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TensorFlow vs TensorFlow.js: What are the differences?

TensorFlow: Open Source Software Library for Machine Intelligence. 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; 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.

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

TensorFlow.js is an open source tool with 11.2K GitHub stars and 816 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.

Uber Technologies, 9GAG, and StyleShare Inc. are some of the popular companies that use TensorFlow, whereas TensorFlow.js is used by 8villages, ADEXT, and Taralite. TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to TensorFlow.js, which is listed in 5 company stacks and 3 developer stacks.

Decisions about TensorFlow and TensorFlow.js

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 53.8K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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Pros of TensorFlow
Pros of TensorFlow.js
  • 25
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful
  • 4
    NodeJS Powered
  • 4
    Open Source
  • 1
    Deploy python ML model directly into javascript

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Cons of TensorFlow
Cons of TensorFlow.js
  • 9
    Hard
  • 5
    Hard to debug
  • 1
    Documentation not very helpful
    Be the first to leave a con

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    What is 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.

    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!

    Jobs that mention TensorFlow and TensorFlow.js as a desired skillset
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    What tools integrate with TensorFlow?
    What tools integrate with TensorFlow.js?

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    Blog Posts

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    What are some alternatives to TensorFlow and TensorFlow.js?
    Theano
    Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
    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.
    OpenCV
    OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    See all alternatives