Learn BIGDL with Real Code Examples

Built on top of Apache Spark’s RDD and DataFrame APIs

Tensor and neural network layers optimized for distributed computation

Supports CPU and GPU acceleration with Intel MKL and CUDA

Integrates with Spark ML pipelines and SQL operations

High-level Keras-style APIs for user-friendly model definition

RDD/DataFrame-based data flow

Tensor-based neural network computations

Layer/Module abstraction for network design

Distributed Optimizer for parallel training

Integration with Spark ML pipelines and SQL

Layered neural network abstraction

Distributed training with data-parallel strategy

Spark-based computation graph

High-level API for usability

Integration with big data ecosystem

Recommendation systems on e-commerce platforms

Real-time fraud detection in finance

Telecom customer churn prediction

Healthcare predictive analytics

Large-scale image and text classification pipelines

Distributed deep learning on big data infrastructure

High performance on CPUs and GPUs

Integration with Spark and Hadoop ecosystems

User-friendly high-level APIs

Interoperability with other deep learning frameworks

Add more cluster nodes for large datasets

Use data-parallel training

Cache RDDs/DataFrames to reduce IO overhead

Optimize batch sizes and layer configurations

Leverage GPUs for compute-intensive layers

Upgrade BigDL via PyPI or Maven

Verify Spark/Hadoop compatibility

Test existing models on new version

Update pipelines for API changes

Validate distributed training performance