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starforge-core

Client for StarForge. You get the sf command and a Python package named starforge.

Use this to scaffold a lab, lock a post-training method, and submit jobs to a StarForge console. The control plane is a separate install, starforge-server. Training images do not need this whole package; the server injects a runner that carries the kernel only.

Install

pip install starforge-core
# or
uv tool install starforge-core

sf --help

Python 3.10 or newer. Import name is starforge, not starforge_core.

pip install starforge-core[data] adds HuggingFace datasets for local prepare scripts.

Start a lab

Do not clone the platform repo. This package writes a project for you:

sf init my-lab --yes
cd my-lab
sf login --server https://<your-console>
sf new my-grpo --method nemo-rl/grpo
sf validate my-grpo
sf submit my-grpo --profile h200:8
sf job logs

sf methods lists the catalog on the server you logged into. Methods look like nemo-rl/grpo, verl/sft, trl/kto, openrlhf/ppo, evalkit/benchmark, custom/custom.

Examples live in starforge-tutorial. Day-to-day work should be your own sf init tree.

Python

Catalog methods already forward framework logs. Custom train.sh jobs that want console curves call:

from starforge.report import init, log, finish

init()
log({"loss": 0.12}, step=1)
finish()

starforge.spec_to_env is the JobSpec to environment mapping used on both the client and the job. starforge.get_recipe / starforge.recipe_names read the bundled catalog.

What this package contains

  • sf CLI: init, login, experiments, submit, sweep, jobs, datasets, plugins, bench, serve
  • JobSpec types and spec_to_env
  • Recipe catalog and framework adapters (NeMo-RL, verl, TRL, OpenRLHF, evalkit, custom)
  • Job-side reporter (starforge.report)

Cluster credentials stay on the server. Your laptop keeps an access token under ~/.forge/.

Docs and source

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