Ml Training

ML Training Plugin

Machine learning training and inference pipeline using cloud GPUs (Modal, Lambda Labs, RunPod) with HuggingFace ecosystem - no local GPU required.

Overview

This plugin enables ML model training and deployment without requiring local GPU hardware. All training runs on cloud GPU platforms with pay-per-use pricing, making it accessible and cost-effective for startups and individual developers.

Key Features

• Cloud GPU Training: Modal ($0.59/hr), Lambda Labs ($0.31/hr), RunPod (pay-per-minute)
• No Local GPU Required: Run everything from any laptop with internet
• HuggingFace Ecosystem: Transformers, PEFT, Accelerate, TRL
• Cost Optimization: LoRA/QLoRA for 90% memory reduction
• Serverless Inference: Auto-scaling endpoints with Modal
• Full Stack Integration: FastAPI, Next.js, Supabase

Quick Start

# Initialize ML project
/ml:init my-classifier

# Add cloud platform
/ml:add-platform modal

# Add training data from Supabase
/ml:add-dataset supabase training_data

# Configure training
/ml:add-training-config classification

# Deploy training to cloud GPU
/ml:deploy-training

# Deploy inference endpoint
/ml:deploy-inference

Commands

Core Initialization

• /ml:init - Initialize ML training project
• /ml:add-platform - Add Modal/Lambda/RunPod platform
• /ml:setup-framework - Configure HuggingFace/PyTorch/Ray

Data Preparation

• /ml:add-dataset - Load training data from Supabase/local/HuggingFace
• /ml:add-preprocessing - Add tokenization and data transforms
• /ml:validate-data - Validate data quality

Training Configuration

• /ml:add-training-config - Create training configuration
• /ml:add-peft - Add LoRA/QLoRA for cost savings
• /ml:optimize-training - Optimize batch size and mixed precision
• /ml:add-monitoring - Add TensorBoard/WandB monitoring

Cloud GPU Deployment

• /ml:deploy-training - Deploy training job to cloud GPU
• /ml:deploy-inference - Deploy model for serverless inference
• /ml:monitor-training - Monitor active training jobs

Integration

• /ml:add-fastapi-endpoint - Add inference endpoint to FastAPI
• /ml:add-nextjs-ui - Add ML UI components to Next.js
• /ml:integrate-supabase - Connect to Supabase for data storage

Testing & Utilities

• /ml:test - Test ML pipeline components
• /ml:estimate-cost - Estimate training and inference costs

Architecture

Local Machine (No GPU)
├── requirements-local.txt    # Lightweight CLI tools
├── venv/                     # Virtual environment
├── train.py                  # Training script
└── modal_image.py           # Cloud GPU dependencies

Cloud GPU Servers (Modal/Lambda/RunPod)
├── torch                     # Heavy ML framework
├── transformers             # HuggingFace models
├── accelerate               # Distributed training
└── peft                     # LoRA/QLoRA

Cost Examples

Training (4 hours)

• Modal T4: $2.36
• Lambda A10: $1.24 (cheapest)
• Modal A100: $8.40

Inference (10K requests/month)

• Modal Serverless: $5/month
• Auto-scaling 0-1000+ GPUs

Integration

Works with existing AI Dev Marketplace plugins:

• fastapi-backend: Serve inference endpoints
• nextjs-frontend: Display predictions and training UI
• supabase: Store training data and model metadata
• vercel-ai-sdk: Stream inference results
• rag-pipeline: Combine RAG retrieval with ML predictions

Documentation

See /home/gotime2022/.claude/plugins/marketplaces/ai-dev-marketplace/ML-TRAINING-AND-INFERENCE.md for comprehensive guide.

License

MIT License - see LICENSE file

Version

1.0.0 (2025-11-01)