🔥 News 🔥
- [Oct 2025] Train and serve Andrej Karpathy's nanochat - the best ChatGPT that $100 can buy: example
- [Oct 2025] Run large-scale LLM training with TorchTitan on any AI infra: example
- [Sep 2025] Scaling AI infrastructure at Abridge - 10x faster development with SkyPilot: blog
- [Sep 2025] Network and Storage Benchmarks for LLM training on the cloud: blog
- [Aug 2025] Serve and finetune OpenAI GPT-OSS models (gpt-oss-120b, gpt-oss-20b) with one command on any infra: serve + LoRA and full finetuning
- [Jul 2025] Run distributed RL training for LLMs with Verl (PPO, GRPO) on any cloud: example
- [Jul 2025] Finetune Llama4 on any distributed cluster/cloud: example
- [Jul 2025] Two-part blog series,
The Evolution of AI Job Orchestration: (1) Running AI jobs on GPU Neoclouds, (2) The AI-Native Control Plane & Orchestration that Finally Works for ML - [Apr 2025] Spin up Qwen3 on your cluster/cloud: example
- [Feb 2025] Prepare and serve Retrieval Augmented Generation (RAG) with DeepSeek-R1: blog post, example
LLM Finetuning Cookbooks: Finetuning Llama 2 / Llama 3.1 in your own cloud environment, privately: Llama 2 example and blog; Llama 3.1 example and blog
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.
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, or 16+ clouds
- Flexible provisioning of GPUs, TPUs, CPUs, with auto-retry
- Team deployment and resource sharing
SkyPilot cuts your cloud costs & maximizes GPU availability:
- Autostop: automatic cleanup of idle resources
- Spot instance support: 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:
# Choose your clouds:
pip install -U "skypilot[kubernetes,aws,gcp,azure,oci,nebius,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,seeweb]"To get the latest features and fixes, use the nightly build or install from source:
# Choose your clouds:
pip install "skypilot-nightly[kubernetes,aws,gcp,azure,oci,nebius,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,seeweb]"
Current supported infra: Kubernetes, AWS, GCP, Azure, OCI, Nebius, Lambda Cloud, RunPod, Fluidstack, Cudo, Digital Ocean, Paperspace, Cloudflare, Samsung, IBM, Vast.ai, VMware vSphere, Seeweb, Prime Intellect.
You can find our documentation here.
A SkyPilot task specifies: resource requirements, data to be synced, setup commands, and the task commands.
Once written in this unified interface (YAML or Python API), the task can be launched on any available infra (Kubernetes, 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:
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 1Prepare the workdir by cloning:
git clone https://github.com/pytorch/examples.git ~/torch_examplesLaunch with sky launch (note: access to GPU instances is needed for this example):
sky launch my_task.yamlSkyPilot then performs the heavy-lifting for you, including:
- Find the cheapest & available infra across your clusters or clouds
- Provision the GPUs (pods or VMs), with auto-failover if the infra returned capacity errors
- Sync your local
workdirto the provisioned cluster - Auto-install dependencies by running the task's
setupcommands - Run the task's
runcommands, and stream logs
See Quickstart to get started with SkyPilot.
See SkyPilot examples that cover: development, training, serving, LLM models, AI apps, and common frameworks.
Latest featured examples:
| Task | Examples |
|---|---|
| Training | Verl, Finetune Llama 4, TorchTitan, PyTorch, DeepSpeed, NeMo, Ray, Unsloth, Jax/TPU |
| Serving | vLLM, SGLang, Ollama |
| Models | DeepSeek-R1, Llama 4, Llama 3, CodeLlama, Qwen, Kimi-K2, Mixtral |
| AI apps | RAG, vector databases (ChromaDB, CLIP) |
| Common frameworks | Airflow, Jupyter |
Source files can be found in llm/ and examples/.
To learn more, see SkyPilot Overview, SkyPilot docs, and SkyPilot blog.
SkyPilot adopters: Testimonials and Case Studies
Partners and integrations: Community Spotlights
Follow updates:
Read the research:
- SkyPilot paper and talk (NSDI 2023)
- Sky Computing whitepaper
- Sky Computing vision paper (HotOS 2021)
- SkyServe: AI serving across regions and clouds (EuroSys 2025)
- Managed jobs spot instance policy (NSDI 2024)
SkyPilot was initially started at the Sky Computing Lab at UC Berkeley and has since gained many industry contributors. To read about the project's origin and vision, see Concept: Sky Computing.
We are excited to hear your feedback:
- For issues and feature requests, please open a GitHub issue.
- For questions, please use GitHub Discussions.
For general discussions, join us on the SkyPilot Slack.
We welcome all contributions to the project! See CONTRIBUTING for how to get involved.