skypilot-nightly
SkyPilot: Manage all your AI compute.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires credentials and quota for at least one supported cloud provider or access to a Kubernetes cluster, Slurm system, or on-premises infrastructure.
- 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.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions. Suitable for proprietary and open-source projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 10,498 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 129,004 downloads/mo, #11,687 on PyPI
Alternatives
Verify before relying
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.- 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.
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 on this page. Verify before relying on it.
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
skypilot-nightly on PyPI
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.
Requires credentials and quota for at least one supported cloud provider or access to a Kubernetes cluster, Slurm system, or on-premises infrastructure.
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.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 | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 50 packageswheelsetuptoolspipcachetoolsclickcoloramacryptographyjinja2jsonschemanetworkxpandaspendulumPrettyTablepython-dotenvrichtabulatetqdmtyping_extensionsfilelockpackagingpsutilpulppyyamlijsonorjsonrequestsuvicornfastapipydanticpython-multipart |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 129,004 / month, #11,687 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries :: Python ModulesTopic :: System :: Distributed Computing |
Evidence: skypilot_nightly-1.0.0.dev20260814-py3-none-any.whl
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See also lithops · skypilot · dstack · clearml-agent · torchx · hopsworks · aws-parallelcluster · flyte · prime · vastai