skypilot-nightly 1.0.0.dev20250530

SkyPilot: Run AI on Any Infra — Unified, Faster, Cheaper.

Run AI on Any Infra — Unified, Faster, Cheaper

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:fire: News :fire:

• [Apr 2025] Spin up Qwen3 on your cluster/cloud: example
• [Mar 2025] Run and serve Google Gemma 3 using SkyPilot example
• [Feb 2025] Prepare and serve Retrieval Augmented Generation (RAG) with DeepSeek-R1: blog post, example
• [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

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

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SkyPilot is an open-source framework for running AI and batch workloads on any infra.

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 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,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,nebius]"

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,nebius]"

Current supported infra: Kubernetes, AWS, GCP, Azure, OCI, Lambda Cloud, Fluidstack, RunPod, Cudo, Digital Ocean, Paperspace, Cloudflare, Samsung, IBM, Vast.ai, VMware vSphere, Nebius.

Getting started

You can find our documentation here.

• Installation
• Quickstart
• CLI reference

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:

1. Find the lowest priced VM instance type across different clouds
2. Provision the VM, with auto-failover if the cloud returned capacity errors
3. Sync the local workdir to the VM
4. Run the task's setup commands to prepare the VM for running the task
5. Run the task's run commands

See Quickstart to get started with SkyPilot.

Runnable examples

See SkyPilot examples that cover: development, training, serving, LLM models, AI apps, and common frameworks.

Latest featured examples:

Task	Examples
Training	PyTorch, DeepSpeed, Finetune Llama 3, NeMo, Ray, Unsloth, Jax/TPU
Serving	vLLM, SGLang, Ollama
Models	DeepSeek-R1, Llama 3, CodeLlama, Qwen, Mixtral
AI apps	RAG, vector databases (ChromaDB, CLIP)
Common frameworks	Airflow, Jupyter

Source files and more examples can be found in llm/ and examples/.

More information

To learn more, see SkyPilot Overview, SkyPilot docs, and SkyPilot blog.

Case studies and integrations: Community Spotlights

Follow updates:

• Slack
• X / Twitter
• LinkedIn
• SkyPilot Blog (Introductory blog post)

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.

Questions and feedback

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.