NVIDIA Corporation

Tune Megatron Bridge training for MoE models, which route tokens to specialized experts, by overlapping expert communication with computation. Check dispatcher requirements, avoid conflicting settings, and compare steady training performance while changing one overlap setting at a time.

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• Reduce exposed token dispatch and combine time
• Check requirements for expert communication overlap
• Configure flex dispatch and resolve overlap conflicts

Convert single-node training or inference commands into multi-node Slurm batch jobs. Choose an appropriate process launch method, configure shared storage and containers, and investigate communication timeouts, launch errors, and GPU memory shortages.

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• Converting a single-node command into a multi-node sbatch job
• Investigating NCCL communication timeouts
• Estimating GPU memory needs for MoE models

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.

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

Guides hierarchical context parallelism in Megatron-Bridge, which splits input sequences across nested GPU groups. Covers configuration, implementation locations, constraints, and log checks to help enable the feature and investigate memory errors or performance regressions.

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• Configure hierarchical context parallelism
• Scale context parallelism beyond KV head counts
• Investigate memory errors after configuration changes

Plan and configure Nemotron customization workflows using existing steps for data preparation, translation, training, conversion, optimization, and evaluation. Check how outputs connect between stages and produce configuration files suited to the supplied models, data, and hardware.

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• Configure LoRA fine-tuning for a supplied model and dataset
• Connect data preparation and supervised fine-tuning
• Benchmark an existing hosted model endpoint

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.

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

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.

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

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.

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

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.

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

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.

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

Create and modify NeMo AutoModel training and evaluation recipes. Get guidance on YAML configuration, functions that build training components, execution flow, validation, and checkpoint saving and resuming, with minimal examples and local checks.

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• Create a new fine-tuning recipe
• Add or change YAML configuration fields
• Configure validation, checkpoint saving, and resuming

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.

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

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.

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

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.

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

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.

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

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

Build or repair NVIDIA voice agents with Pipecat or LiveKit for real-time spoken conversations. Set up speech recognition, language models, and speech synthesis, customize pronunciation or multilingual routing, and deploy locally or in the cloud.

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• Build a new real-time voice agent
• Repair an existing voice conversation pipeline
• Customize speech recognition and pronunciation

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.

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• 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 sequence packing, which combines training examples into token sequences, and long-context training in Megatron-Bridge. Compare offline, in-batch, and Energon online approaches while checking batch sizes, parallelism requirements, and model compatibility.

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• Configure offline packing for text model fine-tuning
• Choose packing methods for vision-language models
• Check parallelism constraints for long-context training

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.

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

Preserve coding-agent progress in readable session files, including goals, decisions, changed files, and next steps. Helps resume long-running work after disconnects or editor restarts and gives another agent concise handoff instructions.

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• Resume development after a disconnect
• Preserve progress before restarting VS Code
• Hand off unfinished work to another agent

Choose and install the right NeMo Relay option for a command-line tool, a Python, Node.js, or Rust application, or a supported framework integration. Check basic availability and report the installation result before moving to runtime setup.

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• Trying NeMo Relay through a temporary CLI run
• Adding a Relay package to an existing application
• Installing Relay for a supported framework

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.

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

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.

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