---
id: lightning-sdk
version: "2026.8.14"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# lightning-sdk — SDK to develop using Lightning AI Studios

License: permissive · Maintenance: active · Downloads: 92.7K/mo

## What it is and what it does

Lightning SDK is a Python client library for the Lightning AI platform that lets you automate resource management and job orchestration from code. It exposes compute, studios, jobs, deployments, and sandboxes as Python objects you can create, configure, monitor, and tear down programmatically—all without switching to a web UI or CLI. The library is built for automation-first workflows: a single script can authenticate, provision a machine, submit a training job, wait for completion, collect results, and clean up.

Under the hood, it wraps Lightning AI's REST API and provides both a Python API and CLI commands. It depends on 15 runtime packages including fastapi, uvicorn, docker, and websocket-client, suggesting it handles both synchronous and asynchronous operations, container orchestration, and real-time log streaming. It requires Python 3.11 or later and is actively maintained with no known security vulnerabilities.

Use it for:

- Automate multi-step ML training pipelines: provision machines, submit jobs, wait for completion, and collect artifacts in a single script.
- Deploy containerized services (e.g., nginx, custom APIs) to managed infrastructure and inspect logs without manual portal access.
- Create ephemeral or persistent development sandboxes for testing, then snapshot and tear down to control costs.
- Integrate Lightning AI resource management into CI/CD workflows to run batch jobs on demand.
- Build team automation tools that resolve teamspace scope and manage shared compute resources programmatically.

## Worth the install?

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

Python SDK to create, manage, and automate Lightning AI resources—compute, studios, jobs, deployments, and sandboxes—from scripts and notebooks without leaving your code.

Yes, if you use Lightning AI and need to automate resource provisioning and job orchestration from Python. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it safe to adopt. The 15 runtime dependencies are a consideration if you're sensitive to dependency bloat, but they appear necessary for the full feature set. Not relevant if you don't use Lightning AI or prefer CLI-only interaction.

## Install

pip install lightning-sdk
uv add lightning-sdk
poetry add lightning-sdk

## Installing lightning-sdk

Before you install: Low install friction with a pure-Python wheel and no compiled dependencies. Active maintenance with a release on 2026-08-14 and no known vulnerabilities. Requires Python 3.11 or later.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.

Quickstart:

pip install lightning-sdk

from lightning_sdk.sandbox import Sandbox

sandbox = Sandbox.create(name="example", instance_type="cpu-1")
command = sandbox.run_command("python --version")
print(command.output)
sandbox.delete()

Requires Python 3.11 or later. Authentication via LIGHTNING_USER_ID and LIGHTNING_API_KEY environment variables or lightning login command.

Verify before relying:

- Whether the 15 runtime dependencies (fastapi, uvicorn, docker, textual, etc.) are all required for typical use or if many are optional.
- Performance characteristics and rate limits when orchestrating large numbers of jobs or deployments.
- Whether persistent sandboxes incur storage or compute costs beyond active runtime.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 92.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

lightning ai automation python, managed compute orchestration sdk, ai training job deployment, studio and sandbox management, cloud resource provisioning api, batch job scheduling, container deployment platform, orchestration, ai-infrastructure, automation

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