an open source platform to learn, create, collaborate through challenges

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What is Codalab?

CodaLab Competitions is a powerful open source framework for running competitions that involve result or code submission. You can either participate in an existing competition or host a new competition.

Most competitions hosted on Codalab are machine learning (data science) competitions, but Codalab is NOT limited to this application domain. It can accommodate any problem for which a solution can be provided in the form of a zip archive containing a number of files to be evaluated quantitatively by a scoring program (provided by the organizers). The scoring program must return a numeric score, which is displayed on a leaderboard where the performances of participants are compared.

History of Codalab

Codalab was created in 2013 as a joint venture between Microsoft and Stanford University. Originally the vision was to create an ecosystem for conducting computational research in a more efficient, reproducible, and collaborative manner, combining worksheets and competitions. Worksheets capture complex research pipelines in a reproducible way and create "executable papers".

Some competitions have been organized using worksheets, but the competition platform and the worksheet platform have both a large user base and can be used independently. In 2014, ChaLearn joined to co-develop Codalab competitions. Since 2015, University Paris-Saclay is community lead of Codalab competitions, under the direction of Isabelle Guyon, professor of big data. Codalab is administered by CKCollab and the LRI staff.

Codalab in Research

Codalab in research Codalab is used actively in research. In 2016/2017, 88 new challenges were launched. Recent high profile challenges organized with Codalab include the 2 million Euro prize of the EU, organized by the See.4C consortium, the CIKM AnalytiCup 2017, which attracted 493 participants, MSCOCO (633 participants) and the ChaLearn AutoML challenge 2017 (687 participants).

Since 2016, Codalab offers the possibility of organizing machine learning challenges with code submission. The simplest machine learning challenges require only the submission of results, which are compared to a solution (or key) by a scoring program. Result submission challenges are less computationally expensive than code submission challenges. However, they offer less possibilities. In particular, code submission allows conducting fair benchmarks by executing submitted code in the same condition for all participants.

Codalab has been providing free resources for challenge organizers who want to run high impact events, within a pre-approved agreed upon budget. New since version 1.5: organizers can hook up their own compute workers to the backend of Codalab to redirect the code submissions, enabling growth to big data competitions running at the expense of the organizers. For very special dedicated projects, Codalab can be customized since it is an open source project.

News

AutoDL

October 2018: We are preparing a challenge on Automatic Deep Learning (AutoDL) challenging participants to design code eliminating the need of human expertise to choose the architecture and hyper-parameters of deep neural networks. The challenge is co-organized and sponsored by Google. The protocol will be tested on Codalab in October.

TrackML

September 2018: The LAL and CERN are organizing a challenge to reconstruct particle trajectories in high energy physics detectors. After the success of the first phase with result submission only, a second phase with code submission will be run on Codalab. TrackML is an officially selected challenge of the NIPS 2018 conference.

AutoML3

August 2018: Codalab is proud to host the third challenge on Automatic Machine Learning: Lifelong Machine Learning with drift. AutoML3 is an officially selected challenge of the NIPS 2018 conference.

See.4C

February 2018: 2 million Euro Big Data EU prize powered by Codalab.

DataIA

February 2018: Isabelle Guyon presents Codalab at the newly formed Institute of Convergence DataIA

Student Projects

January 2018: Paris-Saclay master students create challenges for L2 students.

Homework

January 2018: Paris-Saclay instructors create reinforcement learning homework.

10,000 Users

December 2017: Codalab exceeds 10000 users with 480 competitions (145 public)

CiML workshop

December 2017: Codalab presented at the Challenges in Machine Learning workshop [slides].

Version 1.5 is out!

November 2017: Explore the new features: scale up your code submission competition with your own compute workers (full privacy, dockers); organize RL challenges and hook up simulators providing data on demand (with your own "ingestion program"); use the ChaLab wizard to create competitions in minutes.

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