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Merge pull request #19 from HMUNACHI/dev
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Dev
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HMUNACHI committed Mar 12, 2024
2 parents 8aecd88 + 04b16d4 commit 93da34f
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51 changes: 51 additions & 0 deletions .github/workflows/weekly_release.yml
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name: Weekly Release

on:
schedule:
- cron: '0 0 * * 1' # Runs every Sunday at 00:00 UTC

jobs:

deploy:
runs-on: ubuntu-latest

steps:
- uses: actions/checkout@v2
with:
fetch-depth: 0 # Fetch all commits to get accurate version number

- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: '3.x'

- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install setuptools wheel twine bumpversion
- name: Update version number
run: |
# Update the version number in setup.py and __init__.py
bumpversion --current-version $(python setup.py --version) --new-version $(bumpversion --list minor --new-minor-version --no-commit | grep new_minor_version | sed 's/new_minor_version=//') minor setup.py nanodl/__init__.py
# Configure Git user
git config --local user.email "[email protected]"
git config --local user.name "GitHub Action"
# Commit the version changes
git add setup.py nanodl/__init__.py
git commit -m "Bump version"
- name: Build and publish
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
run: |
python setup.py sdist bdist_wheel
twine upload dist/*
- name: Git tag
run: |
git tag v$(python setup.py --version)
git push origin v$(python setup.py --version)
8 changes: 2 additions & 6 deletions README.md
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Expand Up @@ -29,16 +29,12 @@ Developing and training transformer-based models is typically resource-intensive
- True random number generators in Jax which do not need the verbose code.
- A range of advanced algorithms for NLP and computer vision tasks, such as Gaussian Blur, BLEU etc.
- Each model is contained in a single file with no external dependencies, so the source code can also be easily used.
- True random number generators in Jax which do not need the verbose code (examples shown in next sections).

Feedback on any of our discussion, issue and pull request threads are welcomed! Please report any feature requests, issues, questions or concerns in the [discussion forum](https://github.com/hmunachi/nanodl/discussions), or just let us know what you're working on! In case you want to reach out directly, we're at [email protected].

## What's New in version 1.2.1.dev1

- Google's Gemma architecture.
- Reward model wrapper and data-parallel distributed reward trainer.
- True random number generators in Jax which do not need the verbose code (examples shown in next sections).

There are experimental features (like MAMBA architecture and RLHF) in the repo which is not available via the package, pending tests.
There are experimental features (like MAMBA architecture and RLHF) in the repo which are not available via the package, pending tests.

## Quick install

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2 changes: 1 addition & 1 deletion setup.py
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setup(
name='nanodl',
version='1.2.0.dev1',
version='1.2.1.dev1',
author='Henry Ndubuaku',
author_email='[email protected]',
description='A Jax-based library for designing and training transformer models from scratch.',
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