HyperbolicLabs/skypilot

HyperbolicLabs/skypilot

Description: SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 16+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.

Language: Python

License: Apache-2.0

Stars: 0

Forks: 2

Open issues: 0

Created: 2025-05-07T14:52:13Z

Pushed: 2025-07-29T23:55:16Z

Default branch: master

Fork: yes

Parent repository: skypilot-org/skypilot

Archived: no

README:

Run AI on Any Infra — Unified, Faster, Cheaper

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:fire: *News* :fire:

• [Jul 2025] 🎉 SkyPilot v0.10.0 released! **blog post**, **release notes**
• [Jul 2025] Finetune Llama4 on any distributed cluster/cloud: [example](./llm/llama-4-finetuning/)
• [Jul 2025] Two-part blog series, The Evolution of AI Job Orchestration: (1) Running AI jobs on GPU Neoclouds, (2) The AI-Native Control Plane & Orchestration that Finally Works for ML
• [Apr 2025] Spin up Qwen3 on your cluster/cloud: [example](./llm/qwen/)
• [Mar 2025] Run and serve Google Gemma 3 using SkyPilot [example](./llm/gemma3/)
• [Feb 2025] Prepare and serve Retrieval Augmented Generation (RAG) with DeepSeek-R1: **blog post**, [example](./llm/rag/)
• [Feb 2025] Run and serve DeepSeek-R1 671B using SkyPilot and SGLang with high throughput: [example](./llm/deepseek-r1/)
• [Feb 2025] Prepare and serve large-scale image search with vector databases: **blog post**, [example](./examples/vector_database/)
• [Jan 2025] Launch and serve distilled models from [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) and [Janus](https://github.com/deepseek-ai/DeepSeek-Janus) on Kubernetes or any cloud: [R1 example](./llm/deepseek-r1-distilled/) and [Janus example](./llm/deepseek-janus/)
• [Oct 2024] :tada: SkyPilot crossed 1M+ downloads :tada:: Thank you to our community! **Twitter/X**

LLM Finetuning Cookbooks: Finetuning Llama 2 / Llama 3.1 in your own cloud environment, privately: Llama 2 [example](./llm/vicuna-llama-2/) and **blog**; Llama 3.1 [example](./llm/llama-3_1-finetuning/) 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: |
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: 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…