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Accelerators are getting faster, but is your data loading keeping up? In this video, we explore the Grain Dataset API, a powerful Python library designed to optimize data processing for machine learning. Learn how to build efficient, deterministic data pipelines that ensure your accelerators aren’t left waiting.
Dive into the chaining syntax for transformations—including mapping, shuffling, filtering, and batching. You’ll also discover how to preserve random access for easy debugging and how to implement robust, asynchronous checkpointing with Orbax to save your data loading state alongside your model.
Resources:
Grain GitHub Repository→https://goo.gle/4qq8ccM
Grain Documentation → https://goo.gle/4jTKUJZ
Orbax documentation → https://goo.gle/4qjhyai
Hear about Grain from the Engineer Lead → https://goo.gle/3NutGqn
Chapters:
0:00 – The Data Loading Bottleneck
0:27 – Recap: Grain & DataLoader
0:58 – The Grain Dataset API Overview
1:44 – Supported Data Sources (ArrayRecord, TFDS, Parquet)
2:02 – Transformation Pipeline: Shuffle, Map, Filter, Batch
2:33 – Code Example: Filtering News Headlines
3:12 – Checkpointing with get_state and set_state
3:56 – Asynchronous Checkpointing with Orbax
5:01 – Next Steps & Keras Hub
Subscribe to Google for Developers → https://goo.gle/developers
Speaker: Yufeng Guo,
Products Mentioned: Keras, Gemma, JAX Read More Google for Developers
