launch-nemo-rl

Solid

Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI. Covers ephemeral vs long-lived RayCluster modes, iterating on runs, and debugging hung or failed training jobs.

AI & Automation 4 stars 0 forks Updated today Apache-2.0

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Skill Content

# launch-nemo-rl — running NeMo-RL recipes on Kubernetes via nrl-k8s This is the playbook for the `nrl-k8s` CLI at `infra/nrl_k8s/`. Follow it when the user asks to launch / iterate / debug a NeMo-RL recipe on a Kubernetes cluster. Verify current state (`kubectl`, `git log`, the recipe + infra files) before acting — the cluster is shared and the cost of a wrong action is high. ## 1. One command, two modes There is a single top-level submission command: **`nrl-k8s run`**. It has two lifecycle modes. | Mode | Invocation | When to use | Cluster after? | | :----------------- | :---------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------- | | Ephemeral (default) | `nrl-k8s run` | One-shot. KubeRay applies a RayJob, runs, tears the cluster down. Best for most runs. | No (auto) | | Long-lived | `nrl-k8s run --raycluster` | Dev loop. Reuses a matching live cluster, applies if absent, warns + reuses on drift (pass `--recreate` to replace). Then submits daemons and training. First-choice for iteration. | Yes | Ask: *Do I need th...

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Author
yangwhale
Repository
yangwhale/CloseCrab
Created
6 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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