Learn BIGDL with Real Code Examples

BigDL allows data scientists to run deep learning directly on top of existing big data infrastructures without moving data.

It integrates with Apache Spark and Apache Hadoop ecosystems for scalable training and inference.

BigDL supports high-level deep learning APIs for neural networks, CNNs, RNNs, and optimizations for distributed computing.

Distributed training on CPUs and GPUs

Optimized computation engine leveraging Intel MKL and vectorization

Data-parallel and model-parallel training strategies

Inference at scale on Spark/Hadoop clusters

Built-in metrics, evaluation, and visualization tools

NNModel: defines the neural network architecture

Optimizer: handles model training with specified loss and optimizer

Dataset: RDD or DataFrame-based dataset for distributed training

Module: layers and blocks composing a neural network

Estimator/Pipeline: integrates BigDL with Spark ML pipelines

Scripts/ - Python or Scala model scripts

Datasets/ - large-scale data on HDFS/S3

Models/ - saved BigDL model files

Notebooks/ - exploratory analysis and training

Logs/ - training and evaluation logs

Load large dataset into Spark DataFrame or RDD

Preprocess data using Spark transformations

Define neural network architecture using BigDL layers

Train model using Optimizer with distributed training

Evaluate performance and deploy model for inference on cluster

Beginner: small dataset experiments using local Spark

Intermediate: training distributed neural networks

Advanced: custom layer implementation and optimizations

Expert: integrating with Spark ML pipelines and streaming data

Enterprise: scalable AI pipelines with real-time inference on clusters

BigDL vs TensorFlow: BigDL scales on Spark/Hadoop; TensorFlow more standalone/deep learning focused

BigDL vs PyTorch: PyTorch better for research/experimentation; BigDL integrates with big data pipelines

BigDL vs Spark MLlib: MLlib for classical ML; BigDL for deep learning on Spark

BigDL vs H2O.ai: H2O for general ML; BigDL for distributed deep learning on Spark

BigDL vs Keras: Keras for small to medium datasets; BigDL scales to large clusters

2016 – Initial release by Intel

2017 – Added Keras-style high-level API

2018 – Distributed training optimizations and GPU support

2019 – BigDL 0.9+ integrated with Analytics Zoo

2025 – BigDL 2.x with full Spark 3.x support and modern deep learning layers

BigDL: distributed deep learning library on Spark

RDD: Resilient Distributed Dataset in Spark

DataFrame: structured distributed dataset

Optimizer: training algorithm for neural networks

Module: layer or network block in BigDL