TensorFlow聽vs聽TransmogrifAI

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TensorFlow

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2.6K
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75
TransmogrifAI

4
16
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0
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TensorFlow vs TransmogrifAI: 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; TransmogrifAI: Automated machine learning for structured data (by Salesforce). TransmogrifAI (pronounced tr膬ns-m艔g藞r蓹-f墨) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning.

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

TransmogrifAI is an open source tool with 1.57K GitHub stars and 271 GitHub forks. Here's a link to TransmogrifAI's open source repository on GitHub.

Decisions about TensorFlow and TransmogrifAI

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 路 38.7K 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 TensorFlow
Pros of TransmogrifAI
  • 23
    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 TensorFlow
    Cons of TransmogrifAI
    • 8
      Hard
    • 5
      Hard to debug
    • 1
      Documentation not very helpful
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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 TransmogrifAI?

      TransmogrifAI (pronounced tr膬ns-m艔g藞r蓹-f墨) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning

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

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      What companies use TransmogrifAI?
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      What tools integrate with TensorFlow?
      What tools integrate with TransmogrifAI?

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      What are some alternatives to TensorFlow and TransmogrifAI?
      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
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