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.
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Improve MoE training throughput in Megatron Bridge. MoE models divide work among expert networks; this skill identifies memory, communication, and compute bottlenecks, guides controlled configuration comparisons, and validates speed alongside training health.
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Choose Megatron Bridge training configurations for a model, GPU allocation, and training goal. Get starting commands and parallel processing adjustments, with clear distinctions between changes that affect learning behavior, execution tuning, and benchmark-only settings.
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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.
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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.
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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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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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Add support for new model architectures in NeMo AutoModel, from inspecting configuration files to implementing layers, converting saved weights, registering models, and testing numerical agreement with Hugging Face implementations.
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Package reusable NeMo Relay behavior as an embedded component or an independently distributed plugin. Design shared configuration, validate it before activation, and register behavior with rollback handling so failed setup does not leave partial changes active.
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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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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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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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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.
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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.
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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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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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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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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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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 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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