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google/yggdrasil-decision-forests

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660 نجوم·80 تفرعات·C++·Apache-2.0·1 مشاهدةydf.readthedocs.io↗

Yggdrasil Decision Forests

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

Features

  • Computation and Optimization - Library for training and serving decision forest models.

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بدائل مفتوحة المصدر لـ Yggdrasil Decision Forests

مشاريع مفتوحة المصدر مشابهة، مرتبة حسب عدد الميزات المشتركة مع Yggdrasil Decision Forests.
  • arogozhnikov/einopsالصورة الرمزية لـ arogozhnikov

    arogozhnikov/einops

    9,398عرض على GitHub↗

    Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein summation, and multi-dimensional array operations. It serves as an abstraction layer that works across NumPy, PyTorch, TensorFlow, and JAX, allowing for tensor transformations without changing the API. The library distinguishes itself through a declarative notation system that uses readable string patterns to describe tensor rearrangements and reductions. This approach includes an extended Einstein summation interface that supports multi-letter axis names and a named dimension mapping

    Pythoncupydeep-learningeinops
    عرض على GitHub↗9,398
  • bitsandbytes-foundation/bitsandbytesالصورة الرمزية لـ bitsandbytes-foundation

    bitsandbytes-foundation/bitsandbytes

    7,968عرض على GitHub↗

    bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large language models. It serves as a GPU memory optimizer and quantization framework, compressing model weights and features to 8-bit and 4-bit precision to enable inference and training on hardware with limited memory. The project provides a framework for low-rank adaptation, allowing the fine-tuning of quantized models by combining 4-bit weights with small trainable matrices. It further distinguishes itself through memory paging, which moves optimizer states between CPU and GPU memory t

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  • cupy/cupyالصورة الرمزية لـ cupy

    cupy/cupy

    11,000عرض على GitHub↗

    CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and numerical computing on NVIDIA GPUs. It serves as a GPU-accelerated numerical library and a CUDA-based SciPy implementation, offloading heavy calculations to graphics hardware to increase processing speed for scientific and engineering workloads. The library enables multi-framework tensor exchange, allowing data buffers to be shared between different deep learning frameworks using standardized memory layouts to avoid memory copies. It also supports custom GPU kernel integratio

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  • adapter-hub/adaptersالصورة الرمزية لـ adapter-hub

    adapter-hub/adapters

    2,815عرض على GitHub↗

    A Unified Library for Parameter-Efficient and Modular Transfer Learning

    Python
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عرض جميع البدائل الـ 30 لـ Yggdrasil Decision Forests→

الأسئلة الشائعة

ما هي وظيفة google/yggdrasil-decision-forests؟

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

ما هي الميزات الرئيسية لـ google/yggdrasil-decision-forests؟

الميزات الرئيسية لـ google/yggdrasil-decision-forests هي: Computation and Optimization.

ما هي البدائل مفتوحة المصدر لـ google/yggdrasil-decision-forests؟

تشمل البدائل مفتوحة المصدر لـ google/yggdrasil-decision-forests: arogozhnikov/einops — Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein… bitsandbytes-foundation/bitsandbytes — bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large… cupy/cupy — CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and… dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… deap/deap. adapter-hub/adapters — A Unified Library for Parameter-Efficient and Modular Transfer Learning.