Leaf vs PredictionIO vs TensorFlow

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Leaf

20
40
+ 1
0
PredictionIO

66
103
+ 1
5
TensorFlow

2.6K
2.8K
+ 1
77
Decisions about Leaf, PredictionIO, and TensorFlow

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 · 52.7K 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 Leaf
Pros of PredictionIO
Pros of TensorFlow
    Be the first to leave a pro
    • 5
      Predict Future
    • 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

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

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        What is Leaf?

        Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.

        What is PredictionIO?

        PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

        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.

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        What companies use Leaf?
        What companies use PredictionIO?
        What companies use TensorFlow?

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        What tools integrate with Leaf?
        What tools integrate with PredictionIO?
        What tools integrate with TensorFlow?
          No integrations found

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          What are some alternatives to Leaf, PredictionIO, and TensorFlow?
          Leaflet
          Leaflet is an open source JavaScript library for mobile-friendly interactive maps. It is developed by Vladimir Agafonkin of MapBox with a team of dedicated contributors. Weighing just about 30 KB of gzipped JS code, it has all the features most developers ever need for online maps.
          Volt
          Volt is a ruby web framework where your ruby code runs on both the server and the client (via opal.) The DOM automatically update as the user interacts with the page. Page state can be stored in the URL, if the user hits a URL directly, the HTML will first be rendered on the server for faster load times and easier indexing by search engines.
          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.
          Keras
          Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
          scikit-learn
          scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
          See all alternatives