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PyTorch

986
1.1K
+ 1
42
Tensorflow Lite

47
87
+ 1
0
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PyTorch vs Tensorflow Lite: What are the differences?

Developers describe PyTorch as "A deep learning framework that puts Python first". 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. On the other hand, Tensorflow Lite is detailed as "Deploy machine learning models on mobile and IoT devices". 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.

PyTorch and Tensorflow Lite belong to "Machine Learning Tools" category of the tech stack.

PyTorch is an open source tool with 37.4K GitHub stars and 9.54K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.

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Pros of PyTorch
Pros of Tensorflow Lite
  • 14
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
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    Cons of PyTorch
    Cons of Tensorflow Lite
    • 3
      Lots of code
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      - No public GitHub repository available -

      What is 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.

      What is 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.

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

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      What are some alternatives to PyTorch and Tensorflow Lite?
      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.
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      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.
      MXNet
      A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
      Torch
      It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
      See all alternatives