This release is a pre-release and may not be stable for production use.
Run AI on Any Infra — Unified, Faster, Cheaper
:fire: News :fire:
- [Feb 2025] Run and serve DeepSeek-R1 671B using SkyPilot and SGLang with high throughput: example
- [Feb 2025] Prepare and serve large-scale image search with vector databases: blog post, example
- [Jan 2025] Launch and serve distilled models from DeepSeek-R1 and Janus on Kubernetes or any cloud: R1 example and Janus example
- [Oct 2024] :tada: SkyPilot crossed 1M+ downloads :tada:: Thank you to our community! Twitter/X
- [Sep 2024] Point, launch and serve Llama 3.2 on Kubernetes or any cloud: example
- [Sep 2024] Run and deploy Pixtral, the first open-source multimodal model from Mistral AI.
- [Jun 2024] Reproduce GPT with llm.c on any cloud: guide
- [Apr 2024] Serve Qwen-110B on your infra: example
- [Apr 2024] Host Ollama on the cloud to deploy LLMs on CPUs and GPUs: 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 framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability.
SkyPilot abstracts away infra burdens:
- Launch clusters, jobs, and serving on any infra
- Easy job management: queue, run, and auto-recover many jobs
SkyPilot supports multiple clusters, clouds, and hardware (the Sky):
- Bring your reserved GPUs, Kubernetes clusters, or 12+ clouds
- Flexible provisioning of GPUs, TPUs, CPUs, with auto-retry
SkyPilot cuts your cloud costs & maximizes GPU availability:
- Autostop: automatic cleanup of idle resources
- Managed Spot: 3-6x cost savings using spot instances, with preemption auto-recovery
- Optimizer: 2x cost savings by auto-picking 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,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,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp]"
Current supported infra (Kubernetes; AWS, GCP, Azure, OCI, Lambda Cloud, Fluidstack, RunPod, Cudo, Digital Ocean, Paperspace, Cloudflare, Samsung, IBM, Vast.ai, VMware vSphere):
Getting Started
You can find our documentation here.
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 (YAML or Python API), the task can be launched on any available cloud. 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: |
pip install "torch<2.2" torchvision --index-url https://download.pytorch.org/whl/cu121
# 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:
- Find the lowest priced VM instance type across different clouds
- Provision the VM, with auto-failover if the cloud returned capacity errors
- Sync the local
workdirto the VM - Run the task's
setupcommands to prepare the VM for running the task - Run the task's
runcommands
Refer to Quickstart to get started with SkyPilot.
More Information
To learn more, see SkyPilot Overview, SkyPilot docs, and SkyPilot blog.
Runnable examples:
- AI Gallery
- LLMs on SkyPilot
- DeepSeek-R1
- DeepSeek-Janus
- Llama 3.2: lightweight and vision models
- Pixtral
- Llama 3.1 finetuning and serving
- GPT-2 via
llm.c - Llama 3
- Qwen
- Databricks DBRX
- Gemma
- Mixtral 8x7B; Mistral 7B (from official Mistral team)
- Code Llama
- vLLM: Serving LLM 24x Faster On the Cloud (from official vLLM team)
- SGLang: Fast and Expressive LLM Serving On the Cloud (from official SGLang team)
- Vicuna chatbots: Training & Serving (from official Vicuna team)
- Train your own Vicuna on Llama-2
- Self-Hosted Llama-2 Chatbot
- Ollama: Quantized LLMs on CPUs
- LoRAX
- QLoRA
- LLaMA-LoRA-Tuner
- Tabby: Self-hosted AI coding assistant
- LocalGPT
- Falcon
- Add yours here & see more in
llm/!
- Framework examples: Vector Database, PyTorch DDP, DeepSpeed, JAX/Flax on TPU, Stable Diffusion, Detectron2, Distributed TensorFlow, Ray Train, NeMo, programmatic grid search, Docker, Cog, Unsloth, Ollama, llm.c, Airflow and many more (
examples/).
Case Studies and Integrations: Community Spotlights
Follow updates:
Read the research:
- SkyPilot paper and talk (NSDI 2023)
- Sky Computing whitepaper
- Sky Computing vision paper (HotOS 2021)
- Policy for Managed Spot Jobs (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.
Support and Questions
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.
Contributing
We welcome all contributions to the project! See CONTRIBUTING for how to get involved.
3 maintainers
Metadata
3 maintainers
Release files for skypilot-nightly 1.0.0.dev20250215
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| skypilot_nightly-1.0.0.dev20250215.tar.gz | 973.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| skypilot_nightly-1.0.0.dev20250215-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.1 MB
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