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Cortex.dev

7
19
+ 1
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Torch

271
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Torch vs Cortex.dev: What are the differences?

Torch: An open-source machine learning library and a script language based on the Lua programming language. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation; Cortex.dev: Deploy machine learning models in production. It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

Torch and Cortex.dev belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Torch are:

  • A powerful N-dimensional array
  • Lots of routines for indexing, slicing, transposing
  • Amazing interface to C, via LuaJIT

On the other hand, Cortex.dev provides the following key features:

  • Autoscaling
  • Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more
  • CPU / GPU support

Torch and Cortex.dev are both open source tools. It seems that Torch with 8.42K GitHub stars and 2.34K forks on GitHub has more adoption than Cortex.dev with 1.42K GitHub stars and 69 GitHub forks.

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What is Cortex.dev?

It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

What is Torch?

It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Cortex.dev?
What companies use Torch?
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    What tools integrate with Cortex.dev?
    What tools integrate with Torch?

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    What are some alternatives to Cortex.dev and Torch?
    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