BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster

TL;DR

BigDL 2.0 enables seamless scaling of AI pipelines from laptops to large distributed clusters, simplifying deployment and accelerating AI workloads with minimal manual effort.

Contribution

It introduces a unified framework that allows easy transition from local notebooks to distributed AI training, with automatic hardware acceleration and scalability.

Findings

Up to 9.6x speedup on single node

Seamless scaling to hundreds of servers

Adopted by major industry users

Abstract

Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deployment). These usually entail many manual and error-prone steps for the data scientists to fully take advantage of the available hardware resources (e.g., SIMD instructions, multi-processing, quantization, memory allocation optimization, data partitioning, distributed computing, etc.). To address this challenge, we have open sourced BigDL 2.0 at https://github.com/intel-analytics/BigDL/ under Apache 2.0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2.0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9.6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases). BigDL 2.0 has already been adopted by many real-world users (such as Mastercard, Burger King, Inspur, etc.) in production.