This Getting Started document provides quick reference information regarding installing Analytics Zoo, running the applications, and developing your own applications using Analytics Zoo.
1. Try Analytics Zoo
Users can easily try Analytics Zoo with Docker or Google Colab without installing it. For more information:
Please also check the examples for various Analytics Zoo features (such as distributed TensorFlow and PyTorch on Spark, DL support in Spark ML pipeline, RayOnSpark, Cluster Serving, AutoML, etc.)
2. Install Analytics Zoo
Analytics Zoo installation methods are available for Python and Scala users.
- Check the Python User Guide for how to install Analytics Zoo in Python program environment.
- Check the Scala User Guide for how to install Analytics Zoo in Scala program environment.
3. Run Analytics Zoo Applications
Analytics Zoo applications can run on remote or cloud resources, such as YARN, K8s, Databricks, or Google Dataproc.
3.1 Run on YARN
- Python users can follow the instructions in Python User Guide to run Analytics Zoo applications on YARN.
- Scala users can follow the instructions in Scala User Guide to run Analytics Zoo applications on YARN.
3.2 Run on K8s
- Check the instructions for how to run Analytics Zoo applicaitons on K8s.
3.3 Run on Databricks
- Check the instructions for how to run Analytics Zoo applicaitons on Databricks.
3.4 Run on Google Dataproc
- Check the instructions for how to run Analytics Zoo applications on Google Dataproc environment.
4. Develop Analytics Zoo Applications
Analytics Zoo provides comprehensive support for for building end-to-end, integrated data analytics and AI applications.
- TensorFlow users can leverage TFPark APIs for running distributed TensorFlow on Spark.
Pytorch users can user either:
- NNFrame APIs to run Spark ML Pipeline and Dataframe with PyTorch support, or
- Estimator APIs to train and evaluate distributed PyTorch on Spark.
BigDL users can use either:
- NNFrame APIs to run Spark ML Pipeline and Dataframe with BigDL support, or
- Keras-style APIs for BigDL to build deep learning pipeline.
4.4 Cluster Serving
Analytics Zoo Cluster Serving is a real-time distributed serving solution for deep learning (including TF, PyTorch, Caffe, BigDL and OpenVINO). Follow the Cluster Serving Programming Guide to run the Cluster Serving; the Cluster Serving API Guide explains the APIs in more detail.
Analytics Zoo provides scalable AutoML support for time series prediction (including automatic feature generation, model selection and hyper-parameter tuning). Check the AutoML Overview for a high level description of the AutoML framework. Please check out the details in the Programming Guide and API Guide.