MMLSpark provides Apache Spark with a number of deep learning and data science tools, including seamless integration of Spark Machine Learning pipeline with Microsoft CogniTIve Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly scalable large images and texts The data set analyzes the prediction model.
Microsoft has open sourced MMLSpark, a deep learning library for Apache Spark. MMLSpark is perfectly integrated with the Microsoft Cognitive Toolkit and OpenCV.
Microsoft found that while SparkML can build an extensible machine learning platform, the vast majority of developers' energy is spent calling the underlying API. MMLSpark is designed to simplify repetitive work in PySpark.
For example, UCI's adult income census data set uses other items to predict income:
If you use SparkML directly, each column needs to be processed separately and organized into the correct data type; only two lines of code are needed in MMLSpark:
Deep neural networks (DNN) are not inferior to humans in the fields of image recognition and speech recognition, but the training of DNN models requires professionals to perform, and integration with SparkML is also very difficult. MMLSpark provides a convenient Python API to easily train DNN algorithms. MMLSpark makes it easy to use existing models for classification tasks, training on distributed GPU nodes, and building scalable image processing pipelines using OpenCV.
The following three lines of code can initialize a DNN model from the Microsoft Cognitive Toolset to extract features from the image:
MMLSpark has been released to Docker Hub and can be deployed on a stand-alone basis using the following commands:
MMLSpark is licensed under the MIT protocol.
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