Ml-nodes
The AI HUB provides resources and services for machine learning and deep learning tasks at UiO. This page describes the available resources, how to get access to them, how to use them and how to get support in using them.
Available hardware resources
| Name | Status |
CPUs/ RAM(GiB) |
GPU | Shared home area | OS and software | Comments |
|---|---|---|---|---|---|---|
|
ml1.hpc.uio.no ml2.hpc.uio.no ml3.hpc.uio.no |
Production | 28 cores (Intel Xeon)/128 | 4 X RTX2080Ti | Yes | RHEL 8.3 with module system |
|
|
ml4.hpc.uio.no |
Production | 32 cores (AMD)/128 | 2 X AMD Vega 10 XL/XT | Yes | RHEL 8.3 with module system |
|
|
ml6.hpc.uio.no ml7.hpc.uio.no |
Production | 32 cores (AMD)/128 | 8 X RTX2080Ti | Yes | RHEL 8.3 with module system |
|
| bluemaster01 | Production | 32 cores Power9/512 | 4 x Nvidia V100 | Yes | RHEL 8.3 with module system |
|
How to get access
Send a mail to itf-ai-support@usit.uio.no with your UiO username and a short description of your project or intended use of the resources.
How to load software
Module system
We use the Lmod module system for all AI hub machines. Please refer the modules document for details.
How to use Jupyter
Please see here for using jupyter with GPU support
Home area
The HOME area is shared between ml1, ml2, ml3, ml4, ml6 and ml7. i.e. you will see the same content when you login into any of these machines.
The home area is backed-up each night. To recover files one can access /itf-fi-ml/home/.snapshots/<time stamp>/<username> where your home folder has been backed up.
Using /scratch for large datasets
Since the home area of the ML machines is shared, the performance might not be the fastest when working with large datasets. To accommodate such workflows, each ML node has its own private scratch folder where users can store data temporarily when working on it. The scratch folder is local to each machine so when logging in to different ML machines users will see different content.
To start using the scratch folder simply upload data to /scratch/users/<username> and access it from here. The scratch area is useful if you need to read and/or write a lot of data to files.
There are currently no usage limits on the scratch folders, but we retain the right to remove data that is not in active use when the scratch area of a machine is nearing full. If your workflow requires writing a lot of data to files we recommend you read and write from the scratch area and then move the results to your home area when the experiment is done.