Run LLMs and AI on Any Cloud

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🔥 News 🔥

• [June, 2023] Serving LLM 24x Faster On the Cloud with vLLM and SkyPilot: example, blog post
• [June, 2023] Two new clouds supported: Samsung SCP and Oracle OCI!
• [April, 2023] SkyPilot YAMLs released for finetuning & serving the Vicuna model with a single command!
• [March, 2023] Vicuna LLM chatbot trained using SkyPilot for $300 on spot instances!
• [March, 2023] Serve your own LLaMA LLM chatbot (not finetuned) on any cloud: example, repo

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SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution.

SkyPilot abstracts away cloud infra burdens:

• Launch jobs & clusters on any cloud
• Easy scale-out: queue and run many jobs, automatically managed
• Easy access to object stores (S3, GCS, R2)

SkyPilot maximizes GPU availability for your jobs:

• Provision in all zones/regions/clouds you have access to (the Sky), with automatic failover

SkyPilot cuts your cloud costs:

• Managed Spot: 3-6x cost savings using spot VMs, with auto-recovery from preemptions
• Optimizer: 2x cost savings by auto-picking the cheapest VM/zone/region/cloud
• Autostop: hands-free cleanup of idle clusters

SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

Install with pip or from source:

pip install "skypilot[aws,gcp,azure,ibm,oci,scp,lambda]"  # choose your clouds

Current supported providers (AWS, Azure, GCP, Lambda Cloud, IBM, Samsung, OCI, Cloudflare):

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: V100:1  # 1x NVIDIA V100 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 torchvision

# 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

Refer to Quickstart to get started with SkyPilot.

Learn more

• Documentation
• Example: HuggingFace
• Tutorials
• YAML reference
• Framework examples: PyTorch DDP, Distributed TensorFlow, JAX/Flax on TPU, Stable Diffusion, Detectron2, programmatic grid search, Docker, and many more.

More information:

• SkyPilot Blog
  • Introductory blog post
• NSDI 2023 paper & talk

Issues, feature requests, 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 and value all contributions to the project! Please refer to CONTRIBUTING for how to get involved.