JAX Data Loading: Optimizing ML Pipelines with Grain Dataset API & Orbax Checkpointing

The Data Loading Bottleneck in ML

• ⚡ Accelerators are becoming faster, making data loading a critical bottleneck if data isn't delivered at an appropriate rate.
• ⚠️ Wasting compute cycles while waiting for training data is a key problem to avoid.

Introducing the Grain Dataset API

• 💡 Grain is a Python library designed for fast data reading and processing for machine learning.
• 🧩 The Dataset API offers a chaining syntax for defining data transformation steps, providing more general processing capabilities.
• 🔑 Key features include data set mixing, greater control over execution order (sharding, shuffling), and preserving random access for debugging.

Data Transformation Pipeline

• ⚙️ Pipelines typically start with a map data set for efficient random access, supporting sources like Parquet, TFDS, and Array Records.
• 🔄 A common order of operations involves mapping and shuffling, followed by filtering, and finally batching.
• ⚠️ To use batching after filtering, the data set must be converted to an iter data set.

Checkpointing with Grain and Orbax

• 📌 Grain's iterators allow for quick and easy checkpointing using get_state and set_state to save and restore the iterator's state.
• 🚀 For more robust saving, Grain is compatible with Orbax, a library dedicated to checkpointing and exporting models.
• 💾 Orbax enables asynchronous checkpointing of the input pipeline alongside the model, saving the state to a file for resuming training on other machines.