Warpgbm MCP Server

@jefferythewind

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トランスポート

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作者

jefferythewind

投稿者

Timothy DeLise

Warpgbm MCP Server について

概要はまだありません

#warpgbm#gbdt#gpu-accelerated

Warpgbm MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Warpgbm MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンでサーバーのエンドポイントに接続し、ツール一覧を取得できます。

概要

What is WarpGBM?

WarpGBM is a high-performance, GPU-accelerated Gradient Boosted Decision Tree (GBDT) library built with PyTorch and custom CUDA kernels. It supports regression, binary, and multiclass classification and includes an invariant learning algorithm (DES) that learns signals stable across shifting distributions. It is for data scientists and machine learning engineers who need speed and distribution-robust models.

How to use WarpGBM?

Install via pip install warpgbm or pip install git+https://github.com/jefferythewind/warpgbm.git. Requires PyTorch with CUDA support. Use the Scikit-Learn-compatible API: create a WarpGBM object with parameters like objective, max_depth, n_estimators, call fit(X, y) and predict(X). For invariant learning, pass an era_id array to fit.

Key features of WarpGBM

GPU-native CUDA kernels for binning, histograms, splits, and inference
Unified infrastructure for regression, binary, and multiclass classification
Invariant learning via Directional Era-Splitting (DES) algorithm
Scikit-Learn compatible – drop‑in replacement
Warm start and checkpointing for incremental training
Pre-binned data optimization and mixed precision (float32/int8) support

Use cases of WarpGBM

Training high-speed gradient boosting models on large datasets (1M+ rows)
Building models that generalize across shifting market regimes or time periods
Multiclass classification with early stopping and rich evaluation metrics
Incremental model development and production updates without retraining from scratch
Hyperparameter tuning with checkpointing to decide on the number of trees

よくある質問

How does WarpGBM compare to XGBoost, LightGBM, and CatBoost?

On synthetic data (1M rows × 1K features, Google Colab L4 GPU), WarpGBM trains 2× faster than XGBoost and 23× faster than CatBoost while achieving comparable correlation. LightGBM inference is faster, but WarpGBM trains similarly to LightGBM and beats it on pre-binned Numerai data (13× faster).

What are the prerequisites for installing WarpGBM?

PyTorch with CUDA support is required. The library uses custom CUDA kernels; a GPU is strongly recommended, though CPU mode (`device='cpu'`) is available.

Can I train a model without a GPU?

Yes, set `device='cpu'` in the constructor. However, performance will be significantly slower – GPU acceleration is a core feature.

What is the invariant learning feature (DES)?

When you pass an `era_id` array (marking which time period/regime

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