BigDL is a unified distributed AI framework designed to scale data analytics and AI applications from laptops to clusters. It offers specialized libraries for large language models (LLMs), big data AI pipelines, transparent program acceleration, deep learning on Spark, time series analysis, recommendation systems, and hardware-secured AI.

How It Works

BigDL leverages Apache Spark and Ray for distributed execution, enabling users to scale single-node Python or Scala/Java programs. Its core strength lies in providing high-level APIs that abstract away distributed computing complexities, allowing seamless integration with popular deep learning frameworks like TensorFlow, PyTorch, and Keras. The Nano library further enhances performance by transparently applying CPU optimizations.

Quick Start & Requirements

• Installation: pip install bigdl (recommended via conda environment).
• Prerequisites: Python, Spark/Ray (for distributed modes). Specific libraries may have additional requirements.
• Documentation: BigDL Docs

Highlighted Details

• LLM Support: Optimized for Intel CPUs and GPUs, though the LLM library is deprecated in favor of ipex-llm.
• Orca: Scales TensorFlow, PyTorch, and OpenVINO programs on Spark/Ray clusters, supporting distributed data processing and model training.
• Nano: Transparently accelerates TensorFlow and PyTorch programs on CPUs with optimizations like BF16, INT8 quantization, and JIT compilation, offering up to 10x speedup.
• PPML: Provides hardware-secured AI execution using Intel SGX/TDX for enhanced data privacy.

Maintenance & Community

• Development Focus: Future development for LLMs is directed to the ipex-llm project.
• Community: Support via Mail List, User Group, and GitHub Issues.

Licensing & Compatibility

• License: Apache 2.0.
• Compatibility: Generally compatible with commercial and closed-source applications.

Limitations & Caveats

• The bigdl-llm library is deprecated and users should migrate to ipex-llm. Performance optimizations are primarily targeted at Intel hardware.