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3,534 results
- Nemo Automodel Distributed TrainingNVIDIAOfficialGuide for selecting and configuring distributed training strategies in NeMo AutoModel, including FSDP2, Megatron FSDP, DDP, and parallelism settings.
- Nemo Automodel Model OnboardingNVIDIAOfficialGuide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation.
- Nemo Automodel Recipe DevelopmentNVIDIAOfficialCreate and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.
- Nemo Mbridge Perf Activation RecomputeNVIDIAOfficialValidate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute. Use for activation memory OOMs or regressions involving recompute_granularity, recompute_num_layers, recompute_modules, recompute_method, selective recompute, full recompute, or activation checkpointing.
- Nemo Mbridge Perf Cpu OffloadingNVIDIAOfficialValidate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.
- Nemo Mbridge Perf Cuda GraphsNVIDIAOfficialValidate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.
- Nemo Mbridge Perf Memory TuningNVIDIAOfficialTechniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.
- Nemo Mbridge Perf Moe Dispatcher SelectionNVIDIAOfficialChoose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage. Summarizes patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up work.
- Nemo Mbridge Perf Moe Hardware ConfigsNVIDIAOfficialRepresentative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.
- Nemo Mbridge Perf Moe Long ContextNVIDIAOfficialLong-context MoE training guidance for Megatron Bridge. Covers CP sizing, selective recompute, dispatcher choices, and practical patterns from DSV3, Qwen3, and Qwen3-Next long-context experiments.
- Nemo Mbridge Perf Moe Vlm TrainingNVIDIAOfficialPractical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.
- Nemo Mbridge Perf Parallelism StrategiesNVIDIAOfficialOperational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.
- Nemo Mbridge Perf Sequence PackingNVIDIAOfficialValidate and use packed sequences and long-context training in Megatron-Bridge, including offline LLM packing, collate-time VLM packing, Energon online packing, and CP constraints.
- Nemo Mbridge Recipe RecommenderNVIDIAOfficialRecommend and customize Megatron Bridge library and benchmark recipes for a user's model, GPU count, hardware, sequence length, and pretrain/SFT/PEFT goal. Use when selecting a starting recipe, comparing library and benchmark configs, resizing parallelism for a GPU allocation, or distinguishing convergence changes, semantics-preserving execution tuning, and benchmark-only shortcuts.
- Nemo Mbridge ResiliencyNVIDIAOfficialResiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.
- Nemoclaw User OverviewNVIDIAOfficialExplains how OpenClaw, OpenShell, and NemoClaw form the ecosystem, NemoClaw's position in the stack, what NemoClaw adds beyond the community sandbox, and when to prefer NemoClaw versus integrating OpenShell and OpenClaw directly. Use when users ask about the relationship between OpenClaw, OpenShell, and NemoClaw, or when to use NemoClaw versus OpenShell.
- Nv Generate MrNVIDIAOfficialUsed for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.
- Nv Generate Mr BrainNVIDIAOfficialUsed for generating synthetic T1, T2, FLAIR, SWI, or MRA brain MRI volumes with NV-Generate-CTMR MR-Brain v1. Not for production training data.
- Nv Segment CtNVIDIAOfficialUsed for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.
- Nvflare Autofl ReportNVIDIAOfficialGenerate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.
- Nvflare Diagnose JobNVIDIAOfficialUse when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.
- Nvshmem Collect Performance DataNVIDIAOfficialCollect and package NVSHMEM put/get bandwidth, latency, and other perftest results with system and topology evidence for performance sanity checks.
- Nvshmem Configure Nic Pe MappingNVIDIAOfficialRecommend NVSHMEM NIC-to-PE mappings and environment exports. Use for HCA selection, multi-NIC configuration, or topology-based mapping diagnostics.
- Nvshmem Select Remote TransportNVIDIAOfficialSelect an NVSHMEM remote transport from target system and kernel evidence. Use for inter-node selection, compatibility checks, or configuration.