Learn BIGDL with Real Code Examples

Install Apache Spark 3.x or Hadoop 3.x cluster

Add BigDL library JARs to Spark classpath or use PyPI for Python API

Configure Spark parameters for memory, executor cores, and GPU if needed

Launch Spark shell or PySpark with BigDL enabled

Verify installation with sample model training on example dataset

Install Apache Spark 3.x and Hadoop if needed

Install BigDL Python/Scala library

Configure cluster memory, cores, and GPU resources

Test with example dataset and model

Integrate with ML pipelines or streaming jobs

Scripts/ - Python/Scala model scripts

Datasets/ - HDFS or S3 storage paths

Models/ - serialized BigDL models

Logs/ - training and evaluation logs

PipelineConfigs/ - optional pipeline parameters

spark-submit --jars bigdl.jar your_script.py

Use PySpark shell with BigDL enabled

Set Spark executor and driver memory for distributed training

Submit jobs on YARN/Mesos/Kubernetes

Monitor Spark UI for job progress and logs

Supports Unicode datasets

Works globally on standard Spark/Hadoop clusters

Documentation in English

Community contributions from multiple regions

Compliant with enterprise data standards

Works on all major OS supporting Spark/Hadoop

Python/Scala APIs for developers

Free and open-source under Apache 2.0

Designed for enterprise-scale big data AI

Integrates with existing Spark/Hadoop clusters

Jupyter notebooks or Spark notebooks for code execution

Visualization of metrics and model performance

Use Spark UI for monitoring distributed jobs

Integrate charts for evaluation metrics

Export results for reporting

Save trained models for inference

Track experiment parameters and metrics

Version scripts and pipelines

Backup datasets and logs

Maintain reproducibility using cluster configurations

Use Spark RDDs/DataFrames as primary data containers

Preprocess using Spark transformations

Partition datasets for distributed training

Cache data for iterative training

Track feature engineering steps in pipelines