SkyPilot

AI compute management software runs workloads across your Kubernetes and Slurm clusters or cloud accounts, with job recovery and GPU, TPU and CPU support.

SkyPilot manages AI training, inference and development workloads across infrastructure you control. It's for AI teams sharing compute and infrastructure teams managing jobs across clusters and cloud providers.

Portable workloads: Keep the environment and job together in a portable definition, then move workloads between supported infrastructure without changing their code.
Job scheduling and recovery: Queue jobs, recover failed runs and fall back to another provider when compute capacity isn't available.
Distributed jobs: Run multi-node workloads with scheduling that allocates their required resources together, across clusters managed through one control plane.
Compute utilization: Pack workloads onto shared clusters, automatically stop idle resources and select infrastructure based on availability and cost.
Interactive development: Connect an IDE to Kubernetes pods, sync code and use SSH for work alongside scheduled jobs.

SkyPilot is open source under Apache 2.0. It supports GPUs, TPUs and CPUs on Kubernetes, Slurm and cloud providers including AWS, GCP, Azure, CoreWeave and RunPod. Workloads run within your own accounts, VPCs and clusters; choosing a cloud provider places the compute in that account rather than on your local machine.

GPU Management and Monitoring Kubernetes and Cloud-Native AI Code execution Distributed execution Multi-user access

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SkyPilot setup with marimo, Pydantic and PyTorch

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SkyPilot

AI compute management software runs workloads across your Kubernetes and Slurm clusters or cloud accounts, with job recovery and GPU, TPU and CPU support.