google/trax

★ 8,305PythonAudience · researcherComplexity · 4/5Setup · moderate

Mindmap

mindmap
  root((trax))
    Model types
      Transformer
      LSTM
      ResNet
      Reformer
    Tasks
      Language translation
      Image recognition
      Reinforcement learning
    Hardware
      CPU
      GPU
      TPU
    Backends
      JAX
      TensorFlow
    Getting started
      Colab notebook
      Command-line tool

mindmap root((trax)) Model types Transformer LSTM ResNet Reformer Tasks Language translation Image recognition Reinforcement learning Hardware CPU GPU TPU Backends JAX TensorFlow Getting started Colab notebook Command-line tool

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Things people build with this

USE CASE 1

Train a Transformer model for text translation using Trax's built-in implementation without writing the architecture from scratch.

USE CASE 2

Run a reinforcement learning experiment using Trax's RL algorithms to train an agent through trial and error.

USE CASE 3

Use the memory-efficient Reformer model to process long documents on a GPU without running out of memory.

USE CASE 4

Run the introductory walkthrough in a Colab notebook to learn deep learning concepts without any local setup.

Tech stack

PythonJAXTensorFlowNumPy

Getting it running

Difficulty · moderate Time to first run · 30min

Best run on GPU or TPU, a Colab notebook is available for a zero-setup introduction.

In plain English

Trax is a deep learning library built by the Google Brain team. Deep learning is a category of machine learning where models learn patterns from large amounts of data by passing that data through many layers of mathematical transformations. Trax aims to make these models easier to understand by keeping the code simple and readable, while still running fast on specialized hardware like graphics cards and Google's custom AI chips called TPUs. The library includes implementations of well-known model types such as the Transformer (widely used for language tasks like translation and text generation), LSTM (a type of model suited to sequences), and ResNet (a model architecture commonly used for image recognition). It also includes implementations of reinforcement learning algorithms, which are methods for training models to make decisions by trial and error. Research models developed at Google, such as the Reformer (a memory-efficient variant of the Transformer), are also included. Trax runs on CPUs, GPUs, and TPUs without requiring code changes. Under the hood, it uses either JAX or TensorFlow as its computing backend. JAX is a library that speeds up numerical operations and automatically computes gradients, which are the mathematical quantities needed to train a model. The library connects to many standard datasets and integrates with the TensorFlow dataset catalog. You can use Trax as a Python library in scripts or notebooks, or call it as a command-line tool, which is useful when training large models on remote machines. The repository includes example notebooks covering translation, named entity recognition, and n-gram language modeling, and there is a Colab notebook that runs the full introductory walkthrough without requiring any local setup. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

Show me how to build and train a Transformer model for English-to-French translation using the Trax library.

Prompt 2

Using Trax, write a training loop for an LSTM model on a sequence prediction task, then evaluate it on a validation set.

Prompt 3

How do I switch Trax's computing backend between JAX and TensorFlow, and what are the trade-offs for GPU training?

Prompt 4

Give me a minimal Trax example that loads a dataset from TensorFlow Datasets and trains a simple model for named entity recognition.

Prompt 5

Walk me through running the Trax Colab notebook for the first time, what cells to run and what the expected output looks like.

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