Nemo Rl Brev Etiquette
NVIDIAOfficialPublished by NVIDIA
- Skill
- Guidelines
- Cloud and DevOps
- Apache-2.0
- Medium risk
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nemo-rl-brev-etiquette/downloadUnzip 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
/ephemeralcache root per user, not under each experiment. This includes Hugging Face models, dataset caches, PyTorch caches, Triton caches,uvcaches, and pip caches.Before a campaign or long run, check capacity with
df -h /home/ubuntu/RL /ephemeraland avoid starting if/ephemeralis missing or nearly full.Create a campaign root such as
/ephemeral/nemo-rl/${USER:-ubuntu}/nemo-rl-auto-research/<campaign>and use one subdirectory per experiment.Do not leave large files, cache directories, or generated outputs in the git checkout. If a tool defaults to the repo, override its output/cache path before running it.
Environment Secrets
Treat
/home/ubuntu/RL/.envas the local secret store. It may contain keys such asWANDB_API_KEY,HF_TOKEN, orHUGGING_FACE_HUB_TOKEN.Before any run that may need external auth, load
/home/ubuntu/RL/.envwhen it exists. Never print,cat, log, commit, or summarize secret values.If
/home/ubuntu/RL/.envis absent, or a required key is still unset after loading it, remind the user to add the needed key to that file before launching authenticated work.
if [ -f /home/ubuntu/RL/.env ]; then
set -a
. /home/ubuntu/RL/.env
set +a
else
echo "Missing /home/ubuntu/RL/.env; add required keys such as WANDB_API_KEY or HF_TOKEN before authenticated runs."
fiAuto-Research Pattern
When using nemo-rl-auto-research, keep the git ledger in the repo and heavy evidence on /ephemeral.
if [ -f /home/ubuntu/RL/.env ]; then
set -a
. /home/ubuntu/RL/.env
set +a
fi
BREV_ROOT=/ephemeral/nemo-rl/${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/wandbRecord 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
/ephemeralrun root before editing recipes or launching jobs.Reuse a shared cache root such as
/ephemeral/nemo-rl/${USER:-ubuntu}/cacheacross 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.logor an equivalent file under/ephemeral.Periodically check disk during long runs with
df -h /ephemeraland 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
/ephemeralis cleaned.
Files
- ATTRIBUTION.mdAdded by AutoGineer843 B
- BENCHMARK.md4.2 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.1 KiB
- evals/evals.json2.2 KiB
- skill-card.md3.8 KiB
- skill.oms.sig4.5 KiB
skill-card.md
Description: <br>
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. <br>
This skill is ready for commercial/non-commercial use. <br>
Owner
NVIDIA <br>
License/Terms of Use: <br>
Apache 2.0 <br>
Use Case: <br>
Developers and engineers running NeMo-RL training experiments, auto-research campaigns, and model downloads on Brev instances who need guidance on disk layout, cache management, and secret handling to avoid filling the workspace volume. <br>
Deployment Geography for Use: <br>
Global <br>
Known Risks and Mitigations: <br>
Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>
Reference(s): <br>
Skill Output: <br>
Output Type(s): [Configuration instructions, Shell commands] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>
Evaluation Agents Used: <br>
Claude Code (
claude-code) <br>Codex (
codex) <br>
Evaluation Tasks: <br>
Evaluated against 4 evaluation tasks (2 positive skill-activation, 2 negative) with 2 attempts per task via NVSkills-Eval. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access. <br>
Correctness: Checks whether the agent follows the expected workflow and produces the correct final output. <br>
Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant. <br>
Effectiveness: Checks whether the agent performs measurably better with the skill than without it. <br>
Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work. <br>
Underlying evaluation signals used in this run: <br>
security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>skill_execution: Verifies that the agent loaded the expected skill and workflow. <br>skill_efficiency: Checks routing quality, decoy avoidance, and redundant tool usage. <br>accuracy: Grades final-answer correctness against the reference answer. <br>goal_accuracy: Checks whether the overall user task completed successfully. <br>behavior_check: Verifies expected behavior steps, including safety expectations. <br>token_efficiency: Compares token usage with and without the skill. <br>
Evaluation Results: <br>
| Dimension | Num | claude-code | codex |
|---|---|---|---|
| Security | 8 | 100% (+0%) | 100% (+0%) |
| Correctness | 8 | 81% (+6%) | 84% (+12%) |
| Discoverability | 8 | 100% (+0%) | 86% (+2%) |
| Effectiveness | 8 | 84% (+9%) | 72% (+12%) |
| Efficiency | 8 | 88% (+1%) | 78% (+3%) |
Skill Version(s): <br>
1.5.4 (source: pyproject.toml) <br>
Ethical Considerations: <br>
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>
(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>
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
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nemo-rl-brev-etiquette.zip