BigDL DocumentationΒΆ
BigDL makes it easy for data scientists and data engineers to build end-to-end, distributed AI applications. The BigDL 2.0 release combines the original BigDL and Analytics Zoo projects, providing the following features:
DLlib: distributed deep learning library for Apache Spark
Orca: seamlessly scale out TensorFlow and PyTorch pipelines for distributed Big Data
RayOnSpark: run Ray programs directly on Big Data clusters
Chronos: scalable time series analysis using AutoML
PPML: privacy preserving big data analysis and machine learning (experimental)
Nano: automatically accelerate TensorFlow and PyTorch pipelines by applying modern CPU optimizations
Quick Start
User Guide
Nano Overview
DLlib Overview
Orca Overview
Chronos Overview
PPML Overview
- Privacy Preserving Machine Learning (PPML) User Guide
- Trusted Big Data Analytics and ML
- Trusted FL (Federated Learning)
- Building Linux Kernel from Source with SGX Enabled
- Deploy the Intel SGX Device Plugin for Kubernetes
- Trusted Cluster Serving with Graphene on Kubernetes
- TPC-H with Trusted SparkSQL on Kubernetes
- TPC-DS with Trusted SparkSQL on Kubernetes
- Privacy Preserving Machine Learning (PPML) on Azure User Guide
Serving Overview
Common Use Case
Python API
Real-World Application