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NVIDIA/NVTabular

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0 estrellas·0 forks·5 vistas

NVTabular

Features

  • Big Data and Distributed Computing - Feature engineering for large-scale tabular data.

Historial de estrellas

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Alternativas open-source a NVTabular

Proyectos open-source similares, clasificados según cuántas características comparten con NVTabular.
  • dask/daskAvatar de dask

    dask/dask

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    Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl

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  • google/tensorstoreAvatar de google

    google/tensorstore

    1,522Ver en GitHub↗

    Library for reading and writing large multi-dimensional arrays.

    C++
    Ver en GitHub↗1,522
  • h2oai/h2o-3Avatar de h2oai

    h2oai/h2o-3

    7,493Ver en GitHub↗

    h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i

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  • cupy/cupyAvatar de cupy

    cupy/cupy

    11,000Ver en 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

    Python
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Ver las 9 alternativas a NVTabular→

Preguntas frecuentes

¿Cuáles son las características principales de nvidia/nvtabular?

Las características principales de nvidia/nvtabular son: Big Data and Distributed Computing.

¿Qué alternativas de código abierto existen para nvidia/nvtabular?

Las alternativas de código abierto para nvidia/nvtabular incluyen: 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… google/tensorstore — Library for reading and writing large multi-dimensional arrays. h2oai/h2o-3 — h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and… kwgoodman/bottleneck — Fast NumPy array functions written in C. rapidsai/cudf — cuDF is a GPU-accelerated dataframe library and data processing engine designed for manipulating and analyzing large…