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MLE agent

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0.4.2

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MLE-Agent: Your intelligent companion for seamless AI engineering and research.

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Overview

MLE-Agent is designed as a pairing LLM agent for machine learning engineers and researchers. It is featured by:

? Autonomous Baseline: Automatically builds ML/AI baselines and solutions based on your requirements.
?End-to-end ML Task: Participates in Kaggle competitions and completes tasks independently.
? Arxiv and Papers with Code Integration: Access best practices and state-of-the-art methods.
? Smart Debugging: Ensures high-quality code through automatic debugger-coder interactions.
File System Integration: Organizes your project structure efficiently.
? Comprehensive Tools Integration: Includes AI/ML functions and MLOps tools for a seamless workflow.
☕ Interactive CLI Chat: Enhances your projects with an easy-to-use chat interface.
? Smart Advisor: Provides personalized suggestions and recommendations for your ML/AI project.
Weekly Report: Automatically generates detailed summaries of your weekly works.

mle_v030.mp4

Milestones

09/24/2024: Release the 0.4.2 with enhanced Auto-Kaggle mode to complete an end-to-end competition with minimal effort.
09/10/2024: Release the 0.4.0 with new CLIs like MLE report, MLE kaggle, MLE integration and many new models like Mistral.
07/25/2024: Release the 0.3.0 with huge refactoring, many integrations, etc. (v0.3.0)
07/11/2024: Release the 0.2.0 with multiple agents interaction (v0.2.0)
?‍? 07/03/2024: Kaia is born
06/01/2024: Release the first rule-based version of MLE agent (v0.1.0)

Get started

Installation

pip install mle-agent -U
# or from source
git clone [email protected]:MLSysOps/MLE-agent.git
pip install -e .

Usage

mle new <project name>

And a project directory will be created under the current path, you need to start the project under the project directory.

cd <project name>
mle start

You can also start an interactive chat in the terminal under the project directory:

mle chat

Use cases

? Prototype an ML Baseline

MLE agent can help you prototype an ML baseline with the given requirements, and test the model on the local machine. The requirements can be vague, such as "I want to predict the stock price based on the historical data".

cd <project name>
mle start

Generate Work Report

MLE agent can help you summarize your weekly report, including development progress, communication notes, reference, and to-do lists.

Mode 1: Web Application to Generate Report from GitHub

cd <project name>
mle report

Then, you can visit http://localhost:3000/ to generate your report locally.

Mode 2: CLI Tool to Generate Report from Local Git Repository

cd <project name>
mle report-local --email=<git email> --start-date=YYYY-MM-DD --end-date=YYYY-MM-DD <path_to_git_repo>

--start-date and --end-date are optional parameters. If omitted, the command will generate a report for the default date range of the last 7 days.
Replace <git email> with your Git email and <path_to_git_repo> with the path to your local Git repository.

? Start with Kaggle Competition

MLE agent can participate in Kaggle competitions and finish coding and debugging from data preparation to model training independently. Here is the basic command to start a Kaggle competition:

cd <project name>
mle kaggle

Or you can let the agents finish the Kaggle task without human interaction if you have the dataset and submission file ready:

cd <project name>
mle kaggle --auto 
--datasets "<path_to_dataset1>,<path_to_dataset2>,..." 
--description "<description_file_path_or_text>" 
--submission "<submission_file_path>" 
--sub_example "<submission_example_file_path>"  
--comp_id "<competition_id>"