analytics-zoo/graphene

Graphene / Graphene-SGX - a library OS for Linux multi-process applications, with Intel SGX support

A unified Data Analytics and AI platform for distributed TensorFlow, Keras, PyTorch, Apache Spark/Flink and Ray

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What is Analytics Zoo?

Analytics Zoo provides a unified data analytics and AI platform that seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data.

• Integrated Analytics and AI Pipelines for easily prototyping and deploying end-to-end AI applications.

  • Write TensorFlow or PyTorch inline with Spark code for distributed training and inference.
  • Native deep learning (TensorFlow/Keras/PyTorch/BigDL) support in Spark ML Pipelines.
  • Directly run Ray programs on big data cluster through RayOnSpark.
  • Plain Java/Python APIs for (TensorFlow/PyTorch/BigDL/OpenVINO) Model Inference.

• High-Level ML Workflow that automates the process of building large-scale machine learning applications.

  • Automatically distributed Cluster Serving (for TensorFlow/PyTorch/Caffe/BigDL/OpenVINO models) with a simple pub/sub API.
  • Scalable AutoML for time series prediction (that automatically generates features, selects models and tunes hyperparameters).

• Built-in Algorithms and Models for Recommendation, Time Series, Computer Vision and NLP applications.

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Why use Analytics Zoo?

You may want to develop your AI solutions using Analytics Zoo if:

• You want to easily prototype the entire end-to-end pipeline that applies AI models (e.g., TensorFlow, Keras, PyTorch, BigDL, OpenVINO, etc.) to production big data.
• You want to transparently scale your AI applications from a laptop to large clusters with "zero" code changes.
• You want to deploy your AI pipelines to existing YARN or K8S clusters WITHOUT any modifications to the clusters.
• You want to automate the process of applying machine learning (such as feature engineering, hyperparameter tuning, model selection and distributed inference).

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How to use Analytics Zoo?

• Check out the Getting Started page for a quick overview of how to use Analytics Zoo.
• Refer to the Python, Scala and Docker guides to install Analytics Zoo.
• Visit the Document Website (mirror in China) for more information on Analytics Zoo.
• Check the Powered By & Presentations pages for real-world applications using Analytics Zoo.
• Join the Google Group (or subscribe to the Mail List) for more questions and discussions on Analytics Zoo.