:fire: News :fire:
The Evolution of AI Job Orchestration: (1) Running AI jobs on GPU Neoclouds, (2) The AI-Native Control Plane & Orchestration that Finally Works for MLLLM 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:
SkyPilot makes Kubernetes easy for AI & Infra teams:
SkyPilot unifies multiple clusters, clouds, and hardware:
SkyPilot cuts your cloud costs & maximizes GPU availability:
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]"
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]"
Current supported infra: Kubernetes, AWS, GCP, Azure, OCI, Nebius, Lambda Cloud, RunPod, Fluidstack, Cudo, Digital Ocean, Paperspace, Cloudflare, Samsung, IBM, Vast.ai, VMware vSphere.
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 1
Prepare the workdir by cloning:
git clone https://github.com/pytorch/examples.git ~/torch_examples
Launch with sky launch (note: access to GPU instances is needed for this example):
sky launch my_task.yaml
SkyPilot then performs the heavy-lifting for you, including:
workdir to the provisioned clustersetup commandsrun commands, and stream logsSee 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 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 general discussions, join us on the SkyPilot Slack.
We welcome all contributions to the project! See CONTRIBUTING for how to get involved.
FAQs
SkyPilot: Run AI on Any Infra — Unified, Faster, Cheaper.
We found that skypilot demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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