Introduction to BigDL 2.x
BigDL 2.x is an open-source project designed to simplify the scaling of data analytics and AI applications from a single device to a distributed cloud environment. This project leverages various specialized libraries, each tailored to different aspects of data processing and machine learning. Below, we delve into these libraries to understand their functionalities and applications.
Overview of Libraries
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IPEX-LLM (formerly BigDL-LLM): Although deprecated in the original project, it was optimized for large language model processing on Intel CPUs and GPUs. The focus has since shifted to the IPEX-LLM project.
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Orca: It facilitates the distribution of big data and AI workloads across Spark and Ray platforms. This library is crucial for those looking to scale TensorFlow and PyTorch programs effortlessly across multiple nodes.
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Nano: Tailored for enhancing the performance of TensorFlow and PyTorch applications on Intel hardware. Nano incorporates modern CPU optimizations to deliver up to a tenfold increase in processing speed.
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DLlib: Acts similarly to Spark's MLlib but focuses on deep learning. It empowers users to build distributed deep learning applications using familiar Spark DataFrames and machine learning pipelines.
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Chronos: Specifically designed for time series analysis, it leverages AutoML to streamline and enhance predictive model construction for temporal data.
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Friesian: This library eases the construction of comprehensive recommendation systems, handling everything from offline feature transformation to real-time model serving.
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PPML: Provides a trusted environment bolstered by Intel's hardware security technologies, like SGX and TDX, for running sensitive data and AI tasks securely across distributed environments.
Installation
For a smooth installation of BigDL, it is recommended to use a Conda environment. The basic installation command through pip is as follows:
conda create -n my_env
conda activate my_env
pip install bigdl
To use specific libraries such as Chronos, individual packages can be installed with:
pip install bigdl-chronos
Getting Started with BigDL Libraries
Orca
Orca shines when there's a need to scale TensorFlow, PyTorch, or OpenVINO applications across clusters. With just a few lines of code, developers can initialize contexts for distributed execution, facilitating seamless data processing and model training. Additionally, RayOnSpark within Orca allows users to run Ray programs inline with Spark code, enhancing the integration of distributed processing capabilities.
Nano
Nano expedites TensorFlow and PyTorch programs with minimal code alterations, using CPU optimizations like SIMD and low precision. This results in significant speed improvements during inference and training, simplifying model acceleration on laptops or servers.
DLlib
DLlib allows Spark users to implement deep learning applications using either Python or Scala with Spark DataFrames. The integration with Spark's native APIs simplifies the transition from traditional machine learning pipelines to deep learning workflows.
Chronos
Chronos simplifies time series forecasting. Its integration with AutoML allows even those with minimal machine learning expertise to develop high-quality predictive models. The library processes time series data efficiently, trains models, and offers robust prediction capabilities.
Friesian
Friesian assists users in building large-scale recommendation systems. It covers the entire spectrum from offline data processing and model training to near-real-time feature updates and online serving, making it an invaluable tool for developers in the recommendation domain.
PPML
PPML ensures secure distributed data analytics via Intel's security technologies, safeguarding sensitive workloads during computation on both private and public cloud infrastructures.
Support and Resources
BigDL's developers encourage users to seek support through mailing lists, user forums, and GitHub issue tracking. There are also comprehensive documentation and user guides available for each library to help users effectively integrate BigDL into their projects.
BigDL 2.x is a testament to seamless scalability and efficiency in AI and big data processing, backed by robust support and tailored libraries for specific data analytics needs. Each library within the BigDL ecosystem serves a unique function, empowering developers to build faster, more secure, and easily scalable applications.