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MaxText

As explained in Deep Learning with JAX

A simple, scalable JAX library for training large language models in pure Python/JAX, especially on Google Cloud TPUs.

Need to train a large language model on Google Cloud TPUs without building a heavily configurable framework? MaxText is a simple, performant, scalable LLM implementation written in pure Python and JAX. JAX and the XLA compiler provide optimization while MaxText scales from one host to very large clusters. section 12.1.2

MaxText fits a practical progression from experimentation to production:

  • Run locally while developing. section 12.1.2
  • Use a cluster for ad hoc experimentation. section 12.1.2
  • Spawn long-running production jobs through Google Compute Engine. section 12.1.2
  • Extend the implementation by forking and editing source code rather than primarily changing configuration parameters. section 12.1.2

From local run to production job

A team can prototype locally, test an experimental run on a TPU cluster, then move a long-running training job to GCE. This path preserves a concrete implementation while scaling deployment. section 12.1.2

How Deep Learning with JAX covers it

How deep it goes
Defines a concrete pure-Python/JAX implementation and its scaling model, but leaves detailed architecture and configuration mechanics unspecified.
Examples
Uses local execution, experimental TPU clusters, GCE-managed jobs, and large-cluster scaling as practical deployment examples.
What it assumes
Assumes familiarity with JAX and large-language-model training while positioning TPU execution as the target environment.
What you can do after
Operate scalable JAX LLM training across local environments, TPU clusters, and GCE-managed production jobs.
How it connects
Connects JAX, the XLA compiler, Google Cloud TPUs, Pax, T5X, and large language model training.
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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.