Lepton AI

The Python library and lep CLI for NVIDIA DGX Cloud Lepton

Homepage • Examples • Documentation • CLI References

The LeptonAI Python library lets you operate the NVIDIA DGX Cloud Lepton platform from Python and the command line. Key features include:

• A lep command-line tool to create and manage endpoints, batch jobs, dev pods, Ray and Slurm clusters, fine-tuning jobs, storage, secrets, and more.
• A Client to call your deployed endpoints like native Python functions.
• Pythonic configuration specs that are readily shipped to the cloud.
• Skills that let agents operate the Lepton platform for you.

Getting started

Install the library, which also installs the lep command-line tool:

pip install -U leptonai

Log in to your workspace (this opens a browser to fetch credentials if you don’t pass them in):

lep login

Deploy a container image as an endpoint, then inspect it:

lep endpoint create -n my-endpoint --container-image my-registry/my-app:latest
lep endpoint list
lep endpoint status -n my-endpoint

Batch jobs and dev pods work the same way:

# Run a batch job
lep job create -n my-job --container-image my-registry/my-trainer:latest --command "python train.py"

# Launch an interactive dev pod
lep pod create -n my-pod --resource-shape gpu.a10

Run lep --help, or lep --help for any subcommand, to explore everything. See the CLI references for the full guide.

Calling an endpoint from Python

Once an endpoint is running, call it from Python with the Client. It reads the endpoint’s OpenAPI schema and exposes each path as a method:

from leptonai.client import Client, local

# Connect to a workspace endpoint...
c = Client("my-workspace", "my-endpoint", token="MY_TOKEN")
# ...or to something running locally:
c = Client(local(port=8080))

# Discover the available paths and their docs
print(c.paths())
print(c.run.__doc__)

# Call the endpoint as if it were a local function
print(c.run(inputs="hello world"))

Checking out more examples

You can find more examples in the examples repository, and full guides in the documentation.

Skills: Operating Lepton from Claude Code or Codex

This repo ships an agent skill that lets Claude Code (or Codex) drive the lep CLI for you — listing endpoints, inspecting jobs and dev pods, checking workspace status, and managing workloads, all from natural language. It uses the same lep CLI installed above, so make sure it is authenticated to your workspace.

The plugin lives under plugins/lepton-cli with per-agent manifests for Claude Code, Codex, and Cursor (.claude-plugin/, .codex-plugin/, .cursor-plugin/), all sharing the one skill at skills/lepton-cli. It is listed in two marketplaces in this repo: .claude-plugin/marketplace.json for Claude Code and .agents/plugins/marketplace.json for Codex.

Codex — add this repo as a marketplace, then install the plugin:

codex plugin marketplace add leptonai/leptonai
codex plugin add lepton-cli@lepton-skills

Or browse interactively: run /plugins in the Codex CLI (or open Plugins in the Codex app), find Lepton CLI, and install.

Claude Code — install from the Lepton marketplace in one line, nothing to clone:

/plugin marketplace add leptonai/leptonai
/plugin install lepton-cli@lepton-skills

Start a new session, then ask something like “List the endpoints in my Lepton workspace.” The skill asks for explicit confirmation before any command that modifies or deletes a workload.

Contributing

Contributions and collaborations are welcome and highly appreciated. Please check out the contributor guide for how to get involved.

License

The Lepton AI Python library is released under the Apache 2.0 license.

Developer Note: early development of LeptonAI was in a separate mono-repo, which is why you may see commits from the leptonai/lepton repo. We intend to use this open source repo as the source of truth going forward.