The article provides a comprehensive guide on how to implement synchronous data parallelism in TensorFlow using the tf.distribute API. It explains two key distribution methods: data parallelism and model parallelism, emphasizing the benefits of synchronous data parallelism where multiple model replicas remain in sync after processing each batch. The guide details the setup for single-host, multi-device training and multi-worker distributed training, catering to both small-scale research workflows and larger industry needs, especially for high-resolution image classification tasks using numerous GPUs.
Data parallelism allows a single model to replicate across multiple devices, processing different data batches and merging results to maintain model performance.
This guide emphasizes synchronous data parallelism using tf.distribute API, ensuring replicated models stay in sync for consistent convergence behavior.
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