---
id: skypilot-nightly
version: "1.0.0.dev20260814"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# skypilot-nightly — SkyPilot: Manage all your AI compute.

License: permissive · Maintenance: active · Downloads: 129.0K/mo

## What it is and what it does

SkyPilot is a control plane for running AI workloads across heterogeneous infrastructure—clouds, Kubernetes clusters, Slurm systems, and reserved GPU pools. You define a task once in YAML or Python, specifying resource needs, setup commands, and job commands, then launch it to any available backend. SkyPilot handles provisioning, failover, workdir syncing, dependency installation, and log streaming.

It targets two audiences: AI teams who want a simple, portable interface to run jobs anywhere without vendor lock-in, and infrastructure teams who need a unified control plane to schedule, scale, and optimize utilization across multiple clusters and clouds. Key features include gang scheduling for multi-node jobs, multi-cluster support, intelligent bin-packing, automatic idle resource cleanup, and interactive development on Kubernetes.

Use it for:

- Launch distributed training jobs across multiple GPU clouds with automatic failover to cheaper or available alternatives.
- Run batch inference workloads on a mix of reserved GPUs, Kubernetes pods, and cloud instances from a single job queue.
- Develop and debug AI code interactively on remote Kubernetes clusters with SSH and code sync.
- Manage team resource sharing and governance across on-premises Slurm clusters and multiple cloud accounts.
- Optimize GPU utilization on shared Kubernetes clusters through gang scheduling and workload bin-packing.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

SkyPilot provisions and manages AI compute jobs across multiple cloud providers, Kubernetes clusters, Slurm systems, and on-premises infrastructure through a unified YAML or Python interface.

Yes—if you manage or use AI compute across multiple clouds or clusters. SkyPilot eliminates vendor lock-in and simplifies multi-infrastructure orchestration. The nightly build carries development-stage risk; for production use, verify stability requirements. Low install friction and active maintenance make it a reasonable choice for teams already managing heterogeneous infrastructure.

## Install

pip install skypilot-nightly
uv add skypilot-nightly
poetry add skypilot-nightly

## Installing skypilot-nightly

Before you install: Low friction: pure Python wheel with no compiled dependencies. Active maintenance—released daily as a nightly build with 10498 repository stars and a commit from 2026-08-14. Substantial dependency footprint (50 runtime packages) is typical for a multi-cloud orchestration tool.

License in practice: Apache 2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions. Suitable for proprietary and open-source projects.

Quickstart:

pip install 'skypilot-nightly[kubernetes,aws,gcp,azure]'

Create my_task.yaml:
resources:
  accelerators: A100:1
setup: pip install torch
run: python train.py

Then launch with the CLI.

Requires credentials and quota for at least one supported cloud provider or access to a Kubernetes cluster, Slurm system, or on-premises infrastructure.

Verify before relying:

- Whether the nightly build is production-ready or intended for testing only.
- Specific performance characteristics or latency overhead of the orchestration layer.
- Whether all 50 runtime dependencies are always required or conditionally installed by cloud provider extras.

## Package facts

- License: Apache 2.0 (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 129.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

multi-cloud GPU job scheduler, kubernetes ai workload orchestration, distributed compute management, cloud-agnostic ml job launcher, gpu cluster provisioning, multi-infrastructure task runner, ai workload scaling, multi-cloud, gpu-orchestration, kubernetes

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