# autogluon-assistant **Repository Path**: knifecms/autogluon-assistant ## Basic Information - **Project Name**: autogluon-assistant - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-05-11 - **Last Updated**: 2025-05-11 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
# 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/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.

aga_demo

## 💾 Installation AutoGluon Assistant is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows. You can install with: ```bash pip install autogluon.assistant ``` You can also install from source: ```bash 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: ```bash pip install -r requirements.txt ``` ### API Keys #### Configuring LLMs AG-A supports using both AWS Bedrock and OpenAI as LLM model providers. You will need to set up API keys for the respective provider you choose. By default, AG-A uses AWS Bedrock for its language models. #### AWS Bedrock Setup AG-A integrates with AWS Bedrock by default. To use AWS Bedrock, you will need to configure your AWS credentials and region settings: ```bash export AWS_DEFAULT_REGION="" export AWS_ACCESS_KEY_ID="" export AWS_SECRET_ACCESS_KEY="" ``` Ensure you have an active AWS account and appropriate permissions set up for using Bedrock models. You can manage your AWS credentials through the AWS Management Console. See [Bedrock supported AWS regions](https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-regions.html) #### OpenAI Setup To use OpenAI, you'll need to set your OpenAI API key as an environment variable: ```bash export OPENAI_API_KEY="sk-..." ``` You can sign up for an OpenAI account [here](https://platform.openai.com/) and manage your API keys [here](https://platform.openai.com/account/api-keys). Important: Free-tier OpenAI accounts may be subject to rate limits, which could affect AG-A's performance. We recommend using a paid OpenAI API key for seamless functionality. #### Azure OpenAI Setup To use Azure OpenAI, you'll need to set the following Azure OpenAI values, as environment variables: ```bash export AZURE_OPENAI_API_KEY=<...> export OPENAI_API_VERSION=<...> export AZURE_OPENAI_ENDPOINT=<...> ``` ## Usage We support two ways of using AutoGluon Assistant: WebUI and CLI. ### Web UI AutoGluon Assistant Web UI allows users to leverage the capabilities of AG-A through an intuitive web interface. The web UI enables users to upload datasets, configure AG-A runs with customized settings, preview data, monitor execution progress, view and download results, and supports secure, isolated sessions for concurrent users. #### To run the AG-A Web UI: ```bash aga ui # OR # Launch Web-UI on specific port e.g. 8888 aga ui --port 8888 ``` AG-A Web UI should now be accessible in your web browser at `http://localhost:8501` or the specified port. ### CLI Before launching AG-A CLI, prepare your data files in the following structure: ``` └── data # Data files directory ├── train.[ext] # Training dataset (required) ├── test.[ext] # Test dataset (required) └── description.txt # Dataset and task description (recommended) ``` Note: - The training and test files can be in any tabular data format (e.g., csv, parquet, xlsx) - While there are no strict naming requirements, we recommend using clear, descriptive filenames - The description file is optional but recommended for better model selection and optimization. It can include: - Dataset description - Problem context - Evaluation metrics - Any other relevant information Now you can launch the AutoGluon Assistant run using the following command: ```bash aga run [NAME_OF_DATA_DIR] --presets [PRESET_QUALITY] # e.g. aga run ./toy_data --presets best_quality ``` We support three presets, including `medium_quality`, `high_quality` and `best_quality`. We use `best_quality` as a default setting. After the run is complete, model predictions on test dataset are saved into the `aga-output-.csv` file. It will be formatted according to optional `sample_submission.csv` file if provided. #### Overriding Configs You can override specific settings in the YAML configuration defined in the [config folder](https://github.com/autogluon/autogluon-assistant/tree/main/src/autogluon/assistant/configs) using the `config_overrides` parameter with format `"key1=value1, key2.nested=value2"` from the command line. Here are some example commands on using configuration overrides: ```bash aga run toy_data --config_overrides "feature_transformers.enabled_models=None, time_limit=3600" # OR aga run toy_data --config_overrides "feature_transformers.enabled_models=None" --config_overrides "time_limit=3600" ```