#### [🌟 **SkyPilot Demo** 🌟: Click to see a 1-minute tour](https://demo.skypilot.co/dashboard/)
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:
- [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://blog.skypilot.co/scaling-autoresearch/), [**HackerNews**](https://news.ycombinator.com/item?id=47442435)
- [Mar 2026] **SkyPilot Agent Skills**: GPU access and job management for AI agents: [**docs**](https://docs.skypilot.co/en/latest/getting-started/skill.html)
- [Jan 2026] **Shopify case study**: Shopify runs all AI training workloads on SkyPilot: [**case study**](https://shopify.engineering/skypilot)
- [Dec 2025] **SkyPilot v0.11** released: Multi-Cloud Pools, Fast Managed Jobs, Enterprise-Readiness at Large Scale, Programmability. [**Release notes**](https://github.com/skypilot-org/skypilot/releases/tag/v0.11.0)
- [Dec 2025] Train **an agent to use Google Search** as a tool with RL on your Kubernetes or clouds: [**blog**](https://blog.skypilot.co/verl-tool-calling/), [**example**](./llm/verl/)
- [Oct 2025] Run **RL training for LLMs** with SkyRL on your Kubernetes or clouds: [**example**](./llm/skyrl/)
## Overview
SkyPilot **is easy to use for AI teams**:
- 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 auto-retry
- [Team deployment](https://docs.skypilot.co/en/latest/reference/api-server/api-server.html) and resource sharing
SkyPilot **cuts your cloud costs & maximizes GPU availability**:
* Autostop: automatic cleanup of idle resources
* [Spot instance support](https://docs.skypilot.co/en/latest/examples/managed-jobs.html#running-on-spot-instances): 3-6x cost savings, with preemption auto-recovery
* Intelligent scheduling: automatically run on the cheapest & most available infra
SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.
Install with pip:
```bash
# Choose your clouds:
pip install -U "skypilot[kubernetes,aws,gcp,azure,oci,nebius,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,seeweb,shadeform,verda]"
```
To get the latest features and fixes, use the nightly build or [install from source](https://docs.skypilot.co/en/latest/getting-started/installation.html):
```bash
# Choose your clouds:
pip install "skypilot-nightly[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
```
## Getting started
You can find our documentation [here](https://docs.skypilot.co/).
- [Installation](https://docs.skypilot.co/en/latest/getting-started/installation.html)
- [Quickstart](https://docs.skypilot.co/en/latest/getting-started/quickstart.html)
- [CLI reference](https://docs.skypilot.co/en/latest/reference/cli.html)
## 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).
## More information
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://blog.skypilot.co/).
SkyPilot adopters: [Testimonials and Case Studies](https://blog.skypilot.co/case-studies/)
Partners and integrations: [Community Spotlights](https://blog.skypilot.co/community/)
Follow updates:
- [Slack](http://slack.skypilot.co)
- [X / Twitter](https://twitter.com/skypilot_org)
- [LinkedIn](https://www.linkedin.com/company/skypilot-oss/)
- [SkyPilot Blog](https://blog.skypilot.co/) ([Introductory blog post](https://blog.skypilot.co/introducing-skypilot/))
Read the research:
- [SkyPilot paper](https://www.usenix.org/system/files/nsdi23-yang-zongheng.pdf) and [talk](https://www.usenix.org/conference/nsdi23/presentation/yang-zongheng) (NSDI 2023)
- [Sky Computing whitepaper](https://arxiv.org/abs/2205.07147)
- [Sky Computing vision paper](https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s02-stoica.pdf) (HotOS 2021)
- [SkyServe: AI serving across regions and clouds](https://arxiv.org/pdf/2411.01438) (EuroSys 2025)
- [Managed jobs spot instance policy](https://www.usenix.org/conference/nsdi24/presentation/wu-zhanghao) (NSDI 2024)
SkyPilot was initially started at the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley and has since gained many industry contributors. To read about the project's origin and vision, see [Concept: Sky Computing](https://docs.skypilot.co/en/latest/sky-computing.html).
## 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! See [CONTRIBUTING](CONTRIBUTING.md) for how to get involved.
