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


Keywords
analytics-zoo, apache-spark, bigdl, deep-neural-network, distributed-deep-learning, keras-tensorflow, python, pytorch, scala
Licenses
Apache-2.0/libpng-2.0
Install
pip install analytics-zoo==0.8.1

Documentation


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


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.


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).

How to use Analytics Zoo?