This page lists computing resources that may support your course research project. We provide CPU VMs and facilitate access to available campus resources, including GPUs and storage, but we do not provide credits for paid AI services.
If none of the options below meets your team’s needs, consider a CPU-friendly approach, use smaller models or datasets where appropriate, or identify a fallback before relying on GPU access or paid services.
Ultimately, plan your project around resources that your team can access reliably, and be resourceful!
You must comply with the policy above. Violations may result in the immediate revocation of access.
CloudLab provides on-demand
access to computing environments, most notably CPU VMs, for research
and education. We have created a CloudLab project for this course,
ml4sys. Use the
project
invitation link to request access.
When using these resources, you must also comply with the official CloudLab Acceptable Use Policy. Refer to the CloudLab documentation for usage instructions. For technical support, contact the course staff first rather than CloudLab.
The Illinois Campus Cluster is a campus-wide high-performance computing and storage system that supports both batch jobs and interactive sessions. Job wait times are reportedly long overall, so plan accordingly and submit your jobs well in advance!
For this course, we have requested access to the shared Engineering Instructional queue/partition, which provides both CPU and GPU nodes as described below. All students on the course roster should automatically have access to the partition through the course; if not, contact the course staff for help.
This partition eng-instruction consists of five compute
nodes, providing a total of 512 CPU cores, 3.5 TB of RAM, and
6 NVIDIA A10 GPUs.
| Node type | Nodes | CPUs per node | Memory per node | GPUs per node | Interconnect |
|---|---|---|---|---|---|
| CPU | 3 | 2× AMD EPYC 7713 64-core | 1 TB | — | HDR InfiniBand |
| GPU | 2 | 2× Intel Xeon 8358 32-core | 256 GB | 3× NVIDIA A10 | HDR InfiniBand |
/projects/illinois/eng/shared/shared/examples/my-accounts-eng/projects/illinois/eng/shared/shared/examples/sample.sbatcheng-instruction partition using
the account for this course 26fa-cs598fyy-eng. See the
Running Jobs guide
for comprehensive submission instructions.Illinois Computes provide additional computing resources but they are currently intended to support research rather than instructional use. This course will not have an Illinois Computes allocation, but you may obtain access through a research group, subject to that group’s policies.
Illinois Computes Research Notebooks (ICRN) is a no-cost, convenient option for interactive model training, providing web-based access to Jupyter Notebooks, VS Code, and PyTorch. Each notebook receives guaranteed access to one CPU core and shared access to GPU resources. If this suits your project's needs, check whether you can access ICRN.