onboard-world-model

To containerize a world model for multi-GPU deployment, you standardize your research repository into a BYOF registry candidate by enforcing strict capability contracts and running automated smoke tests.

Automated validation for action-conditioned rollouts implements hard-gate testing for data loaders, tokenizers, and model rollouts to ensure simulation reliability before production deployment.

Yes, you can use this onboarding process for latent video prediction models in robotics to containerize them and verify performance on Nebius GPU clusters before sim-to-real training.

Yes, you need the NPA workbench CLI to manage GPU resource profiles, handle S3 data staging, and configure Rerun visualization artifacts during the world model onboarding process.

The best way to transition world model research code to production-ready simulation is by using a standardized playbook that transforms repositories into multi-GPU BYOF registry candidates with automated validation.

World model onboarding requires strict capability contracts to enforce standardized interfaces for data loaders and tokenizers, ensuring reliable multi-GPU deployment and automated smoke testing.