# init2winit

**Repository Path**: mirrors_google/init2winit

## Basic Information

- **Project Name**: init2winit
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-16
- **Last Updated**: 2026-02-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# init2winit

A Jax/Flax codebase for running deterministic, scalable, and well-documented deep learning experiments, with a particular emphasis on neural network initialization, optimization, and tuning experiments.

There is not yet a stable version (nor an official release of this library).
All APIs are subject to change.

This is a research project, not an official Google product.


## Installation
The current development version requires Python 3.6-3.8.

To install the latest development version inside a virtual environment, run

```
python3 -m venv env-i2w
source env-i2w/bin/activate
pip install --upgrade pip
pip install "git+https://github.com/google/init2winit.git#egg=init2winit"
pip install --upgrade jax jaxlib==0.1.66+cuda111 -f https://storage.googleapis.com/jax-releases/jax_releases.html
```

where `cuda111` corresponds to the installed version of CUDA. For more Jax install information see the [Jax README](https://github.com/google/jax#installation).

## Usage

An example MNIST experiment can be run with the following command:

```sh
python3 main.py \
    --experiment_dir=/tmp/test_mnist \
    --model=fully_connected \
    --dataset=mnist \
    --num_train_steps=10
```

For local debugging we recommend using the `fake` dataset:

```sh
python3 main.py \
    --experiment_dir=/tmp/test_fake \
    --num_train_steps=10 \
    --dataset=fake \
    --hparam_overrides='{"input_shape": [28, 28, 1], "output_shape": [10]}'
```

The `hparam_overrides` accepts a serialized JSON object with hyperparameter names/values to use. See the flags in `main.py` for more information on possible configurations.

See the [`dataset_lib`](https://github.com/google/init2winit/tree/master/init2winit/dataset_lib) and [`model_lib`](https://github.com/google/init2winit/tree/master/init2winit/model_lib) directories for currently implemented datasets and models.


## Citing
To cite this repository:

```bibtex
@software{init2winit2021github,
  author = {Justin M. Gilmer and George E. Dahl and Zachary Nado and Priya Kasimbeg and Sourabh Medapati},
  title = {{init2winit}: a JAX codebase for initialization, optimization, and tuning research},
  url = {http://github.com/google/init2winit},
  version = {0.0.2},
  year = {2023},
}
```

For a list of references to the models, datasets, and techniques implemented in this codebase, see [`references.md`](https://github.com/google/init2winit/tree/master/init2winit/references.md).


## Contributors
Contributors (past and present):

- Ankush Garg
- Behrooz Ghorbani
- Cheolmin Kim
- David Cardoze
- George E. Dahl
- Justin M. Gilmer
- Michal Badura
- Priya Kasimbeg
- Rohan Anil
- Sourabh Medapati
- Sneha Kudugunta
- Varun Godbole
- Zachary Nado
- Vlad Feinberg
- Derrick Xin
- Naman Agarwal
- Daniel Suo
- Bilal Khan
- Jeremy Cohen
- Kacper Krasowiak