全部 Agent Skills
41,263 个结果
- Kermt Pretrain ScratchNVIDIA官方Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrain_ddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model is randomly initialized.
- Kermt SetupNVIDIA官方Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Every other kermt-* skill depends on this; invoke it first.
- Launch Nemo RlNVIDIA官方Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI. Covers ephemeral vs long-lived RayCluster modes, iterating on runs, and debugging hung or failed training jobs.
- Mcore Create IssueNVIDIA官方Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.
- Mcore Linting And FormattingNVIDIA官方Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.
- Mcore Run On SlurmNVIDIA官方How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.
- Mcore Split PrNVIDIA官方Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.
- Mcore TestingNVIDIA官方Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.
- Medtech Model Evidence ExportNVIDIA官方Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.
- Molmim NimNVIDIA官方Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.
- Msa Search NimNVIDIA官方Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment.
- Msa Structure Prediction PipelineNVIDIA官方NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences.
- Nemo Automodel Distributed TrainingNVIDIA官方Guide for selecting and configuring distributed training strategies in NeMo AutoModel, including FSDP2, Megatron FSDP, DDP, and parallelism settings.
- Nemo Automodel Launcher ConfigNVIDIA官方Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
- Nemo Automodel Model OnboardingNVIDIA官方Guide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation.
- Nemo Automodel Recipe DevelopmentNVIDIA官方Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.
- Nemo Data Designer PluginNVIDIA官方Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
- Nemo Evaluator PluginNVIDIA官方Evaluate models, datasets, and agents with the NeMo Evaluator plugin. Use for metric selection, SDK checks, platform jobs, and result retrieval.
- Nemo Fabric Build AdapterNVIDIA官方Build, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract. Use when creating adapter or target descriptors, mapping AgentConfig into an agent harness or custom-agent runtime, implementing start/invoke/stop, declaring schemas and capabilities, packaging discovery metadata, or assessing adapter conformance.
- Nemo Fabric IntegrateNVIDIA官方Use this skill when integrating NVIDIA NeMo Fabric into a consumer application, service, evaluation harness, or platform through the typed Python SDK — translating the consumer's own application, job, or deployment config into an in-memory FabricConfig, choosing the single-invocation convenience API or an explicitly started runtime, validating with plan and doctor, and consuming normalized…
- Nemo Mbridge Mlm Bridge TrainingNVIDIA官方Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.
- Nemo Mbridge Multi Node SlurmNVIDIA官方Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.
- Nemo Mbridge Perf Activation RecomputeNVIDIA官方Validate 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 OffloadingNVIDIA官方Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.