Alternatives to TensorFlow.js logo

Alternatives to TensorFlow.js

TensorFlow, Python, Tensorflow Lite, Keras, and PyTorch are the most popular alternatives and competitors to TensorFlow.js.
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What is TensorFlow.js and what are its top alternatives?

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.js is a tool in the Machine Learning Tools category of a tech stack.
TensorFlow.js is an open source tool with 15.2K GitHub stars and 1.5K GitHub forks. Here’s a link to TensorFlow.js's open source repository on GitHub

Top Alternatives to TensorFlow.js

  • TensorFlow

    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. ...

  • Python

    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • Tensorflow Lite

    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. ...

  • Keras

    Keras

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...

  • PyTorch

    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

    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. ...

  • CUDA

    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. ...

  • Kubeflow

    Kubeflow

    The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. ...

TensorFlow.js alternatives & related posts

TensorFlow logo

TensorFlow

2.5K
2.7K
76
Open Source Software Library for Machine Intelligence
2.5K
2.7K
+ 1
76
PROS OF TENSORFLOW
  • 24
    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
CONS OF TENSORFLOW
  • 8
    Hard
  • 5
    Hard to debug
  • 1
    Documentation not very helpful

related TensorFlow posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.2M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

See more
Python logo

Python

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105.7K
6.5K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
130.5K
105.7K
+ 1
6.5K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 928
    Readable code
  • 817
    Beautiful code
  • 768
    Rapid development
  • 671
    Large community
  • 418
    Open source
  • 379
    Elegant
  • 268
    Great community
  • 261
    Object oriented
  • 209
    Dynamic typing
  • 70
    Great standard library
  • 52
    Very fast
  • 48
    Functional programming
  • 35
    Scientific computing
  • 33
    Easy to learn
  • 30
    Great documentation
  • 25
    Matlab alternative
  • 23
    Productivity
  • 22
    Easy to read
  • 19
    Simple is better than complex
  • 17
    It's the way I think
  • 17
    Imperative
  • 15
    Very programmer and non-programmer friendly
  • 14
    Powerful
  • 14
    Free
  • 13
    Fast and simple
  • 13
    Powerfull language
  • 12
    Scripting
  • 9
    Machine learning support
  • 9
    Explicit is better than implicit
  • 8
    Unlimited power
  • 8
    Ease of development
  • 7
    Import antigravity
  • 7
    Clear and easy and powerfull
  • 6
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Flat is better than nested
  • 5
    Fast coding and good for competitions
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    Python has great libraries for data processing
  • 5
    High Documented language
  • 5
    I love snakes
  • 5
    Although practicality beats purity
  • 5
    Great for tooling
  • 4
    Readability counts
  • 3
    CG industry needs
  • 3
    Beautiful is better than ugly
  • 3
    Multiple Inheritence
  • 3
    Complex is better than complicated
  • 3
    Great for analytics
  • 3
    Socially engaged community
  • 3
    Rapid Prototyping
  • 3
    Lists, tuples, dictionaries
  • 3
    Plotting
  • 2
    Generators
  • 2
    Simple and easy to learn
  • 2
    Import this
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 2
    List comprehensions
  • 2
    Special cases aren't special enough to break the rules
  • 2
    Now is better than never
  • 2
    If the implementation is hard to explain, it's a bad id
  • 2
    If the implementation is easy to explain, it may be a g
  • 1
    Many types of collections
  • 1
    Better outcome
  • 1
    Batteries included
  • 1
    Ys
  • 1
    Good
  • 1
    Pip install everything
  • 1
    Easy to setup and run smooth
  • 1
    Because of Netflix
  • 1
    Flexible and easy
  • 1
    Web scraping
  • 1
    Should START with this but not STICK with This
  • 1
    Powerful language for AI
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Only one way to do it
  • 1
    A-to-Z
  • 0
    Pro
  • 0
    Powerful
CONS OF PYTHON
  • 50
    Still divided between python 2 and python 3
  • 27
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 19
    Package management is a mess
  • 19
    GIL
  • 13
    Too imperative-oriented
  • 12
    Hard to understand
  • 11
    Dynamic typing
  • 9
    Very slow
  • 8
    Not everything is expression
  • 7
    Explicit self parameter in methods
  • 7
    Indentations matter a lot
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 6
    Requires C functions for dynamic modules
  • 5
    Threading
  • 5
    The "lisp style" whitespaces
  • 5
    Hard to obfuscate
  • 4
    Fake object-oriented programming
  • 4
    Incredibly slow
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 3
    Official documentation is unclear.
  • 3
    Circular import
  • 3
    Not suitable for autocomplete
  • 1
    Training wheels (forced indentation)
  • 1
    Meta classes

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 38 upvotes · 3.7M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Nick Parsons
Director of Developer Marketing at Stream · | 35 upvotes · 1.3M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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Tensorflow Lite logo

Tensorflow Lite

43
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0
Deploy machine learning models on mobile and IoT devices
43
82
+ 1
0
PROS OF TENSORFLOW LITE
    Be the first to leave a pro
    CONS OF TENSORFLOW LITE
      Be the first to leave a con

      related Tensorflow Lite posts

      Keras logo

      Keras

      870
      906
      12
      Deep Learning library for Theano and TensorFlow
      870
      906
      + 1
      12
      PROS OF KERAS
      • 5
        Quality Documentation
      • 4
        Easy and fast NN prototyping
      • 3
        Supports Tensorflow and Theano backends
      CONS OF KERAS
      • 3
        Hard to debug

      related Keras posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.2M views

      Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

      At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

      TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

      Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

      https://eng.uber.com/horovod/

      (Direct GitHub repo: https://github.com/uber/horovod)

      See more

      I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

      See more
      PyTorch logo

      PyTorch

      867
      966
      33
      A deep learning framework that puts Python first
      867
      966
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      33
      PROS OF PYTORCH
      • 11
        Easy to use
      • 9
        Developer Friendly
      • 8
        Easy to debug
      • 5
        Sometimes faster than TensorFlow
      CONS OF PYTORCH
      • 2
        Lots of code

      related PyTorch posts

      Server side

      We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

      • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

      • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

      • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

      Client side

      • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

      • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

      • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

      Cache

      • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

      Database

      • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

      Infrastructure

      • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

      Other Tools

      • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

      • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

      See more
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.2M views

      Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

      At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

      TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

      Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

      https://eng.uber.com/horovod/

      (Direct GitHub repo: https://github.com/uber/horovod)

      See more
      scikit-learn logo

      scikit-learn

      830
      885
      32
      Easy-to-use and general-purpose machine learning in Python
      830
      885
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      32
      PROS OF SCIKIT-LEARN
      • 18
        Scientific computing
      • 14
        Easy
      CONS OF SCIKIT-LEARN
      • 1
        Limited

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      CUDA logo

      CUDA

      152
      112
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      It provides everything you need to develop GPU-accelerated applications
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      PROS OF CUDA
        Be the first to leave a pro
        CONS OF CUDA
          Be the first to leave a con

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          Kubeflow logo

          Kubeflow

          131
          435
          13
          Machine Learning Toolkit for Kubernetes
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          PROS OF KUBEFLOW
          • 5
            System designer
          • 3
            Customisation
          • 3
            Kfp dsl
          • 2
            Google backed
          CONS OF KUBEFLOW
            Be the first to leave a con

            related Kubeflow posts

            Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

            See more