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Paxml

As explained in Deep Learning with JAX

A JAX-based framework for training very large models across TPU slices or pods with configurable parallelization and experimentation.

Need to train a model too large for one accelerator? Paxml, also called Pax, is a JAX-based framework for training models across multiple TPU accelerator chip slices or pods. section 12.1.2

Pax emphasizes efficient scaling while exposing advanced, fully configurable experimentation and parallelization. Its focus is large distributed models rather than hiding distributed-training complexity behind a minimal interface. section 12.1.2

Paxml is a fit when you need to:

  • Train models spanning multiple TPU slices or pods. section 12.1.2
  • Experiment with model configurations and parallelization strategies. section 12.1.2
  • Pursue efficient scaling and high model FLOP utilization. section 12.1.2

How Deep Learning with JAX covers it

How deep it goes
Describes the framework’s scale, configuration, and parallelization focus without detailing its implementation internals.
Examples
Uses models spanning multiple TPU accelerator chip slices or pods as its defining scale example.
What you can do after
Operate configurable JAX experiments for training models distributed across very large TPU environments.
How it connects
Connects JAX with distributed training, TPU accelerators, model parallelization, and large language models.
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This explainer was generated by Manning's knowledge graph from the text of Deep Learning with JAX. The section links point to the parts of the book it draws on.