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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    MXNet

    N/AN/AApache MXNet is a deep learning framework used to mix symbolic and imperative programming to maximize productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. It is free and open-source under the Apache 2.0 license.

    $0

    per month

    Pytorch

    Score9.4 out of 10
    N/APytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.N/A
    Pricing
    MXNetPytorch
    Editions & Modules
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    Offerings
    Pricing Offerings
    MXNetPytorch
    Free Trial
    NoNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Community Pulse
    MXNetPytorch
    Considered Both Products
    Apache
    No answer on this topic
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    100%
    Would buy again
    6 Answers
    Delivers good value for the price
    No answers on this topic
    100%
    Delivers good value for the price
    6 Answers
    Happy with the feature set
    No answers on this topic
    100%
    Happy with the feature set
    6 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    100%
    Lived up to sales and marketing promises
    5 Answers
    Implementation went as expected
    No answers on this topic
    100%
    Implementation went as expected
    5 Answers
    Best Alternatives
    MXNetPytorch
    Small Businesses
    TensorFlow
    Score8.2 out of 10
    TensorFlow
    Score8.2 out of 10
    Medium-sized Companies
    Google Cloud AI
    Score8.7 out of 10
    Google Cloud AI
    Score8.7 out of 10
    Enterprises
    Google Cloud AI
    Score8.7 out of 10
    Google Cloud AI
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    MXNetPytorch
    Likelihood to Recommend
    -
    (0 ratings)
    9.0
    (6 ratings)
    Usability
    -
    (0 ratings)
    10.0
    (1 ratings)
    User Testimonials
    MXNetPytorch
    Likelihood to Recommend
    Apache
    No answers on this topic
    Open Source
    They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.
    Incentivized
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    Pros
    Apache
    No answers on this topic
    Open Source
    • flexibility
    • Clean code, close to the algorithm.
    • Fast
    • Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
    • Versatile, can work efficiently on text/audio/image/tabular datasets.
    Incentivized
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    Cons
    Apache
    No answers on this topic
    Open Source
    • Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow
    Incentivized
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    Usability
    Apache
    No answers on this topic
    Open Source
    The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.
    Incentivized
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    Alternatives Considered
    Apache
    No answers on this topic
    Open Source
    Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.
    Incentivized
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    Return on Investment
    Apache
    No answers on this topic
    Open Source
    • The ability to make models as never before
    • Being able to control the bias of models was not done before the arrival of Pytorch in our company
    Incentivized
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