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Caffe2

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Caffe2 vs Chainer: What are the differences?

What is Caffe2? Open Source Cross-Platform Machine Learning Tools (by Facebook). Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.

What is Chainer? A Powerful, Flexible, and Intuitive Framework for Neural Networks. It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

Caffe2 and Chainer belong to "Machine Learning Tools" category of the tech stack.

Caffe2 and Chainer are both open source tools. Caffe2 with 8.46K GitHub stars and 2.12K forks on GitHub appears to be more popular than Chainer with 4.98K GitHub stars and 1.32K GitHub forks.

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    What is Caffe2?

    Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.

    What is Chainer?

    It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

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    What companies use Caffe2?
    What companies use Chainer?
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    What tools integrate with Caffe2?
    What tools integrate with Chainer?
    What are some alternatives to Caffe2 and Chainer?
    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.
    Caffe
    It is a deep learning framework made with expression, speed, and modularity in mind.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    Tensorflow Lite
    It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
    See all alternatives