End-to-End Deep Learning with Horovod on Apache Spark

Data processing and deep learning are often split into two pipelines, one for ETL processing, the second for model training. Enabling deep learning frameworks to integrate seamlessly with ETL jobs allows for more streamlined production jobs, with faster iteration between feature engineering and model training. The newly introduced Horovod Spark Estimator API enables TensorFlow and PyTorch models to be trained directly on Spark DataFrames, leveraging Horovod’s ability to scale to hundreds of GPUs in parallel