Skip to main content

SkyPilot: An intercloud broker for the clouds

Project description

SkyPilot

Documentation GitHub Release Join Slack

Run LLMs and AI on Any Cloud


:fire: News :fire:

  • [Jul, 2024] Finetune and serve Llama 3.1 on your infra
  • [Jun, 2024] Reproduce GPT with llm.c on any cloud: guide
  • [Apr, 2024] Serve and finetune Llama 3 on any cloud or Kubernetes: example
  • [Apr, 2024] Serve Qwen-110B on your infra: example
  • [Apr, 2024] Using Ollama to deploy quantized LLMs on CPUs and GPUs: example
  • [Feb, 2024] Deploying and scaling Gemma with SkyServe: example
  • [Feb, 2024] Serving Code Llama 70B with vLLM and SkyServe: example
  • [Dec, 2023] Mixtral 8x7B, a high quality sparse mixture-of-experts model, was released by Mistral AI! Deploy via SkyPilot on any cloud: example
  • [Nov, 2023] Using Axolotl to finetune Mistral 7B on the cloud (on-demand and spot): example
  • [Sep, 2023] Case study: Covariant transformed AI development on the cloud using SkyPilot, delivering models 4x faster cost-effectively: read the case study
  • [Aug, 2023] Finetuning Cookbook: Finetuning Llama 2 in your own cloud environment, privately: example, blog post
  • [June, 2023] Serving LLM 24x Faster On the Cloud with vLLM and SkyPilot: example, blog post
Archived
  • [Mar, 2024] Serve and deploy Databricks DBRX on your infra: example
  • [Feb, 2024] Speed up your LLM deployments with SGLang for 5x throughput on SkyServe: example
  • [Dec, 2023] Using LoRAX to serve 1000s of finetuned LLMs on a single instance in the cloud: example
  • [Sep, 2023] Mistral 7B, a high-quality open LLM, was released! Deploy via SkyPilot on any cloud: Mistral docs
  • [July, 2023] Self-Hosted Llama-2 Chatbot on Any Cloud: example
  • [April, 2023] SkyPilot YAMLs for finetuning & serving the Vicuna LLM with a single command!

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, Azure, R2, IBM)

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:

pip install -U "skypilot[aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,kubernetes]"  # choose your clouds

To get the latest features and fixes, use the nightly build or install from source:

pip install "skypilot-nightly[aws,gcp,azure,oci,lambda,runpod,fluidstack,paperspace,cudo,ibm,scp,kubernetes]"  # choose your clouds

Current supported providers (AWS, Azure, GCP, OCI, Lambda Cloud, RunPod, Fluidstack, Paperspace, Cudo, IBM, Samsung, Cloudflare, any Kubernetes cluster):

SkyPilot

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: 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<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

SkyPilot Demo

Refer to Quickstart to get started with SkyPilot.

More Information

To learn more, see our Documentation and Tutorials.

Runnable examples:

Follow updates:

Read the research:

Support and Questions

We are excited to hear your feedback!

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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

skypilot_nightly-1.0.0.dev20240729.tar.gz (879.4 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file skypilot_nightly-1.0.0.dev20240729.tar.gz.

File metadata

File hashes

Hashes for skypilot_nightly-1.0.0.dev20240729.tar.gz
Algorithm Hash digest
SHA256 bf14631c6c6d78c8056154e089ea864db706ef9b6645bd39664fc7960b789f04
MD5 648c86626123e2046af05e0916c47b0c
BLAKE2b-256 8a9f0e1d969cd6d3cd006154a6b6baf67fbf0d3ff8997d730604ea36bd0df497

See more details on using hashes here.

File details

Details for the file skypilot_nightly-1.0.0.dev20240729-py3-none-any.whl.

File metadata

File hashes

Hashes for skypilot_nightly-1.0.0.dev20240729-py3-none-any.whl
Algorithm Hash digest
SHA256 8b4dc33059e7cc0aa3c2e8eec3d3ef16446ee129f4da4f3848d3090236da09c1
MD5 fb6e32000508cd77dcb7297050d5aeae
BLAKE2b-256 d07574459a8941ab1b6e69800f5cffe51eeb33015d401b155efd5c9e2fcee64e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page