lightning-sdk
SDK to develop using Lightning AI Studios
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Authentication via LIGHTNING_USER_ID and LIGHTNING_API_KEY environment variables or lightning login command.
- Low install friction with a pure-Python wheel and no compiled dependencies.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 8 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,709 downloads/mo, #13,431 on PyPI
Alternatives
Verify before relying
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()- 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.
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 on this page. Verify before relying on it.
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
lightning-sdk on PyPI
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.
Requires Python 3.11 or later. Authentication via LIGHTNING_USER_ID and LIGHTNING_API_KEY environment variables or lightning login command.
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()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 the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagesbackoffclickrich-clickdockerfastapipackagingpyyamlrequestsrichsixtqdmtextualurllib3uvicornwebsocket-client |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,709 / month, #13,431 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 |
Evidence: lightning_sdk-2026.8.14-py3-none-any.whl
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See also lightning · lightning-cloud · prime · blaxel · prime-sandboxes · lightning-utilities · nominal · e2b-code-interpreter · e2b · azureml-sdk