⚡ WarpGBM MCP Service

GPU-accelerated gradient boosting as a cloud MCP service
Train on A10G GPUs • Get artifact_id for <100ms cached predictions • Download portable artifacts

🌐 Live Service • 📖 API Docs • 🤖 Agent Guide • 🐍 Python Package

🎯 What is This?

Outsource your GBDT workload to the world's fastest GPU implementation.

WarpGBM MCP is a stateless cloud service that gives AI agents instant access to GPU-accelerated gradient boosting. Built on WarpGBM (91+ ⭐), this service handles training on NVIDIA A10G GPUs while you receive portable model artifacts and benefit from smart 5-minute caching.

🏗️ How It Works (The Smart Cache Workflow)

graph LR
    A[Train on GPU] --> B[Get artifact_id + model]
    B --> C[5min Cache]
    C --> D[<100ms Predictions]
    B --> E[Download Artifact]
    E --> F[Use Anywhere]

Train: POST your data → Train on A10G GPU → Get artifact_id + portable artifact
Fast Path: Use artifact_id → Sub-100ms cached predictions (5min TTL)
Slow Path: Use model_artifact_joblib → Download and use anywhere

Architecture: 🔒 Stateless • 🚀 No model storage • 💾 You own your artifacts

⚡ Quick Start

For AI Agents (MCP)

Add to your MCP settings (e.g., .cursor/mcp.json):

{
  "mcpServers": {
    "warpgbm": {
      "url": "https://warpgbm.ai/mcp/sse"
    }
  }
}

For Developers (REST API)

# 1. Train a model
curl -X POST https://warpgbm.ai/train \
  -H "Content-Type: application/json" \
  -d '{
    "X": [[5.1,3.5,1.4,0.2], [6.7,3.1,4.4,1.4], ...],
    "y": [0, 1, 2, ...],
    "model_type": "warpgbm",
    "objective": "multiclass"
  }'

# Response includes artifact_id for fast predictions
# {"artifact_id": "abc-123", "model_artifact_joblib": "H4sIA..."}

# 2. Make fast predictions (cached, <100ms)
curl -X POST https://warpgbm.ai/predict_from_artifact \
  -H "Content-Type: application/json" \
  -d '{
    "artifact_id": "abc-123",
    "X": [[5.0,3.4,1.5,0.2]]
  }'

🚀 Key Features

Feature	Description
🎯 Multi-Model	WarpGBM (GPU) + LightGBM (CPU)
⚡ Smart Caching	`artifact_id` → 5min cache → <100ms inference
📦 Portable Artifacts	Download joblib models, use anywhere
🤖 MCP Native	Direct tool integration for AI agents
💰 X402 Payments	Optional micropayments (Base network)
🔒 Stateless	No data storage, you own your models
🌐 Production Ready	Deployed on Modal with custom domain

🐍 Python Package vs MCP Service

This repo is the MCP service wrapper. For production ML workflows, consider using the WarpGBM Python package directly:

Feature	MCP Service (This Repo)	Python Package
Installation	None needed	`pip install git+https://...`
GPU	Cloud (pay-per-use)	Your GPU (free)
Control	REST API parameters	Full Python API
Features	Train, predict, upload	+ Cross-validation, callbacks, feature importance
Best For	Quick experiments, demos	Production pipelines, research
Cost	$0.01 per training	Free (your hardware)

Use this MCP service for: Quick tests, prototyping, agents without local GPU
Use Python package for: Production ML, research, cost savings, full control

📡 Available Endpoints

Core Endpoints

Method	Endpoint	Description
`GET`	`/models`	List available model backends
`POST`	`/train`	Train model, get artifact_id + model
`POST`	`/predict_from_artifact`	Fast predictions (artifact_id or model)
`POST`	`/predict_proba_from_artifact`	Probability predictions
`POST`	`/upload_data`	Upload CSV/Parquet for training
`POST`	`/feedback`	Submit feedback to improve service
`GET`	`/healthz`	Health check with GPU status

MCP Integration

Method	Endpoint	Description
`SSE`	`/mcp/sse`	MCP Server-Sent Events endpoint
`GET`	`/.well-known/mcp.json`	MCP capability manifest
`GET`	`/.well-known/x402`	X402 pricing manifest

💡 Complete Example: Iris Dataset

# 1. Train WarpGBM on Iris (60 samples recommended for proper binning)
curl -X POST https://warpgbm.ai/train \
  -H "Content-Type: application/json" \
  -d '{
  "X": [[5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5],
        [5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5],
        [5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5]],
  "y": [0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,
        0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,
        0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2],
  "model_type": "warpgbm",
  "objective": "multiclass",
  "n_estimators": 100
}'

# Response:
{
  "artifact_id": "abc123-def456-ghi789",
  "model_artifact_joblib": "H4sIA...",
  "training_time_seconds": 0.0
}

# 2. Fast inference with cached artifact_id (<100ms)
curl -X POST https://warpgbm.ai/predict_from_artifact \
  -H "Content-Type: application/json" \
  -d '{
  "artifact_id": "abc123-def456-ghi789",
  "X": [[5,3.4,1.5,0.2], [6.7,3.1,4.4,1.4], [7.7,3.8,6.7,2.2]]
}'

# Response: {"predictions": [0, 1, 2], "inference_time_seconds": 0.05}
# Perfect classification! ✨

⚠️ Important: WarpGBM uses quantile binning which requires 60+ samples for proper training. With fewer samples, the model can't learn proper decision boundaries.

🏠 Self-Hosting

Local Development

# Clone repo
git clone https://github.com/jefferythewind/mcp-warpgbm.git
cd mcp-warpgbm

# Setup environment
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Run locally (GPU optional for dev)
uvicorn local_dev:app --host 0.0.0.0 --port 8000 --reload

# Test
curl http://localhost:8000/healthz

# Install Modal
pip install modal

# Authenticate
modal token new

# Deploy
modal deploy modal_app.py

# Service will be live at your Modal URL

Deploy to Other Platforms

# Docker (requires GPU)
docker build -t warpgbm-mcp .
docker run --gpus all -p 8000:8000 warpgbm-mcp

# Fly.io, Railway, Render, etc.
# See their respective GPU deployment docs

🧪 Testing

# Install dev dependencies
pip install -r requirements-dev.txt

# Run all tests
./run_tests.sh

# Or use pytest directly
pytest tests/ -v

# Test specific functionality
pytest tests/test_train.py -v
pytest tests/test_integration.py -v

📦 Project Structure

mcp-warpgmb/
├── app/
│   ├── main.py              # FastAPI app + routes
│   ├── mcp_sse.py           # MCP Server-Sent Events
│   ├── model_registry.py    # Model backend registry
│   ├── models.py            # Pydantic schemas
│   ├── utils.py             # Serialization, caching
│   ├── x402.py              # Payment verification
│   └── feedback_storage.py  # Feedback persistence
├── .well-known/
│   ├── mcp.json             # MCP capability manifest
│   └── x402                 # X402 pricing manifest
├── docs/
│   ├── AGENT_GUIDE.md       # Comprehensive agent docs
│   ├── MODEL_SUPPORT.md     # Model parameter reference
│   └── WARPGBM_PYTHON_GUIDE.md
├── tests/
│   ├── test_train.py
│   ├── test_predict.py
│   ├── test_integration.py
│   └── conftest.py
├── examples/
│   ├── simple_train.py
│   └── compare_models.py
├── modal_app.py             # Modal deployment config
├── local_dev.py             # Local dev server
├── requirements.txt
└── README.md

💰 Pricing (X402)

Optional micropayments on Base network:

Endpoint	Price	Description
`/train`	$0.01	Train model on GPU, get artifacts
`/predict_from_artifact`	$0.001	Batch predictions
`/predict_proba_from_artifact`	$0.001	Probability predictions
`/feedback`	Free	Help us improve!

Note: Payment is optional for demo/testing. See /.well-known/x402 for details.

🔐 Security & Privacy

✅ Stateless: No training data or models persisted
✅ Sandboxed: Runs in temporary isolated directories
✅ Size Limited: Max 50 MB request payload
✅ No Code Execution: Only structured JSON parameters
✅ Rate Limited: Per-IP throttling to prevent abuse
✅ Read-Only FS: Modal deployment uses immutable filesystem

🌍 Available Models

🚀 WarpGBM (GPU)

Acceleration: NVIDIA A10G GPUs
Speed: 13× faster than LightGBM
Best For: Time-series, financial modeling, temporal data
Special: Era-aware splitting, invariant learning
Min Samples: 60+ recommended

⚡ LightGBM (CPU)

Acceleration: Highly optimized CPU
Speed: 10-100× faster than sklearn
Best For: General tabular data, large datasets
Special: Categorical features, low memory
Min Samples: 20+

🗺️ Roadmap

💬 Feedback & Support

Help us make this service better for AI agents!

Submit feedback about:

Missing features that would unlock new use cases
Confusing documentation or error messages
Performance issues or timeout problems
Additional model types you'd like to see

# Via API
curl -X POST https://warpgbm.ai/feedback \
  -H "Content-Type: application/json" \
  -d '{
    "feedback_type": "feature_request",
    "message": "Add support for XGBoost backend",
    "severity": "medium"
  }'

Or via:

GitHub Issues: mcp-warpgbm/issues
GitHub Discussions: warpgbm/discussions
Email: support@warpgbm.ai

📚 Learn More

🐍 WarpGBM Python Package - The core library (91+ ⭐)
🤖 Agent Guide - Complete usage guide for AI agents
📖 API Docs - Interactive OpenAPI documentation
🔌 Model Context Protocol - MCP specification
💰 X402 Specification - Payment protocol for agents
☁️ Modal Docs - Serverless GPU platform

📄 License

GPL-3.0 (same as WarpGBM core)

This ensures improvements to the MCP wrapper benefit the community, while allowing commercial use through the cloud service.

🙏 Credits

Built with:

WarpGBM - GPU-accelerated GBDT library
Modal - Serverless GPU infrastructure
FastAPI - Modern Python web framework
LightGBM - Microsoft's GBDT library

Built with ❤️ for the open agent economy

⭐ Star on GitHub • 🚀 Try Live Service • 📖 Read the Docs

⚡ WarpGBM MCP Service

GPU-accelerated gradient boosting as a cloud MCP service
Train on A10G GPUs • Get artifact_id for <100ms cached predictions • Download portable artifacts

🌐 Live Service • 📖 API Docs • 🤖 Agent Guide • 🐍 Python Package

🎯 What is This?

Outsource your GBDT workload to the world's fastest GPU implementation.

🏗️ How It Works (The Smart Cache Workflow)

graph LR
    A[Train on GPU] --> B[Get artifact_id + model]
    B --> C[5min Cache]
    C --> D[<100ms Predictions]
    B --> E[Download Artifact]
    E --> F[Use Anywhere]

Train: POST your data → Train on A10G GPU → Get artifact_id + portable artifact
Fast Path: Use artifact_id → Sub-100ms cached predictions (5min TTL)
Slow Path: Use model_artifact_joblib → Download and use anywhere

Architecture: 🔒 Stateless • 🚀 No model storage • 💾 You own your artifacts

⚡ Quick Start

For AI Agents (MCP)

Add to your MCP settings (e.g., .cursor/mcp.json):

{
  "mcpServers": {
    "warpgbm": {
      "url": "https://warpgbm.ai/mcp/sse"
    }
  }
}

For Developers (REST API)

# 1. Train a model
curl -X POST https://warpgbm.ai/train \
  -H "Content-Type: application/json" \
  -d '{
    "X": [[5.1,3.5,1.4,0.2], [6.7,3.1,4.4,1.4], ...],
    "y": [0, 1, 2, ...],
    "model_type": "warpgbm",
    "objective": "multiclass"
  }'

# Response includes artifact_id for fast predictions
# {"artifact_id": "abc-123", "model_artifact_joblib": "H4sIA..."}

# 2. Make fast predictions (cached, <100ms)
curl -X POST https://warpgbm.ai/predict_from_artifact \
  -H "Content-Type: application/json" \
  -d '{
    "artifact_id": "abc-123",
    "X": [[5.0,3.4,1.5,0.2]]
  }'

🚀 Key Features

Feature	Description
🎯 Multi-Model	WarpGBM (GPU) + LightGBM (CPU)
⚡ Smart Caching	`artifact_id` → 5min cache → <100ms inference
📦 Portable Artifacts	Download joblib models, use anywhere
🤖 MCP Native	Direct tool integration for AI agents
💰 X402 Payments	Optional micropayments (Base network)
🔒 Stateless	No data storage, you own your models
🌐 Production Ready	Deployed on Modal with custom domain

🐍 Python Package vs MCP Service

This repo is the MCP service wrapper. For production ML workflows, consider using the WarpGBM Python package directly:

Feature	MCP Service (This Repo)	Python Package
Installation	None needed	`pip install git+https://...`
GPU	Cloud (pay-per-use)	Your GPU (free)
Control	REST API parameters	Full Python API
Features	Train, predict, upload	+ Cross-validation, callbacks, feature importance
Best For	Quick experiments, demos	Production pipelines, research
Cost	$0.01 per training	Free (your hardware)

Use this MCP service for: Quick tests, prototyping, agents without local GPU
Use Python package for: Production ML, research, cost savings, full control

📡 Available Endpoints

Core Endpoints

Method	Endpoint	Description
`GET`	`/models`	List available model backends
`POST`	`/train`	Train model, get artifact_id + model
`POST`	`/predict_from_artifact`	Fast predictions (artifact_id or model)
`POST`	`/predict_proba_from_artifact`	Probability predictions
`POST`	`/upload_data`	Upload CSV/Parquet for training
`POST`	`/feedback`	Submit feedback to improve service
`GET`	`/healthz`	Health check with GPU status

MCP Integration

Method	Endpoint	Description
`SSE`	`/mcp/sse`	MCP Server-Sent Events endpoint
`GET`	`/.well-known/mcp.json`	MCP capability manifest
`GET`	`/.well-known/x402`	X402 pricing manifest

💡 Complete Example: Iris Dataset

# 1. Train WarpGBM on Iris (60 samples recommended for proper binning)
curl -X POST https://warpgbm.ai/train \
  -H "Content-Type: application/json" \
  -d '{
  "X": [[5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5],
        [5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5],
        [5.1,3.5,1.4,0.2], [4.9,3,1.4,0.2], [4.7,3.2,1.3,0.2], [4.6,3.1,1.5,0.2], [5,3.6,1.4,0.2],
        [7,3.2,4.7,1.4], [6.4,3.2,4.5,1.5], [6.9,3.1,4.9,1.5], [5.5,2.3,4,1.3], [6.5,2.8,4.6,1.5],
        [6.3,3.3,6,2.5], [5.8,2.7,5.1,1.9], [7.1,3,5.9,2.1], [6.3,2.9,5.6,1.8], [6.5,3,5.8,2.2],
        [7.6,3,6.6,2.1], [4.9,2.5,4.5,1.7], [7.3,2.9,6.3,1.8], [6.7,2.5,5.8,1.8], [7.2,3.6,6.1,2.5]],
  "y": [0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,
        0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,
        0,0,0,0,0, 1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2],
  "model_type": "warpgbm",
  "objective": "multiclass",
  "n_estimators": 100
}'

# Response:
{
  "artifact_id": "abc123-def456-ghi789",
  "model_artifact_joblib": "H4sIA...",
  "training_time_seconds": 0.0
}

# 2. Fast inference with cached artifact_id (<100ms)
curl -X POST https://warpgbm.ai/predict_from_artifact \
  -H "Content-Type: application/json" \
  -d '{
  "artifact_id": "abc123-def456-ghi789",
  "X": [[5,3.4,1.5,0.2], [6.7,3.1,4.4,1.4], [7.7,3.8,6.7,2.2]]
}'

# Response: {"predictions": [0, 1, 2], "inference_time_seconds": 0.05}
# Perfect classification! ✨

⚠️ Important: WarpGBM uses quantile binning which requires 60+ samples for proper training. With fewer samples, the model can't learn proper decision boundaries.

🏠 Self-Hosting

Local Development

# Clone repo
git clone https://github.com/jefferythewind/mcp-warpgbm.git
cd mcp-warpgbm

# Setup environment
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Run locally (GPU optional for dev)
uvicorn local_dev:app --host 0.0.0.0 --port 8000 --reload

# Test
curl http://localhost:8000/healthz

# Install Modal
pip install modal

# Authenticate
modal token new

# Deploy
modal deploy modal_app.py

# Service will be live at your Modal URL

Deploy to Other Platforms

# Docker (requires GPU)
docker build -t warpgbm-mcp .
docker run --gpus all -p 8000:8000 warpgbm-mcp

# Fly.io, Railway, Render, etc.
# See their respective GPU deployment docs

🧪 Testing

# Install dev dependencies
pip install -r requirements-dev.txt

# Run all tests
./run_tests.sh

# Or use pytest directly
pytest tests/ -v

# Test specific functionality
pytest tests/test_train.py -v
pytest tests/test_integration.py -v

📦 Project Structure

mcp-warpgmb/
├── app/
│   ├── main.py              # FastAPI app + routes
│   ├── mcp_sse.py           # MCP Server-Sent Events
│   ├── model_registry.py    # Model backend registry
│   ├── models.py            # Pydantic schemas
│   ├── utils.py             # Serialization, caching
│   ├── x402.py              # Payment verification
│   └── feedback_storage.py  # Feedback persistence
├── .well-known/
│   ├── mcp.json             # MCP capability manifest
│   └── x402                 # X402 pricing manifest
├── docs/
│   ├── AGENT_GUIDE.md       # Comprehensive agent docs
│   ├── MODEL_SUPPORT.md     # Model parameter reference
│   └── WARPGBM_PYTHON_GUIDE.md
├── tests/
│   ├── test_train.py
│   ├── test_predict.py
│   ├── test_integration.py
│   └── conftest.py
├── examples/
│   ├── simple_train.py
│   └── compare_models.py
├── modal_app.py             # Modal deployment config
├── local_dev.py             # Local dev server
├── requirements.txt
└── README.md

💰 Pricing (X402)

Optional micropayments on Base network:

Endpoint	Price	Description
`/train`	$0.01	Train model on GPU, get artifacts
`/predict_from_artifact`	$0.001	Batch predictions
`/predict_proba_from_artifact`	$0.001	Probability predictions
`/feedback`	Free	Help us improve!

Note: Payment is optional for demo/testing. See /.well-known/x402 for details.

🔐 Security & Privacy

🌍 Available Models

🚀 WarpGBM (GPU)

Acceleration: NVIDIA A10G GPUs
Speed: 13× faster than LightGBM
Best For: Time-series, financial modeling, temporal data
Special: Era-aware splitting, invariant learning
Min Samples: 60+ recommended

⚡ LightGBM (CPU)

Acceleration: Highly optimized CPU
Speed: 10-100× faster than sklearn
Best For: General tabular data, large datasets
Special: Categorical features, low memory
Min Samples: 20+

🗺️ Roadmap

💬 Feedback & Support

Help us make this service better for AI agents!

Submit feedback about:

Missing features that would unlock new use cases
Confusing documentation or error messages
Performance issues or timeout problems
Additional model types you'd like to see

# Via API
curl -X POST https://warpgbm.ai/feedback \
  -H "Content-Type: application/json" \
  -d '{
    "feedback_type": "feature_request",
    "message": "Add support for XGBoost backend",
    "severity": "medium"
  }'

Or via:

GitHub Issues: mcp-warpgbm/issues
GitHub Discussions: warpgbm/discussions
Email: support@warpgbm.ai

📚 Learn More

🐍 WarpGBM Python Package - The core library (91+ ⭐)
🤖 Agent Guide - Complete usage guide for AI agents
📖 API Docs - Interactive OpenAPI documentation
🔌 Model Context Protocol - MCP specification
💰 X402 Specification - Payment protocol for agents
☁️ Modal Docs - Serverless GPU platform

📄 License

GPL-3.0 (same as WarpGBM core)

This ensures improvements to the MCP wrapper benefit the community, while allowing commercial use through the cloud service.

🙏 Credits

Built with:

WarpGBM - GPU-accelerated GBDT library
Modal - Serverless GPU infrastructure
FastAPI - Modern Python web framework
LightGBM - Microsoft's GBDT library

Built with ❤️ for the open agent economy

⭐ Star on GitHub • 🚀 Try Live Service • 📖 Read the Docs

WarpGBM MCP Service

⚡ WarpGBM MCP Service

🎯 What is This?

🏗️ How It Works (The Smart Cache Workflow)

⚡ Quick Start

For AI Agents (MCP)

For Developers (REST API)

🚀 Key Features

🐍 Python Package vs MCP Service

📡 Available Endpoints

Core Endpoints

MCP Integration

💡 Complete Example: Iris Dataset

🏠 Self-Hosting

Local Development

Deploy to Modal (Production)

Deploy to Other Platforms

🧪 Testing

📦 Project Structure

💰 Pricing (X402)

🔐 Security & Privacy

🌍 Available Models

🚀 WarpGBM (GPU)

⚡ LightGBM (CPU)

🗺️ Roadmap

💬 Feedback & Support

📚 Learn More

📄 License

🙏 Credits

⚡ WarpGBM MCP Service

🎯 What is This?

🏗️ How It Works (The Smart Cache Workflow)

⚡ Quick Start

For AI Agents (MCP)

For Developers (REST API)

🚀 Key Features

🐍 Python Package vs MCP Service

📡 Available Endpoints

Core Endpoints

MCP Integration

💡 Complete Example: Iris Dataset

🏠 Self-Hosting

Local Development

Deploy to Modal (Production)

Deploy to Other Platforms

🧪 Testing

📦 Project Structure

💰 Pricing (X402)

🔐 Security & Privacy

🌍 Available Models

🚀 WarpGBM (GPU)

⚡ LightGBM (CPU)

🗺️ Roadmap

💬 Feedback & Support

📚 Learn More

📄 License

🙏 Credits