skypilot

Manage all your AI compute

SkyPilot is a system to run, manage, and scale AI workloads on any AI infrastructure. SkyPilot gives **AI teams** a simple interface to run jobs on any infra. **Infra teams** get a unified control plane to manage any AI compute — with advanced scheduling, scaling, and orchestration. ----- :fire: *News* :fire: - [Oct 2026] **SkyPilot AI Infra Meetup** with H Company in SF on Oct 8: [**register now**](https://partiful.com/e/kazBr6qiMhabcqdVm4nf) - [Sep 2026] **RL is Everything, Everywhere, All at Once**: [**blog**](https://skypilot.ai/blog/rl-everything) - [Sep 2026] **SkyPilot Agent Sessions**: Mission Control for Coding Agents, on Your Infrastructure: [**blog**](https://skypilot.ai/blog/agent-sessions) - [Jul 2026] **Serving Kimi K3 on your own GPUs with SkyPilot**: [**blog**](https://skypilot.ai/blog/kimi-k3) - [Jul 2026] **Run AI workloads on any cloud, store on Hugging Face**, no egress fees to worry about: [**Hugging Face blog**](https://huggingface.co/blog/skypilot-hf-storage) - [Jun 2026] **SkyPilot Endpoints**: production-ready inference on every cluster you own: [**blog**](https://skypilot.ai/blog/skypilot-endpoints) - [Jun 2026] **Announcing SkyPilot Sandboxes**: run untrusted, LLM-generated code on the Kubernetes clusters you already own. [**Learn more**](https://skypilot.ai/blog/sandboxes), [**join early access**](https://forms.gle/o4keAryXsVazNjyGA) - [May 2026] **How Multiverse doubled their GPU utilization with SkyPilot**: [**case study**](https://multiversecomputing.com/papers/2x-gpu-utilization-same-hardware-discover-our-efficiency-playbook) - [Apr 2026] Introducing **GPU Compass**: One dashboard to browse, compare pricing, and launch across every GPU cloud. Try it at [**gpus.skypilot.co**](https://gpus.skypilot.co). - [Apr 2026] **Research-Driven Agents**: Agents read arxiv papers before coding, landed 5 llama.cpp kernel fusions and +15% faster flash attention in ~3 hours for ~$29: [**blog**](https://skypilot.ai/blog/research-driven-agents), [**HackerNews**](https://news.ycombinator.com/item?id=47706141) - [Mar 2026] **Scaling Karpathy's Autoresearch**: Autoresearch runs 1 experiment at a time. We gave it 16 GPUs and let it run in parallel: [**blog**](https://skypilot.ai/blog/scaling-autoresearch), [**HackerNews**](https://news.ycombinator.com/item?id=47442435) - [Mar 2026] **How H Company Unlocked Online RL and Unified their AI Platform**: [**case study**](https://hcompany.ai/unlocking-online-rl-skypilot) ## Overview SkyPilot **is easy to use for AI users**: - Quickly spin up compute on your own infra - Environment and job as code — simple and portable - Easy job management: queue, run, and auto-recover many jobs SkyPilot **makes Kubernetes easy for AI & Infra teams**: - Slurm-like ease of use, cloud-native robustness - Local dev experience on K8s: SSH into pods, sync code, or connect IDE - Turbocharge your clusters: gang scheduling, multi-cluster, and scaling SkyPilot **unifies multiple clusters, clouds, and hardware**: - One interface to use reserved GPUs, Kubernetes clusters, Slurm clusters, or 20+ clouds - [Flexible provisioning](https://docs.skypilot.co/en/latest/examples/auto-failover.html) of GPUs, TPUs, CPUs, with smart failover - [Team deployment](https://docs.skypilot.co/en/latest/reference/api-server/api-server.html) and resource sharing SkyPilot **maximizes GPU fleet utilization**: * Autostop: automatic cleanup of idle resources * Binpacking: workload binpacking on shared clusters * Intelligent scheduler: automatically schedule on the most available infra SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes. Install with uv ([also supported](https://docs.skypilot.co/en/latest/getting-started/installation.html): pip, nightly, from source) ```bash # Choose your clouds: uv pip install "skypilot[kubernetes,aws,gcp,azure,oci,nebius,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,seeweb,shadeform,verda]" ``` To use SkyPilot directly with your agent (Claude Code, Codex, etc.), install the [SkyPilot Skill](https://docs.skypilot.co/en/latest/getting-started/skill.html). Tell your agent: ``` Fetch and follow https://github.com/skypilot-org/skypilot/blob/HEAD/agent/INSTALL.md to install the skypilot skill ```

Current supported infra: Kubernetes, Slurm, AWS, GCP, Azure, OCI, CoreWeave, Nebius, Lambda Cloud, RunPod, Fluidstack, Cudo, Digital Ocean, Paperspace, Cloudflare, Samsung, IBM, Vast.ai, VMware vSphere, Seeweb, Prime Intellect, Shadeform, Verda Cloud, VastData, Crusoe.

## Getting started [Install SkyPilot](https://docs.skypilot.co/en/latest/getting-started/installation.html) in 1 minute. Then, launch your first cluster in 2 minutes in [Quickstart](https://docs.skypilot.co/en/latest/getting-started/quickstart.html). SkyPilot is BYOC: Everything is launched within your cloud accounts, VPCs, and clusters. ## Benefits of SkyPilot on Kubernetes SkyPilot makes Kubernetes AI-native. It turbocharges your existing Kubernetes clusters by **accelerating AI/ML velocity**: - AI-friendly interface to launch jobs and deployments - Much simplified interactive dev for K8s (SSH / sync code / connect IDE to pods) ...and **optimizing GPU scheduling, utilization, and scaling**: - Advanced scheduling: Gang scheduling, multi-node jobs, and queueing - Multi-cluster support: Bring all your clusters under one control plane - Multi-cloud support: One consistent interface to manage many providers See [SkyPilot vs Vanilla Kubernetes](https://docs.skypilot.co/en/latest/reference/kubernetes/skypilot-and-vanilla-k8s.html) and this [blog post](https://skypilot.ai/blog/ai-on-kubernetes) for more details. ## SkyPilot in 1 minute A SkyPilot task specifies: resource requirements, data to be synced, setup commands, and the task commands. Once written in this [**unified interface**](https://docs.skypilot.co/en/latest/reference/yaml-spec.html) (YAML or Python API), the task can be launched on any available infra (Kubernetes, Slurm, cloud, etc.). This avoids vendor lock-in, and allows easily moving jobs to a different provider. Paste the following into a file `my_task.yaml`: ```yaml resources: accelerators: A100:8 # 8x NVIDIA A100 GPU num_nodes: 1 # Number of VMs to launch # Working directory (optional) containing the project codebase. # Its contents are synced to ~/sky_workdir/ on the cluster. workdir: ~/torch_examples # Commands to be run before executing the job. # Typical use: pip install -r requirements.txt, git clone, etc. setup: | cd mnist pip install -r requirements.txt # Commands to run as a job. # Typical use: launch the main program. run: | cd mnist python main.py --epochs 1 ``` Prepare the workdir by cloning: ```bash git clone https://github.com/pytorch/examples.git ~/torch_examples ``` Launch with `sky launch` (note: [access to GPU instances](https://docs.skypilot.co/en/latest/cloud-setup/quota.html) is needed for this example): ```bash sky launch my_task.yaml ``` SkyPilot then performs the heavy-lifting for you, including: 1. Find the cheapest & available infra across your clusters or clouds 2. Provision the GPUs (pods or VMs), with auto-failover if the infra returned capacity errors 3. Sync your local `workdir` to the provisioned cluster 4. Auto-install dependencies by running the task's `setup` commands 5. Run the task's `run` commands, and stream logs See [Quickstart](https://docs.skypilot.co/en/latest/getting-started/quickstart.html) to get started with SkyPilot. ## Runnable examples See [**SkyPilot examples**](https://docs.skypilot.co/en/docs-examples/examples/index.html) that cover: development, training, serving, LLM models, AI apps, and common frameworks. Latest featured examples: | Task | Examples | |----------|----------| | Training | [Verl](https://docs.skypilot.co/en/latest/examples/training/verl.html), [Finetune Llama 4](https://docs.skypilot.co/en/latest/examples/training/llama-4-finetuning.html), [TorchTitan](https://docs.skypilot.co/en/latest/examples/training/torchtitan.html), [PyTorch](https://docs.skypilot.co/en/latest/getting-started/tutorial.html), [DeepSpeed](https://docs.skypilot.co/en/latest/examples/training/deepspeed.html), [NeMo](https://docs.skypilot.co/en/latest/examples/training/nemo.html), [Ray](https://docs.skypilot.co/en/latest/examples/training/ray.html), [Unsloth](https://docs.skypilot.co/en/latest/examples/training/unsloth.html), [Jax/TPU](https://docs.skypilot.co/en/latest/examples/training/tpu.html), [OpenRLHF](https://docs.skypilot.co/en/latest/examples/training/openrlhf.html) | | Serving | [vLLM](https://docs.skypilot.co/en/latest/examples/serving/vllm.html), [SGLang](https://docs.skypilot.co/en/latest/examples/serving/sglang.html), [Ollama](https://docs.skypilot.co/en/latest/examples/serving/ollama.html) | | Models | [DeepSeek-R1](https://docs.skypilot.co/en/latest/examples/models/deepseek-r1.html), [Llama 4](https://docs.skypilot.co/en/latest/examples/models/llama-4.html), [Llama 3](https://docs.skypilot.co/en/latest/examples/models/llama-3.html), [CodeLlama](https://docs.skypilot.co/en/latest/examples/models/codellama.html), [Qwen](https://docs.skypilot.co/en/latest/examples/models/qwen.html), [Kimi-K2](https://docs.skypilot.co/en/latest/examples/models/kimi-k2.html), [Kimi-K2-Thinking](https://docs.skypilot.co/en/latest/examples/models/kimi-k2-thinking.html), [Mixtral](https://docs.skypilot.co/en/latest/examples/models/mixtral.html) | | AI apps | [RAG](https://docs.skypilot.co/en/latest/examples/applications/rag.html), [vector databases](https://docs.skypilot.co/en/latest/examples/applications/vector_database.html) (ChromaDB, CLIP) | | Common frameworks | [Airflow](https://docs.skypilot.co/en/latest/examples/frameworks/airflow.html), [Jupyter](https://docs.skypilot.co/en/latest/examples/frameworks/jupyter.html), [marimo](https://docs.skypilot.co/en/latest/examples/frameworks/marimo.html) | Source files can be found in [`llm/`](https://github.com/skypilot-org/skypilot/tree/master/llm) and [`examples/`](https://github.com/skypilot-org/skypilot/tree/master/examples). ## Learn more To learn more, see [SkyPilot Overview](https://docs.skypilot.co/en/latest/overview.html), [SkyPilot docs](https://docs.skypilot.co/en/latest/), and [SkyPilot blog](https://skypilot.ai/blog). SkyPilot adopters: [Testimonials and Case Studies](https://skypilot.ai/case-studies) Follow updates: - [Slack](http://slack.skypilot.co) - [X](https://twitter.com/skypilot_org) - [LinkedIn](https://www.linkedin.com/company/skypilot-oss/) - [YouTube](https://www.youtube.com/@skypilot-org) - [SkyPilot Blog](https://skypilot.ai/blog) ## Questions and feedback We are excited to hear your feedback: * For issues and feature requests, please [open a GitHub issue](https://github.com/skypilot-org/skypilot/issues/new). * For questions, please use [GitHub Discussions](https://github.com/skypilot-org/skypilot/discussions). For general discussions, join us on the [SkyPilot Slack](http://slack.skypilot.co). ## Contributing We welcome all contributions to the project!