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Configure and validate CUDA graphs in Megatron Bridge training. Choose whole-iteration or module-level capture, which records GPU operations for replay, then check compatibility constraints and compare runtime and memory use against ordinary execution.

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• Choose whole-iteration or module-level CUDA graph capture
• Configure graph scopes for dense or MoE training
• Investigate crashes after CUDA graph configuration changes

Turn rough moderation requirements or existing policies into custom safety rules for NVIDIA Nemotron models. Produce a Markdown policy, a JSON category structure, and ready-to-use classification prompts with definitions, exceptions, examples, and rules for keeping conversations on topic.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning moderation notes into a structured safety policy
• Adding custom categories and exceptions to an existing policy
• Defining labeling criteria with safe, unsafe, and ambiguous examples

Configure and validate communication overlap in Megatron-Bridge for MoE models, which distribute work among expert networks. Check compatibility, compare training speed, and investigate regressions across standard dispatch, DeepEP, HybridEP, and delayed weight-gradient computation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Enable expert communication overlap in MoE training
• Check compatibility before using DeepEP or HybridEP
• Compare plain overlap and delayed weight-gradient computation

Search documents through NeMo Retriever MCP and use retrieved passages to support answers. Add missing documents when server access is confirmed, then search them while preserving available document identifiers and page numbers for citations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding evidence in indexed documents
• Adding missing documents and searching their contents
• Preparing answers with document identifiers and page references

Diagnose GPU out-of-memory errors during Megatron Bridge training and choose memory reduction settings. Covers fragmentation, LoRA input storage, recomputation, parallelism changes, and CPU offloading, explaining compatibility limits and potential tradeoffs between memory use and training throughput.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating GPU out-of-memory errors during training
• Reducing retained inputs in LoRA training
• Estimating memory needs before a large training job

Build, migrate, and review adapters that connect external agent runtimes to NVIDIA NeMo Fabric. It guides configuration translation, startup and shutdown, discovery metadata, and validation against the public contract, the rules defining how adapters interact with Fabric.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an adapter for an external agent runtime
• Migrating an adapter to the public contract
• Reviewing configuration mapping and runtime isolation

Configure NeMo AutoModel training launches on a local node, a Slurm cluster scheduler, or cloud resources through SkyPilot. Prepare launch settings for GPUs, containers, environment variables, multi-node communication, and optional performance profiling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Switching between local and cluster training launches
• Configuring a multi-node Slurm training job
• Preparing SkyPilot spot jobs with checkpoint recovery

Search document collections with the NeMo Retriever CLI. Ingest files into a local search index or use a deployed service, then retrieve evidence for questions across PDFs, Office files, images, audio, and video.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find evidence across internal documents
• Build a local search index from files
• Query an existing Retriever service

Helps configure training across multiple GPUs or machines in NeMo AutoModel. Guides strategy selection, YAML settings, and parallelism choices that split model computation or data, with constraints for large models, long sequences, and expert models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a strategy for multi-GPU training
• Configuring parallelism for large models
• Splitting long input sequences across GPUs

Enable Megatron FSDP in Megatron-Bridge to distribute model training state across GPUs. Follow configuration and checkpoint requirements, investigate memory failures or performance regressions, and verify the setup with an existing two-GPU functional test.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Switch from DDP to Megatron FSDP
• Investigate memory failures after FSDP configuration changes
• Check checkpoint formats and incompatible settings

Organize NeMo-RL experiments on Brev by placing large outputs and shared caches on /ephemeral, loading credentials without exposing them, checking disk capacity, and keeping concise reproduction records in the source checkout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare storage before a long training run
• Share model and dataset caches across experiments
• Run authenticated W&B or Hugging Face workflows

Configure observability in NeMo Relay 0.6 or 0.7 to inspect recorded activity. Choose raw events, execution histories, or traces that show how calls unfold, then verify the selected output before adding more destinations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting raw events during local debugging
• Exporting execution histories in ATIF format
• Configuring OpenTelemetry trace output

Migrate existing NeMo Flow code and documentation to NeMo Relay by updating explicit names, imports, paths, and configuration references. A bundled helper previews changes, while language-specific checks cover dependencies, generated files, and remaining legacy identifiers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrate imports and dependencies across languages
• Preview identifier and path changes
• Update CLI examples and documentation

Choose how to split large-model training across GPUs in Megatron Bridge. This guide helps combine parallelism methods, estimate GPU requirements, match settings to hardware connections, and check memory use and performance after configuration changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing parallelism settings for a model and GPU count
• Estimating GPU requirements for MoE models
• Adjusting parallelism for long input sequences

Troubleshoot applications where NeMo Relay is installed but runtime behavior is missing or incorrect. It helps isolate loading failures, inactive execution contexts, missing events, and plugin or export configuration issues before changing application settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnose native extension or shared-library loading failures
• Investigate missing lifecycle events
• Separate execution contexts across concurrent requests

Integrate NVIDIA NeMo Fabric into an application or evaluation system through its Python SDK. Map application settings into typed configuration, choose an execution lifecycle, validate prerequisites, and handle standardized results, artifacts, and telemetry.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Integrate agent execution into an application
• Build configurations for different evaluation conditions
• Manage single calls and conversations with retained state

Tune adaptive behavior in an instrumented NeMo Relay application using runtime data. Configure tool parallelism, prompt caching, and state storage, then compare changes against a baseline to check latency, correctness, and failure rates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate adaptive behavior using runtime telemetry
• Tune tool parallelism against a baseline
• Evaluate prompt-cache planning

Plan long-sequence training for MoE models, which divide work among expert networks, in Megatron Bridge. Use experiment-based guidance to balance parallelism, selective recalculation, and routing choices when investigating memory shortages or reduced training throughput.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning long-sequence MoE training configurations
• Investigating memory shortages during long-context training
• Reviewing throughput drops after a code change

Helps an AI coding assistant answer NemoClaw installation, configuration, operation, and troubleshooting questions using official documentation. It finds relevant pages through MCP, a documentation connection, or Markdown files, then tailors guidance to the selected agent and environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Install NemoClaw for a selected agent
• Configure inference providers and models
• Investigate sandbox errors and logs

Configure Megatron-Bridge to overlap communication with computation during distributed training. Review settings for tensor, data, and pipeline parallelism, check activation requirements, and use included tests to investigate configuration issues or throughput regressions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Enable communication overlap in distributed training
• Check why tensor parallel overlap is inactive
• Investigate slower training after configuration changes

Deploy, run, and test NVIDIA speech recognition, speech synthesis, and translation services in the cloud or on your own infrastructure. Guidance covers model selection, environment checks, custom models, and adjustments to speech processing and pronunciation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy and test a speech recognition model
• Generate speech with Magpie
• Adjust pronunciation for specific words

Choose how to distribute tokens, the units a model processes, across MoE model experts during training. The guide compares alltoall, DeepEP, and HybridEP by GPU hardware, parallelism scale, memory needs, and optimization stage.

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• Choosing a MoE dispatcher for specific GPU hardware
• Establishing an alltoall baseline for initial setup
• Comparing DeepEP and HybridEP under matching conditions

Configure fault detection and recovery for distributed training in Megatron Bridge. The skill covers slow GPU detection, automatic restarts, job interruption handling, and checkpoint saving, with configuration examples and guidance for investigating hangs or failed recovery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configuring automatic recovery after training failures
• Detecting slow GPUs in distributed training
• Preparing for scheduled job interruptions

Keep NeMo Relay event histories separate for concurrent requests, tasks, and agents. The skill guides independent scope stacks, which track nested operations, and preserves parent-child relationships when execution crosses asynchronous tasks, threads, or workers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Separate event histories for concurrent requests
• Preserve parent-child links across async tasks and threads
• Prevent request-specific middleware from leaking