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AI Evaluation & Observability

Evalai

EvalAI is an open-source web platform for hosting AI challenges, evaluating machine-learning and AI algorithms, and comparing submissions through leaderboards. Organizers can define evaluation phases and dataset splits, while participants submit algorithms or agent code for assessment.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Evalai — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use EvalAI when a research or benchmarking challenge needs a shared submission interface and leaderboard, particularly when its evaluation protocol requires custom phases or additional worker compute.

Who it is for

  • Research teams organizing benchmark or AI challenge evaluations.
  • Challenge hosts who need to receive submissions and publish public or private leaderboards.
  • Participants submitting algorithms or Docker-based agent code to challenge evaluations.

Documented uses

  • Create challenge evaluation phases and dataset splits.
  • Accept participant submissions and evaluate them using challenge-defined protocols.
  • Compare results on public or private leaderboards.
  • Use organizer-provided worker clusters when a challenge needs additional compute.
Before you choose

Requirements & limits

  • Challenge organizers can define arbitrary evaluation phases and dataset splits.
  • Leaderboards can be public or private.
  • Remote evaluation can use an organizer's worker-node cluster.
  • Participant agent code can be submitted as Docker images for testing against evaluation environments.
  • The repository documents local setup through Docker Compose; worker services are started separately from the default services.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up EvalAI locally with Docker Compose

Start a local EvalAI instance and open its web interface.

Before you start

  • Docker
  • Docker Compose
  • Git

Steps

  1. Clone the EvalAI repository and change into its directory.

  2. Run docker-compose up --build to start the required services.

  3. To include worker services, use docker-compose --profile worker up --build.

  4. Open http://127.0.0.1:8888 in a browser and use one of the listed default accounts.

Expected outcome

A locally running EvalAI web application, with default superuser, host and participant accounts.

Guide limitations

  • The default startup runs the database, SQS and Django services; worker services require the separate worker profile.
  • This setup guide does not configure a challenge or define an evaluation protocol.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
Cloud-CV/EvalAI · Official homepage title · retained in its source language
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