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Export existing medical AI inference results to MLflow, a system for recording machine learning runs. Preview sanitized metadata, parameters, reproducibility details, and engineering quality metrics before export, with optional image artifacts under explicit upload policies.

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• Recording completed inference runs in MLflow
• Keeping generation parameters and reproducibility details
• Previewing export contents without contacting MLflow

Helps tune activation recomputation in Megatron Bridge, recalculating intermediate training values to reduce GPU memory use. Guides selective or full-layer settings, checks compatibility, and compares memory peaks with training speed to assess whether a configuration completes training steps.

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• Investigate training failures caused by activation memory
• Choose recomputation targets for attention or MoE models
• Compare GPU memory savings against training speed

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

Guide repeated NeMo-RL or NeMo-gym experiments to test hypotheses and improve training settings. Establish a baseline, compare relevant metrics, and preserve reproducible records in git and TSV logs while keeping users involved in launch decisions.

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• Testing hypotheses about training settings
• Comparing accuracy, reward, or throughput
• Recording reproducible experiment histories

Find starting configurations for training MoE models, which route work among specialized expert networks, on H100, B200, GB200, and GB300. Provides model-specific parallel layouts, communication options, and approximate performance bands to guide measured comparisons.

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• Choose initial MoE training settings for specific hardware
• Compare communication backends and parallel layouts
• Estimate approximate training throughput for planning

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

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.

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

Try NeMo Relay through a CLI wrapper, a supported integration, or direct Python, Node.js, or Rust instrumentation. Choose a minimal first run and verify recorded tool and model activity before considering one additional plugin.

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• Try Relay with Codex or Claude Code through the CLI
• Choose a supported integration for an existing application
• Check recorded tool and LLM calls in an application

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

Configure CPU offloading in Megatron Bridge to reduce GPU memory use by moving intermediate training data or optimizer states to CPU memory. Check compatibility, adjust the offloaded fraction, and investigate memory errors or slowdowns.

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• Reduce GPU memory use during model training
• Tune how much optimizer state moves to CPU memory
• Check compatibility with parallelism and CUDA graphs

Wrap existing tool functions and language model calls with NeMo Relay to record execution events, process requests through middleware, and apply guardrails. Guides integration while preserving the original functions’ arguments, results, and business logic.

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• Adding execution events to existing tool calls
• Wrapping model provider calls with NeMo Relay
• Integrating request processing and execution guardrails

Plan domain or language adaptation for Nemotron Speech recognition. Compare word boosting, language models, and fine-tuning based on available audio, accuracy targets, and budget, then route data preparation, training, evaluation, and deployment to the appropriate sub-skills.

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• Choose an approach for recognizing specialist terminology
• Plan adaptation for accents, noise, or another language
• Estimate audio data, GPU, time, and cost requirements

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

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

Guides typed data integration in NeMo Relay by choosing codecs, converters between application values and JSON. Helps preserve middleware edits and caller-visible responses while distinguishing application data conversion from provider-specific LLM request and response handling.

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• Connecting existing domain models to NeMo Relay
• Choosing codecs for Python or Node.js
• Making provider payloads inspectable by middleware

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

Write and review NeMo-RL documentation using its project conventions. Keep the documentation index aligned with added or renamed Markdown files, document new features in the appropriate location, and format function and class docstrings in Google style.

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• Add a new document and update the documentation index
• Update index entries after renaming a Markdown file
• Review function and class docstring formatting

Run Isaac for Healthcare workflows in a simulator using a learned policy or rule-based controller. Verify completed episodes through the final success summary, visible behavior, and recorded data, with checkpoint compatibility checks when a checkpoint is supplied.

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• Evaluating a learned policy for a specified number of episodes
• Checking a workflow with its declared rule-based controller
• Checking a supplied checkpoint and completing simulator evaluation

Plan, run, and verify Jetson video encoding, decoding, transcoding, and segment workflows using NVIDIA samples or PyNvVideoCodec. It checks file hashes, byte sizes, frame counts, and logs to confirm that each stage consumes the preceding stage’s output.

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• Verify an encode-to-decode workflow
• Check H.264-to-HEVC transcoding
• Decode and validate every output segment

Configure USB ports on Jetson Thor or Orin custom carrier boards. Check wiring and paired USB2/USB3 ports, then create a kernel device-tree overlay, a hardware configuration layer, with consistent lane, port, and host controller settings.

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• Enable or disable USB ports on a custom carrier board
• Configure USB roles and power-control wiring
• Investigate USB devices missing after flashing

Add and run unit or functional tests for Megatron-LM. The guide explains test organization, YAML execution recipes, reference results for numerical checks, test filters, temporary disabling, and reproducing continuous integration failures locally.

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• Add unit tests or functional training tests
• Run selected tests and reproduce CI failures locally
• Download or update reference results for numerical checks

Inspect a Jetson BSP folder on a Linux host before flashing. Get a concise report of the L4T release, available board configuration files, and whether the root filesystem appears populated, without modifying files.

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• Checking the L4T release before flashing
• Listing available board configuration files
• Checking whether the root filesystem appears populated

Prepare a workspace for customizing Jetson board support software. Sets up Git tracking for changes, arranges kernel and related source files, and locates or extracts the NVIDIA Crosstool-NG compiler tools needed for later builds.

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• Preparing a workspace after BSP image initialization
• Arranging source files from local archives for offline setup
• Using a customer Git fork as the BSP source

Configure speculative decoding on Jetson vLLM servers, letting a smaller model propose tokens for the main model to verify. Adjust memory allocation and benchmark before and after to assess generation speed under low concurrency.

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• Reducing slow token generation on Thor or AGX Orin
• Choosing EAGLE-3 or a small draft model
• Comparing generation speed before and after configuration