EvalAI - Platform for Scalable Evaluation and Comparison of AI Algorithms

Introducing EvalAI: Revolutionizing AI and ML Challenge Evaluations

EvalAI is an open-source platform designed to streamline the evaluation and comparison of machine learning (ML) and artificial intelligence (AI) algorithms on a large scale. Recognizing challenges in reliably comparing algorithms due to various implementation factors, EvalAI provides centralized leaderboards and submission interfaces for consistent and accurate analyses. The platform leverages robust backends to accelerate evaluation processes, making it easier for researchers to reproduce and assess results from technical papers.

Key Features

Custom Evaluation Protocols and Phases: EvalAI offers flexible evaluation setups with multiple phases and dataset splits. Users can organize results using both public and private leaderboards and integrate with any programming language to conduct evaluations.
Remote Evaluation: For large-scale challenges requiring substantial computational resources, challenge organizers can integrate their own worker node clusters to handle participant submissions. EvalAI manages challenge hosting, user submissions, and leaderboard maintenance.
Evaluation Inside Environments: Participants can submit their agent code in the form of Docker images, which EvalAI evaluates against test environments. This is facilitated by spinning up containers with the submitted code for comprehensive testing.
Command Line Interface (CLI) Support: The evalai-cli tool extends the functionality of the EvalAI web application to the command line, enhancing user accessibility and convenience.
Portability: Designed for scalability, EvalAI is built on open-source technologies such as Docker, Django, Node.js, and PostgreSQL, ensuring the platform's adaptability and widespread adoption.
Faster Evaluation: EvalAI minimizes evaluation time with initial warm-ups of worker nodes, pre-loading datasets into memory, and splitting datasets for parallel processing across multiple cores.

The Goal

The ultimate goal of EvalAI is to create a centralized space for hosting, participating, and collaborating in global AI challenges. By establishing a consistent baseline for AI performance benchmarking, EvalAI aims to further advancements in the field of artificial intelligence.

Installation Instructions

Setting up EvalAI locally is straightforward, primarily facilitated by Docker:

Install Docker and Docker Compose on your machine.

Clone the EvalAI repository from GitHub:

git clone https://github.com/Cloud-CV/EvalAI.git evalai && cd evalai

Build and run the Docker containers using:
```
docker-compose up --build
```
Access EvalAI via a web browser at http://127.0.0.1:8888. Default users include an admin, host user, and participant, each with the password "password".

For any installation issues, consult the common errors during installation page.

Citing EvalAI

Researchers and developers using EvalAI for their challenge hostings are encouraged to cite the platform as follows:

@article{EvalAI,
    title   =  {EvalAI: Towards Better Evaluation Systems for AI Agents},
    author  =  {Deshraj Yadav and Rishabh Jain and Harsh Agrawal and Prithvijit
                Chattopadhyay and Taranjeet Singh and Akash Jain and Shiv Baran
                Singh and Stefan Lee and Dhruv Batra},
    year    =  {2019},
    volume  =  arXiv:1902.03570
}

Behind the Scenes

EvalAI is actively maintained by a dedicated team, including key contributors such as Rishabh Jain and Gunjan Chhablani, among others. Their efforts ensure the continuous development and improvement of the platform.

Get Involved

For those interested in contributing to EvalAI, the project welcomes new contributors who adhere to its contribution guidelines. This open-source community thrives on collective input and expertise, accelerating meaningful advancements in AI evaluation processes.