Warpgbm Mcp

Name: Warpgbm Mcp
Author: jefferythewind

@jefferythewind

リポジトリ

Warpgbm Mcp について

概要はまだありません

#warpgbm #gbdt #gpu-accelerated

基本情報

カテゴリ

その他

トランスポート

stdio

公開者

jefferythewind

投稿者

Timothy DeLise

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"
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

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

FAQ from WarpGBM

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.