lightning-sdk 2026.7.3

SDK to develop using Lightning AI Studios

Lightning SDK for Python

Automate Lightning AI resources from Python scripts, notebooks, and CI.

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Quick start • Examples • CLI • Development • Docs

Why Lightning SDK?

Lightning SDK turns Lightning AI into a Python API. Use it to create and manage compute, run training or batch jobs, operate persistent studios, deploy containers, inspect logs, and manage sandboxes without leaving your code.

It is designed for automation first: scripts can resolve teamspace scope, start machines, wait for work to finish, collect output, and tear resources down in the same flow.

Build on Lightning AI, the platform for training, deploying, and scaling AI applications with managed compute, collaborative studios, and production endpoints.

Quick start

Install the package:

pip install lightning-sdk

Authenticate once:

lightning login

Or export credentials from Lightning AI account settings:

export LIGHTNING_USER_ID="..."
export LIGHTNING_API_KEY="..."

Run a sandbox command:

from lightning_sdk.sandbox import Sandbox

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

Sandbox-only API keys can also be set with LIGHTNING_SANDBOX_API_KEY or passed through Sandbox.configure(...).

Examples

Work with a studio

from lightning_sdk import Machine, Studio

studio = Studio("research", teamspace="owner/teamspace", create_ok=True)
studio.start(Machine.CPU)

print(studio.status)
print(studio.run("python --version"))

studio.stop()

Run a container job

from lightning_sdk import Job, Machine

job = Job.run(
    name="batch-job",
    teamspace="owner/teamspace",
    image="python:3.11-slim",
    machine=Machine.CPU,
    command="python -c 'print(\"hello from a Lightning job\")'",
    interruptible=True,
)

job.wait()
print(job.status)

Deploy a container

from lightning_sdk import Deployment, Machine
from lightning_sdk.api.deployment_api import ApiKeyAuth

deployment = Deployment("nginx-demo", teamspace="owner/teamspace")
deployment.start(
    image="nginx:latest",
    machine=Machine.CPU,
    ports=80,
    replicas=1,
    auth=ApiKeyAuth(),
)

print(deployment.status)

Use persistent sandboxes

from lightning_sdk.sandbox import Sandbox

sandbox = Sandbox.create(
    name="persistent-devbox",
    instance_type="cpu-1",
    persistent=True,
)

sandbox.write_file("/workspace/app.py", "print('hello from a file')\n")
print(sandbox.run_command("python /workspace/app.py").output)

snapshot_id = sandbox.stop()
print(snapshot_id)

CLI

The package installs lightning, lightning-sdk, and sandbox commands. The examples below use lightning, but the SDK-specific entrypoint accepts the same arguments.

lightning deployment create nginx-demo \
  --teamspace owner/teamspace \
  --image nginx:latest \
  --machine CPU \
  --port 80 \
  --replicas 1 \
  --api-key-auth

Inspect and stream logs:

lightning deployment inspect nginx-demo --teamspace owner/teamspace --jobs
lightning deployment logs nginx-demo --teamspace owner/teamspace --follow

Delete when finished:

lightning deployment delete nginx-demo --teamspace owner/teamspace --yes

More examples

Runnable examples live in examples/:

Area	SDK tutorial	CLI tutorial
studios	studios.rst	studios_cli.rst
jobs	jobs.rst	jobs_cli.rst
multi-machine training	mmts.rst	mmts_cli.rst
teamspaces	teamspaces.rst	teamspaces_cli.rst
sandboxes	sandboxes.rst	sandboxes_cli.rst

Development

From the repository root:

pip install -e ./python

From this directory:

pip install -e .

Build docs from the repository root:

uv run --group docs sphinx-build -M html python/docs/source python/docs/build -W --keep-going

License

Apache-2.0. See ../LICENSE.