ml-training

Plugin ml-training from marketplace ai-dev-marketplace in vanman2024/ai-dev-marketplace. Commands read from the repo's manifest — 1 month ago.

Skills (8)

• cloud-gpu-configs
• cost-calculator

  Cost estimation scripts and tools for calculating GPU hours, training costs, and inference pricing across Modal, Lambda Labs, and RunPod platforms. Use when estimating ML training costs, comparing platform pricing, calculating GPU hours, budgeting for ML projects, or when user mentions cost estimati

• example-projects

  Provides three production-ready ML training examples (sentiment classification, text generation, RedAI trade classifier) with complete training scripts, deployment configs, and datasets. Use when user needs example projects, reference implementations, starter templates, or wants to see working code

• google-cloud-configs

  Google Cloud Platform configuration templates for BigQuery ML and Vertex AI training with authentication setup, GPU/TPU configs, and cost estimation tools. Use when setting up GCP ML training, configuring BigQuery ML models, deploying Vertex AI training jobs, estimating GCP costs, configuring cloud

• integration-helpers

  Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions Fast

• monitoring-dashboard

  Training monitoring dashboard setup with TensorBoard and Weights & Biases (WandB) including real-time metrics tracking, experiment comparison, hyperparameter visualization, and integration patterns. Use when setting up training monitoring, tracking experiments, visualizing metrics, comparing model r

• training-patterns

  Templates and patterns for common ML training scenarios including text classification, text generation, fine-tuning, and PEFT/LoRA. Provides ready-to-use training configurations, dataset preparation scripts, and complete training pipelines. Use when building ML training pipelines, fine-tuning models

• validation-scripts

  Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, testing ML pipelines, verifying dependencies, debugging training failures, or ensuring data

Slash commands (2)

• /ml-training:add

  Add a specific feature to an existing ML training project. Features include dataset, model, distributed, finetune, deploy, platform.

• /ml-training:build

  Build complete ML training pipeline for new or existing projects with data preparation, model training, distributed training, and deployment

Subagents (16)

• cost-optimizer

  GPU selection, PEFT configuration, batch size tuning, and cost estimation for optimal training efficiency

• data-engineer

  Dataset preparation, Supabase integration, data loading, and data validation

• data-specialist

  Advanced preprocessing, tokenization, augmentation, and data quality checks

• distributed-training-specialist

  Multi-GPU training with FSDP, DeepSpeed, and Accelerate - handles sharding strategies, gradient accumulation, and performance optimization

• google-bigquery-ml-specialist

  BigQuery ML for SQL-based machine learning training - model creation, Vertex AI integration, remote model deployment, and cost estimation

• google-vertex-specialist

  Vertex AI custom training jobs for deep learning - GPU/TPU selection, PyTorch/TensorFlow/Hugging Face integration, distributed training setup

• inference-deployer

  Model deployment for serverless inference, auto-scaling configuration, and endpoint creation

• integration-specialist

  Integrate ML pipeline with FastAPI, Next.js, and Supabase for full-stack ML applications

• lambda-specialist

  Lambda Labs cloud instances, API integration, and cost-optimized GPU infrastructure for ML training workloads

• ml-architect

  High-level ML pipeline design, framework selection, platform recommendation, and project initialization

• ml-tester

  End-to-end testing of ML pipeline including data validation, training tests, and inference tests

• modal-specialist

  Modal platform deployment, GPU configuration, and serverless ML endpoint setup with cost optimization

• peft-specialist

  Parameter-efficient fine-tuning with LoRA/QLoRA/prefix-tuning

• runpod-specialist

  RunPod serverless and on-demand GPU configuration, FlashBoot setup, and deployment

• training-architect

  Training configuration, hyperparameter tuning, framework setup, and TrainingArguments creation

• training-monitor

  Monitor ML training runs, track metrics with TensorBoard and Weights & Biases, implement failure recovery strategies

Similar plugins