Computing resource¶
In the final phase, a large-scale cloud computing platform is provided for all qualified teams. Followings are some details on computing resource allocation and usage.
Computing resource allocation¶
Basic computing resource¶
Each team will be assigned a computing cluster with 248 CPU cores, 640GB memory, and 1 TB hard disk storage as basic computing resource.
The basic computing resource will allow each team to run at least 30 simulator instances in parallel.
Bonus resource allocation¶
We will arrange 2 rounds of bonus resource allocation on 06/17 and 06/24 (UTC-12), respectively.
Top 10 teams by 2:00 PM (UTC-12) of the allocation day can apply for bonus computing resources (up to 144 CPU cores, 384GB memory per team).
Between 2:00 PM and 10:00 PM (UTC-12) of the allocation day, we will recycle and re-allocate the bonus computing resources. Participants cannot access the computing resources during this period.
Apply for bonus computing resource¶
To apply for bonus computing resource, the team leader needs to send an application email to citybrainchallenge@gmail.com before 2:00 PM (UTC-12) of the allocation day. Please use the same email address you used for registration.
The email only needs to contain a title: “Computing resource request - team XX (team name)” (Email content is not needed).
We will review all applications and grant bonus resources based on the CPU usage of a team in the previous allocation round and send a reponse by 10:00 PM (UTC-12) of the allocation day.
How to use the computing resource¶
Login¶
The top 20 qualified teams’ leaders will receive the login credentials along with the confirmation emails.
The login credential includes IP address (we will add up to 5 IP addresses to the whitelist for each team), user name, and password, which you can use to login to the assigned computing cluster (A ubuntu system is pre-installed).
Model development¶
Ray library and RLlib library are the default packages to support distributed model training. Sample codes of training models using RLlib are provided in
rllib_train.py
.Participants can also use their own preferred distributed computing packages
Result and submission¶
Participants need to download their training log, results and models to their local storage. It is the responsibility of the participants to ensure the security of the data.
Participants still need to submit their model via the official website to get their leaderboard scores and official ranking.