data-labeling
claudeData labeling: annotation guidelines, platforms, inter-annotator agreement, active learning, weak supervision, and label error detection.
1 skill · 1 command · 1 agent
Distributed training: DDP, FSDP, DeepSpeed ZeRO, model parallelism, mixed precision, and multi-node debugging.
Plugin distributed-training from marketplace libre-mlops in HermeticOrmus/LibreMLOps-Claude-Code. Commands read from the repo's manifest — 1 month ago.
distributed-training-patternsDistributed training patterns with code: a PyTorch DDP loop, FSDP with a transformer auto-wrap policy, DeepSpeed ZeRO-3 configuration, AMP with GradScaler, gradient checkpointing, and data loading across ranks. Use when scaling training past one GPU.
/distributed-training:dist-trainGenerate DDP, FSDP, or DeepSpeed configuration, write torchrun or SLURM launch commands, read training metrics, or debug out-of-memory errors and NCCL timeouts.
distributed-training-engineerUse this agent when training has to span several GPUs or nodes, or a multi-GPU job hits out-of-memory errors, NCCL timeouts, or instability. It chooses and configures DDP, FSDP, DeepSpeed ZeRO, or Megatron-LM parallelism and mixed precision, and states the speed, memory, and cost trade-offs.
Data labeling: annotation guidelines, platforms, inter-annotator agreement, active learning, weak supervision, and label error detection.
1 skill · 1 command · 1 agent
ML data pipelines: Apache Beam, Spark, dbt, orchestration, streaming features, validation, and backfills.
1 skill · 1 command · 1 agent
Data versioning: DVC, Delta Lake, lineage, dataset cards, and reproducible dataset splits.
1 skill · 1 command · 1 agent
Experiment tracking: MLflow, Weights & Biases, Neptune, ClearML, hyperparameter search, and run organization.
1 skill · 1 command · 1 agent
Feature engineering: feature stores, transformations, selection, time-series features, leakage checks, and drift.
1 skill · 1 command · 1 agent
GPU optimization: profiling, memory management, torch.compile, Flash Attention, Triton kernels, and quantization.
1 skill · 1 command · 1 agent
Query Trackio projects, runs, and metrics using the `trackio` CLI. Use when the user needs to list projects/runs/metrics, get summaries, or retrieve metric values from local Trackio databases. Supports both human-readable and JSON output formats. Covers project discovery, run inspection, metric querying, and system metrics. Designed for LLM agents and automation scripts.
1 skill
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
26 skills · 4 commands · 3 agents · 1 MCP server
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
2 skills · 1 command · 3 agents
Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture
Multi-GPU and multi-node training including DeepSpeed, PyTorch FSDP, Accelerate, Megatron-Core, PyTorch Lightning, and Ray Train. Use when training large models across GPUs.
98 skills
Agents for data engineering, machine learning, and AI development
12 agents
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