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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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