{"id":1426,"date":"2017-09-18T16:21:42","date_gmt":"2017-09-18T09:21:42","guid":{"rendered":"http:\/\/research.binus.ac.id\/airnd\/?page_id=5"},"modified":"2026-02-23T09:51:51","modified_gmt":"2026-02-23T02:51:51","slug":"resources","status":"publish","type":"page","link":"https:\/\/research.binus.ac.id\/airdc\/resources\/","title":{"rendered":"Resources"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Main Server<\/strong><\/td>\n<\/tr>\n
Model<\/td>\n:<\/td>\nHPE ProLiant DL380 Gen9<\/td>\n<\/tr>\n
Processor<\/td>\n:<\/td>\n\n
2x Intel(R) Xeon(R) CPU E5-2630 v4<\/div>\n

(10 core, 2.20 GHz, 25MB, 85W)<\/td>\n<\/tr>\n

Graphical Processing Unit<\/td>\n:<\/td>\nNVIDIA Tesla P100
\n3584 CUDA cores<\/td>\n<\/tr>\n
Memory<\/td>\n:<\/td>\n212GB<\/td>\n<\/tr>\n
Storage<\/td>\n:<\/td>\nRAID 5 configuration
\n7.37TB 10000rpm SAS<\/td>\n<\/tr>\n
Operating System<\/td>\n:<\/td>\nUbuntu Server 16.04<\/td>\n<\/tr>\n
Frameworks<\/td>\n:<\/td>\n\n
    \n
  • Tensorflow<\/li>\n
  • Keras<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Backup Server<\/strong><\/td>\n<\/tr>\n
Model<\/td>\n:<\/td>\nHPE ProLiant ML350 Gen9<\/td>\n<\/tr>\n
Processor<\/td>\n:<\/td>\n\n
Intel(R) Xeon(R) CPU E5-2620 v4<\/div>\n

(8 core, 2.10 GHz, 20MB, 85W)<\/td>\n<\/tr>\n

Graphical Processing Unit<\/td>\n:<\/td>\nNVIDIA Tesla P4
\n2560 CUDA cores<\/td>\n<\/tr>\n
Memory<\/td>\n:<\/td>\n157GB<\/td>\n<\/tr>\n
Storage<\/td>\n:<\/td>\n25.57TB 7200rpm SAS<\/td>\n<\/tr>\n
Operating System<\/td>\n:<\/td>\nUbuntu Server 16.04<\/td>\n<\/tr>\n
Cloud Computing<\/strong><\/td>\n<\/tr>\n
\"Cloud\"<\/td>\n<\/tr>\n
GPUs<\/strong><\/td>\n<\/tr>\n
NVIDIA Tesla P40 (to be installed in main server)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

HPC Usage Code\u00a0of\u00a0Conduct<\/h3>\n
    \n
  1. Users are required to fill out the server usage request form at the following link:
    \n\ud83d\udd17 https:\/\/s.id\/request-server-AIRDC<\/a><\/li>\n
  2. Each submission is valid for one specific job only<\/strong> (e.g., model training, dataset inference), not an entire project<\/strong>.<\/li>\n
  3. Scheduling is conducted on business days<\/strong> by the AIRDC team, considering factors such as queue length<\/strong>, project category<\/strong>, and job size<\/strong>. The current queue status can be viewed here:
    \n\ud83d\udd17     https:\/\/s.id\/server-queue-AIRDC<\/a><\/li>\n
  4. Users will receive a confirmation email containing SSH access instructions<\/strong> one business day<\/strong> after scheduling is completed.<\/li>\n
  5. The maximum usage duration<\/strong> for each job is 2 days (48 hours)<\/strong>.<\/li>\n
  6. If there is no response within 10 business days<\/strong> after the usage period ends, AIRDC will send a follow-up confirmation email. If the job is still ongoing, users must resubmit the form<\/strong> within 1 day<\/strong> to rejoin the queue.
    Without confirmation, all user data will be deleted<\/strong> from the server.<\/li>\n<\/ol>\n

    Server Specifications:<\/strong><\/h4>\n

    2 \u00d7 Intel\u00ae Xeon\u00ae CPU E5-2630 v4 @ 2.20GHz
    \n1 \u00d7 NVIDIA Tesla P100, CUDA 10.1
    \n212 GB RAM<\/p>\n

    Additional Notes:<\/strong><\/h4>\n
      \n
    1. Server access is supported only via Command Line Interface (CLI)<\/strong> through SSH. Graphical User Interface (GUI) is not supported<\/strong>.<\/li>\n
    2. Users are required to test their code beforehand<\/strong> on a local machine or Google Colab.<\/li>\n
    3. The server environment is Docker-based (containerized)<\/strong>, isolated<\/strong>, and customizable by the user<\/strong>. The maximum storage allocation per user is 150 GB<\/strong>.<\/li>\n<\/ol>\n

      Acknowledgement:<\/strong><\/h4>\n

      It is mandatory to include the attribution \u201cNVIDIA \u2013 BINUS AI R&D Center\u201d<\/strong> in every scientific publication (journal and conference) resulting from the use of this server.<\/p>\n

      Example:<\/strong><\/p>\n

      \"\"<\/p>\n

      Contact:<\/strong><\/h4>\n

      For further inquiries, please contact:<\/p>\n