Overview
In collaboration with Dr. Mert Sabuncu from Radiology, the ITS team has established the framework for a new high-performance computing (HPC) cluster dedicated to AI/ML type workflows, like training neural networks for imaging, LLMs and so on.
This cluster features high-memory nodes, Nvidia GPU servers (A100, A40 and L40), InfiniBand interconnect, and specialized storage designed for AI workloads.
Login to the AI cluster
AI cluster is accessible via terminal SSH sessions. You need to be connecting from the WCM network, or have VPN installed and enabled. Replace <cwid> with your credentials.
ssh <cwid>@ai-login01.med.cornell.edu # or ssh <cwid>@ai-login02.med.cornell.edu
Once logged on:
Last login: Fri Jan 3 11:35:53 2025 from 157.000.00.00 <cwid>@ai-login01:~$ <cwid>@ai-login01:~$ pwd /home/<cwid> <cwid>@ai-login01:~$
Storage
AI cluster has the following storage systems configured:
Name | Mount point | Size | Use | Is backed up? | Comment |
|---|---|---|---|---|---|
Home |
| 2Tb | home filesystem. Used to keep small files, configs, codes, scripts, etc | no | have limited space. It is only used for small files |
Midtier |
| varies per lab | each lab has an allocation under intended for data that is actively being used or processed, research datasets | no | |
AI GPFS |
| 700Tb | tbd | no | Parallel file system for data intensive workloads. Limited access, granted on special requests. |
Common File Management
# List all files and directories in the scratch directory ls /midtier/labname/scratch/ # Navigate to a specific subdirectory cd /midtier/labname/scratch/cwid # Copy a file from the current directory to another directory cp data.txt /midtier/labname/scratch/cwid/ # Move the copied file to a different directory mv /midtier/labname/scratch/cwid/data.txt /midtier/labname/scratch/backup/ # Create a new directory mkdir /midtier/labname/scratch/cwid/new_project/
Running jobs
Computational jobs on the AI cluster are managed with a SLURM job manager. We provide an in-depth tutorial on how to use SLURM <placeholder>, but some basic examples that are immediately applicable on the AI cluster will be discussed in this section.
Important notice:
Do not run computations on login nodes
Running your application code directly without submitting it through the scheduler is prohibited. Login nodes are shared resources and they are reserved for light tasks like file management and job submission. Running heavy computations on login nodes can degrade performance for all users. Instead, please submit your compute jobs to the appropriate SLURM queue, which is designed to handle such workloads efficiently.