UC Berkely Does Skypilot

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UC Berkeley Launches SkyPilot to Help Navigate Soaring Cloud Costs

By Jaime Hampton, Datanami

Runaway cloud computing costs can stifle machine learning and data science projects, and many organizations are using multiple public clouds for different purposes to save money. However, a multi-cloud approach can add significant complexity, since not everyone is a cloud infrastructure expert.

To address this, researchers at U.C. Berkeley’s Sky Computing Lab have launched SkyPilot, an open source framework for running ML and Data Science batch jobs on any cloud, or multiple clouds, with a single cloud-agnostic interface.

SkyPilot uses an algorithm to determine which cloud zone or service provider is the most cost-effective for a given project. The program considers a workload’s resource requirements (whether it needs CPUs, GPUs, or TPUs) and then automatically determines which locations (zone/region/cloud) have available compute resources to complete the job before sending it to the least expensive option to execute.

The solution automates some of the more challenging aspects of running workloads on the cloud. SkyPilot’s makers say the program can reliably provision a cluster with automatic failover to other locations if capacity or quota errors occur, it can sync user code and files from local or cloud buckets to the cluster, and it can manage job queueing and execution. The researchers claim this comes with substantially reduced costs, sometimes by more than 3x.

SkyPilot developer and postdoctoral researcher Zongheng Yang said in a blog post that the growing trend of multi-cloud and multi-region strategies led the team to build SkyPilot, calling it an “intercloud broker.” He notes that organizations are strategically choosing a multi-cloud approach for higher reliability, avoiding cloud vendor lock-in, and stronger negotiation leverage, to name a few reasons.

To save costs, SkyPilot leverages the large price differences between cloud providers for similar hardware resources. Yang gives the example of Nvidia A100 GPUs, and how Azure currently offers the cheapest A100 instances, but Google Cloud and AWS charge a premium of 8% and 20% for the same computing power. For CPUs, some price differences can be over 50%.

Specialized hardware is also a reason to shop around, as many cloud providers are now offering custom options for different workloads. For example, Google Cloud offers TPUs for ML training, AWS has Inferentia for ML inference and Graviton processors for CPU workloads, and Azure provides Intel SGX codes for confidential computing. Scarcity of these specialized resources is also a reason for using multiple clouds, as high-end GPUs are frequently unavailable with long wait times.  ...