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GraphPipe vs TensorFlow.js

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GraphPipe
2
16
+ 1
0
TensorFlow.js
179
375
+ 1
18
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GraphPipe vs TensorFlow.js: What are the differences?

GraphPipe: Machine Learning Model Deployment Made Simple, by Oracle. GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations; TensorFlow.js: Machine Learning in JavaScript. Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

GraphPipe and TensorFlow.js can be categorized as "Machine Learning" tools.

GraphPipe and TensorFlow.js are both open source tools. It seems that TensorFlow.js with 11.2K GitHub stars and 816 forks on GitHub has more adoption than GraphPipe with 643 GitHub stars and 91 GitHub forks.

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Pros of GraphPipe
Pros of TensorFlow.js
    Be the first to leave a pro
    • 6
      Open Source
    • 5
      NodeJS Powered
    • 2
      Deploy python ML model directly into javascript
    • 1
      Cost - no server needed for inference
    • 1
      Privacy - no data sent to server
    • 1
      Runs Client Side on device
    • 1
      Can run TFJS on backend, frontend, react native, + IOT
    • 1
      Easy to share and use - get more eyes on your research

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    What is GraphPipe?

    GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.

    What is TensorFlow.js?

    Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

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

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    What companies use GraphPipe?
    What companies use TensorFlow.js?
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    What tools integrate with GraphPipe?
    What tools integrate with TensorFlow.js?

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    What are some alternatives to GraphPipe and TensorFlow.js?
    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

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