Description: <br>
Guide for selecting and configuring distributed training strategies in NeMo AutoModel, including FSDP2, Megatron FSDP, DDP, and parallelism settings. <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 configuring distributed training strategies (FSDP2, HSDP, DDP, tensor parallelism, pipeline parallelism, context parallelism, expert parallelism) for large-scale model training in NeMo AutoModel. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <br>
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <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>
- NeMo AutoModel Documentation <br>
- SKILL.md <br>
Skill Output: <br>
Output Type(s): [Configuration instructions, Shell commands] <br> Output Format: [Markdown with inline YAML code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>
Evaluation Agents Used: <br>
- Claude Code (
aws/anthropic/bedrock-claude-opus-4-8) <br> - Codex (
openai/openai/gpt-5.5) <br>
Evaluation Tasks: <br>
Evaluated against 3 evaluation tasks (3 positive) in isolated k8s-sandbox pods. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
- Security: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
- Correctness: Checks final-answer correctness against the reference answer. <br>
- Discoverability: Checks whether the expected skill was found and executed when needed. <br>
- Effectiveness: Checks whether the skill helped complete the user's goal and expected workflow (equal-weight mean of goal completion and behavior adherence). <br>
- Efficiency: Checks routing quality, workspace-aware skill reads, and productive tool use. <br>
Underlying evaluation signals used in this run: <br>
security: Unsafe operations, secret leakage, and unauthorized access. <br>skill_execution: Whether the expected skill was found and executed. <br>skill_efficiency: Routing quality, workspace-aware skill reads, and productive tool use. <br>accuracy: Final-answer correctness against the reference answer. <br>goal_accuracy: Whether the user's goal was achieved. <br>behavior_check: Whether the expected workflow behavior was followed. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 58% → 99% (+41 points) | 67% → 92% (+24 points) |
| Security | 83% → 100% (+17 points) | 100% → 100% (±0 points) |
| Correctness | 73% → 100% (+27 points) | 100% → 100% (±0 points) |
| Discoverability | 33% → 100% (+67 points) | 50% → 94% (+44 points) |
| Effectiveness | 69% → 96% (+27 points) | 87% → 91% (+4 points) |
| Efficiency | 32% → 100% (+68 points) | 0% → 73% (+73 points) |
Skill Version(s): <br>
v1.2.1+7febc6e (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>