[release] 0.1.0 releaes prep (#162)

README.md

@@ -1,31 +1,33 @@
+<div align="left">
+  <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
+</div>
 # AutoGluon Assistant
 
 [![Python Versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)](https://pypi.org/project/autogluon.assistant/)
 [![GitHub license](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENSE)
-[![Continuous Integration](https://github.com/autogluon/autogluon-assistant/actions/workflows/lint.yml/badge.svg)](https://github.com/autogluon/autogluon-assistant/actions/workflows/lint.yml)
+[![Continuous Integration](https://github.com/autogluon/autogluon-assistant/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon-assistant/actions/workflows/continuous_integration.yml)
+
 
 AutoGluon Assistant (AG-A) provides users a simple interface where they can input their data, describe their problem, and receive a highly accurate and competitive ML solution — without writing any code. By leveraging the state-of-the-art AutoML capabilities of [AutoGluon](https://github.com/autogluon/autogluon) and integrating them with a Large Language Model (LLM), AG-A automates the entire data science pipeline. AG-A takes [AutoGluon](https://github.com/autogluon/autogluon)'s automation from three lines of code to zero, enabling users to solve new supervised learning tabular problems using only natural language descriptions.
 
 ## 💾 Installation
 
-Installing from source: 
+AutoGluon Assistant is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.
 
-```bash
-# create a conda env
-conda create -n aga python=3.10
-conda activate aga
+You can install with:
 
-# clone repositories
-git clone https://github.com/autogluon/autogluon-assistant.git
-cd autogluon-assistant && pip install -e "." && cd ..
+```bash
+pip install autogluon.assistant
 ```
 
-or simply:
+You can also install from source:
 
 ```bash
-pip install autogluon.assistant
+git clone https://github.com/autogluon/autogluon-assistant.git
+cd autogluon-assistant && pip install -e "."
 ```
 
+
 #### Beta Features
 
 AG-A now supports automatic feature generation as part of its beta features. To enable these features, please install the beta version dependencies using the following command:

docs/whats_new/v0.1.0.md

@@ -0,0 +1,23 @@
+# Version 0.1.0 🎉
+
+👋 Hello World! The AutoGluon team is happy to announce the release of AutoGluon-Assistant. This is the initial release of the Assistant module and enables users to solve tabular machine learning problems using only natural language descriptions. ✨
+
+🚀 Don't wait to try it out and we are happy to any feedback!
+
+🤖 Currently, AutoGluon-Assistant supports AWS Bedrock as default LLM provider while also supporting OpenAI as an alternative.
+
+📚 To learn more, check out our [tutorial](https://github.com/autogluon/autogluon-assistant/tree/0.1.0/docs/tutorials)
+
+🙌 This release contains [**134** commits from **7** contributors](https://github.com/autogluon/autogluon-assistant/commits/0.1.0).
+
+
+👥 Full Contributor List (ordered by # of commits):
+- @AnirudhDagar
+- @FANGAreNotGnu
+- @boranhan
+- @tonyhoo
+- @canerturkmen
+- @tianyuanzoe
+- @huibinshen
+
+This version supports Python versions 3.8 to 3.11.

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools>=61.0", "wheel"]
 build-backend = "setuptools.build_meta"
 [project]
 name = "autogluon.assistant"
-version = "0.0.1"
+version = "0.1.0"
 description = "ML Assistant for Competitive Machine Learning"
 authors = [
     {name = "AutoGluon Community"}

release_instructions/ReleaseInstructions.md

@@ -88,17 +88,20 @@ Only proceed with official release after successful test release verification.
    * Verify documentation is up-to-date
    * Test installation and core features one final time
 
-2. **Release Branch**
-   * Create a branch with format `0.x.y` (no v prefix) from main
-   * Update version in `pyproject.toml` (remove any dev/rc suffix)
-   * Push the branch
+2. **Prepare Release Notes**
    * Prepare the release notes located in docs/whats_new/v0.x.y.md:
      * This will be copy-pasted into GitHub when you release.
      * Include all merged PRs into the notes and mention all PR authors / contributors (refer to past releases for examples).
      * Prioritize major features before minor features when ordering, otherwise order by merge date.
      * Review with at least 2 core maintainers to ensure release notes are correct.
 
-3. **Create GitHub Release**
+   * Push the release notes to the main branch.
+
+3. **Release Branch**
+   * Create a branch with format `0.x.y` (no v prefix) from main
+   * Push the branch
+
+4. **Create GitHub Release**
    * Tag: `v{version}` (e.g., `v0.1.0`)
    * Title: Same as tag
    * Description: Include:
@@ -112,7 +115,7 @@ Only proceed with official release after successful test release verification.
    * Ensure release notes look correct and make any final formatting fixes.
    * Click 'Publish release' and the release will go live.
 
-4. **PyPI Release**
+5. **PyPI Release**
    * Release will be automatically triggered by GitHub release
    * Monitor the `pypi_release.yml` workflow
    * Verify package appears on PyPI