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Neuropod

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XGBoost vs Neuropod: What are the differences?

Developers describe XGBoost as "Scalable and Flexible Gradient Boosting". Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow. On the other hand, Neuropod is detailed as "Uber ATG's open source deep learning inference engine". It is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. It makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.

XGBoost can be classified as a tool in the "Python Build Tools" category, while Neuropod is grouped under "Machine Learning Tools".

Some of the features offered by XGBoost are:

  • Flexible
  • Portable
  • Multiple Languages

On the other hand, Neuropod provides the following key features:

  • Run models from any supported framework using one API
  • Build generic tools and pipelines
  • Fully self-contained models

XGBoost is an open source tool with 19.2K GitHub stars and 7.55K GitHub forks. Here's a link to XGBoost's open source repository on GitHub.

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What is Neuropod?

It is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. It makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.

What is XGBoost?

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

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    What are some alternatives to Neuropod and XGBoost?
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    PyTorch
    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
    scikit-learn
    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    CUDA
    A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
    See all alternatives