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Nemo Rl Brev Etiquette

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Guidelines
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk
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From the author

Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo-rl-auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to managing disk space and environment configuration on Brev instances. It establishes a clear separation between the source code repository located in /home/ubuntu/RL and the high-capacity /ephemeral volume used for heavy experiment artifacts, logs, and caches. By following these patterns, agents can prevent disk exhaustion, maintain clean git repositories, and securely handle sensitive credentials like W&B or Hugging Face tokens.

What it does

  • Defines directory structures to separate source code from experiment data.
  • Provides patterns for redirecting caches and temporary files to the /ephemeral volume.
  • Outlines secure procedures for loading and managing environment variables from .env files.
  • Establishes a checklist for monitoring disk capacity before and during long-running jobs.
  • Standardizes the organization of experiment logs, checkpoints, and artifacts.
  • Offers guidance on cleaning up experiment data without disrupting shared caches.

When to use it

  • When launching NeMo-RL auto-research campaigns on a Brev instance.
  • Before starting training jobs or experiments that generate large logs or checkpoints.
  • When configuring environments that require authenticated access to W&B or Hugging Face.
  • When managing shared caches for tools like PyTorch, Hugging Face, or pip.
  • When disk space in the primary workspace is limited and requires offloading to /ephemeral.

Advantages

  • Prevents workspace disk overflow by offloading heavy assets to /ephemeral.
  • Keeps git repositories clean and focused on code rather than bulky artifacts.
  • Ensures reproducible experiments by standardizing path management.
  • Improves security by centralizing and protecting sensitive environment variables.
  • Facilitates efficient resource usage through shared cache directories.

Usage examples

“How should I configure my environment variables for a new NeMo-RL run on Brev?”
The agent will guide you to store secrets in /home/ubuntu/RL/.env and provide the shell snippet to safely load them into the environment before execution.
“I am running out of disk space on my Brev instance during a training job. What should I do?”
The agent will instruct you to check disk usage with df -h, move large output directories to /ephemeral, and update your configuration to point cache and log paths to the /ephemeral volume.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nemo-rl-brev-etiquette/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip nemo-rl-brev-etiquette.zip -d .agents/skills

Documentation by NVIDIA

Brev Etiquette

Operate as though /home/ubuntu/RL is the source checkout and /ephemeral is the working storage for generated experiment state. Keep the repo small, reproducible, and easy to inspect. Move bulky run outputs to /ephemeral before launching anything expensive.

Storage Rules

  • Keep code edits, small config changes, committed experiment hypotheses, and concise reproducibility records under /home/ubuntu/RL.

  • Put generated experiment assets under /ephemeral, including checkpoints, run logs, Ray temp directories, W&B offline files, profiler traces, evaluation dumps, rollout samples, and per-experiment artifacts.

  • Keep reusable caches under one shared /ephemeral cache root per user, not under each experiment. This includes Hugging Face models, dataset caches, PyTorch caches, Triton caches, uv caches, and pip caches.

  • Before a campaign or long run, check capacity with df -h /home/ubuntu/RL /ephemeral and avoid starting if /ephemeral is missing or nearly full.

  • Create a campaign root such as /ephemeral/nemo-rl/${USER:-ubuntu}/nemo-rl-auto-research/<campaign> and use one subdirectory per experiment.

  • Do not leave large files, cache directories, or generated outputs in the git checkout. If a tool defaults to the repo, override its output/cache path before running it.

Environment Secrets

  • Treat /home/ubuntu/RL/.env as the local secret store. It may contain keys such as WANDB_API_KEY, HF_TOKEN, or HUGGING_FACE_HUB_TOKEN.

  • Before any run that may need external auth, load /home/ubuntu/RL/.env when it exists. Never print, cat, log, commit, or summarize secret values.

  • If /home/ubuntu/RL/.env is absent, or a required key is still unset after loading it, remind the user to add the needed key to that file before launching authenticated work.

if [ -f /home/ubuntu/RL/.env ]; then
  set -a
  . /home/ubuntu/RL/.env
  set +a
else
  echo "Missing /home/ubuntu/RL/.env; add required keys such as WANDB_API_KEY or HF_TOKEN before authenticated runs."
fi

Auto-Research Pattern

When using nemo-rl-auto-research, keep the git ledger in the repo and heavy evidence on /ephemeral.

if [ -f /home/ubuntu/RL/.env ]; then
  set -a
  . /home/ubuntu/RL/.env
  set +a
fi

BREV_ROOT=/ephemeral/nemo-rl/${USER:-ubuntu}
CACHE_ROOT=$BREV_ROOT/cache
CAMPAIGN_ROOT=$BREV_ROOT/nemo-rl-auto-research/<campaign>
EXP_DIR=$CAMPAIGN_ROOT/<experiment>
mkdir -p "$EXP_DIR"/{logs,checkpoints,artifacts,ray,tmp,wandb}
mkdir -p "$CACHE_ROOT"/{huggingface,torch,triton,uv,pip,xdg,wandb}

export HF_HOME=$CACHE_ROOT/huggingface
export HF_HUB_CACHE=$HF_HOME/hub
export HF_DATASETS_CACHE=$HF_HOME/datasets
export TRANSFORMERS_CACHE=$HF_HOME/transformers
export TORCH_HOME=$CACHE_ROOT/torch
export TRITON_CACHE_DIR=$CACHE_ROOT/triton
export UV_CACHE_DIR=$CACHE_ROOT/uv
export PIP_CACHE_DIR=$CACHE_ROOT/pip
export XDG_CACHE_HOME=$CACHE_ROOT/xdg
export WANDB_CACHE_DIR=$CACHE_ROOT/wandb
export RAY_TMPDIR=$EXP_DIR/ray
export TMPDIR=$EXP_DIR/tmp
export WANDB_DIR=$EXP_DIR/wandb

Record the absolute /ephemeral paths in the nemo-rl-auto-research TSV fields for log path, checkpoint path, artifacts, shared cache root, and command. If the TSV itself may grow large, store the full TSV in /ephemeral and keep a small pointer file or summary in the repo.

Launch Checklist

  • Inspect disk first: df -h /home/ubuntu/RL /ephemeral.

  • Choose a unique /ephemeral run root before editing recipes or launching jobs.

  • Reuse a shared cache root such as /ephemeral/nemo-rl/${USER:-ubuntu}/cache across experiments unless a run explicitly requires a clean cache.

  • Override recipe output paths, logger paths, checkpoint paths, and temp paths to point under the experiment directory.

  • Override cache paths to point under the shared cache root.

  • Stream stdout/stderr to $EXP_DIR/logs/run.log or an equivalent file under /ephemeral.

  • Periodically check disk during long runs with df -h /ephemeral and stop gracefully if the volume is approaching exhaustion.

  • At the end, summarize the important metrics and paths in the repo ledger; do not copy bulky artifacts back into /home/ubuntu/RL.

Cleanup

  • Clean only files that belong to the current campaign or experiment.

  • Prefer pruning clearly named experiment directories under /ephemeral/nemo-rl/...; never remove shared caches or another user's run directory without an explicit instruction.

  • Preserve enough small metadata in the repo to reproduce a result after /ephemeral is cleaned.

Files

skill.oms.sig

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/nemo-rl-brev-etiquette · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

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