🚄 Launch jobs & auto-scale clusters on any cloud (AWS, GCP, Azure, etc)
- 💽 Easy access to cloud storage (S3, GCS, etc)
- ⚡️ Maximizes GPU availability across regions & clouds
- 💰 Managed Spot for 3-6x cost savings
📉 Optimizer finds cheapest VM/zone/region/cloud
🔋 Handles preemptions and auto-recovers jobs
🛑 Autostop for automatic cleanup of idle resources
Whether you need more GPUs, want to avoid lock-in, or reduce costs, SkyPilot makes using the cloud(s) easier and more efficient for ML engineers. The open-source project has helped many teams improve their workflows. 😊
💡 Avoid Vendor Lock-in: SkyPilot lets you easily switch between clouds and regions without changing your code. It prevents lock-in to any single cloud provider.
💰 Save on Costs: Features like Managed Spot (3-6x savings!), Optimizer, and Autostop help cut cloud bills substantially. Less money spent on infrastructure means more budget for model experiments.
⚡ Speed Up Experiments: SkyPilot makes scaling out faster and simplifies using specialized hardware (TPUs, latest GPUs) across clouds. Run more experiments at the same time.
📈 Improve GPU Availability: SkyPilot automatically finds GPUs across regions and clouds. Expanding the search space gets you resources faster. No more waiting around for GPU quota!
🚦 Reduce Operational Burden: SkyPilot handles cluster setup, data syncing, queueing jobs, monitoring, etc. You focus on building AI while SkyPilot takes care of the operations.
When working with cloud infrastructure, SkyPilot saves AI engineers time, money, and headaches. Its cloud and region abstractions, cost optimizations, and managed job execution let you focus on building models rather than wrestling with infrastructure.
- 👷🏽♀️ Builders: Zhanghao Wu, Zongheng Yang, Romil Bhardwaj, Wei-Lin Chiang
- 👩🏽💼 Builders on LinkedIn: https://www.linkedin.com/in/zhanghaowu/, https://www.linkedin.com/in/zonghengyang/, https://www.linkedin.com/in/romilb/, https://www.linkedin.com/in/wei-lin-chiang-51b025b2/
- 👩🏽🏭 Builders on X: https://twitter.com/zongheng_yang, https://twitter.com/infwinston
- 💾 Used in XXX repositories
- 👩🏽💻 Contributors: 60
- 💫 GitHub Stars: 4.9k
- 🍴 Forks: 315
- 👁️ Watch: 62
- 🪪 License: Apache-2.0
- 🔗 Links: Below 👇🏽
- GitHub Repository: https://github.com/skypilot-org/skypilot
- Official Website: https://blog.skypilot.co/
- Official Documentation: https://skypilot.readthedocs.io/en/latest/
- Slack Community: http://slack.skypilot.co/
- X Page: https://twitter.com/skypilot_org
- Profile in The AI Engineer: https://github.com/theaiengineer/awesome-opensource-ai-engineering/blob/main/libraries/skypilot/README.md
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