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
sfCLI: 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
- Platform repo: https://github.com/wccdev/starforge
- User docs:
mintlify/in that repo (English and 简体中文)
1 maintainer
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