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‎AUTHORS

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# This is the list of Trax authors for copyright purposes.
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#
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# This does not necessarily list everyone who has contributed code, since in
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# some cases, their employer may be the copyright holder. To see the full list
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# of contributors, see the revision history in source control.
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Google Inc.

‎CONTRIBUTING.md

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# How to Contribute
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# Issues
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* Please tag your issue with `bug`, `feature request`, or `question` to help us
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effectively respond.
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* Please include the versions of JAX or Tensorflow you are running.
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* Please provide the command line you ran as well as the log output.
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# Pull Requests
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We'd love to accept your patches and contributions to this project. There are
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just a few small guidelines you need to follow.
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## Contributor License Agreement
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Contributions to this project must be accompanied by a Contributor License
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Agreement. You (or your employer) retain the copyright to your contribution,
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this simply gives us permission to use and redistribute your contributions as
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part of the project. Head over to <https://cla.developers.google.com/> to see
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your current agreements on file or to sign a new one.
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You generally only need to submit a CLA once, so if you've already submitted one
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(even if it was for a different project), you probably don't need to do it
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again.
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## Code reviews
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All submissions, including submissions by project members, require review. We
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use GitHub pull requests for this purpose. Consult
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[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
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information on using pull requests.
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## Community Guidelines
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This project follows
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[Google's Open Source Community Guidelines](https://opensource.google/conduct/).

‎ISSUE_TEMPLATE.md

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### Description
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...
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### Environment information
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```
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OS: <your answer here>
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$ pip freeze | grep tensor
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# your output here
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$ pip freeze | grep jax
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# your output here
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$ python -V
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# your output here
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```
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### For bugs: reproduction and error logs
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```
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# Steps to reproduce:
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...
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```
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```
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# Error logs:
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...
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```

‎LICENSE

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Apache License
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‎README.md

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## `trax`: Train Neural Nets with JAX
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![train tracks](https://images.pexels.com/photos/461772/pexels-photo-461772.jpeg?dl&fit=crop&crop=entropy&w=640&h=426)
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### `trax`: T2T Radically Simpler with JAX
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*Why?* Because T2T has gotten too complex. We are simplifying the main code too,
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but we wanted to try a more radical step. So you can write code as in pure
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NumPy and debug directly. So you can easily pinpoint each line where things
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happen and understand each function. But we also want it to run fast on
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accelerators, and that's possible with [JAX](https://github.com/google/jax).
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*Status:* preview; things work: models train, checkpoints are saved, TensorBoard
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has summaries, you can decode. But we are changing a lot every day for now.
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Please let us know what we should add, delete, keep, change. We plan to move
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the best parts into core JAX.
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*Entrypoints:*
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* Script: `trainer.py`
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* Main library entrypoint: `trax.train`
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### Examples
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#### Example Colab
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See our example constructing language models from scratch in a GPU-backed colab notebook at
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[Trax Demo](https://colab.research.google.com/github/google/trax/blob/master/trax/notebooks/trax_demo_iclr2019.ipynb)
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#### MLP on MNIST
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```
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python -m trax.trainer \
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--dataset=mnist \
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--model=MLP \
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--config="train.train_steps=1000"
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```
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#### Resnet50 on Imagenet
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```
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python -m trax.trainer \
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--config_file=$PWD/trax/configs/resnet50_imagenet_8gb.gin
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```
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#### TransformerDecoder on LM1B
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```
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python -m trax.trainer \
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--config_file=$PWD/trax/configs/transformer_lm1b_8gb.gin
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```
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### How `trax` differs from T2T
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* Configuration is done with [`gin`](https://github.com/google/gin-config).
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`trainer.py` takes `--config_file` as well as `--config` for file overrides.
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* Models are defined with [`stax`](https://github.com/google/jax/blob/master/jax/experimental/stax.py) in
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`models/`. They are made gin-configurable in `models/__init__.py`.
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* Datasets are simple iterators over batches. Datasets from
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[`tensorflow/datasets`](https://github.com/tensorflow/datasets)
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and [`tensor2tensor`](https://github.com/tensorflow/tensor2tensor)
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are built-in and can be addressed by name.

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