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https://github.com/autogluon/autogluon-assistant

ML Assistant for Competitive Machine Learning
https://github.com/autogluon/autogluon-assistant

automl data-science llms machine-learning

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ML Assistant for Competitive Machine Learning

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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.



## 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"

```