GPUs in Red Cloud
|c4.t1.m20||4||1 Nvidia Tesla T4||20 GB|
|c14.g1.m60||14||1 Nvidia Tesla V100||60 GB|
|c16.a1.m55||16||1 Nvidia A100||55 GB|
Red Cloud has 20 T4 GPUs, 4 V100, and 2 A100 GPUs. You can see how many are available for use here. If no GPU is available, you will receive an error when launching a GPU instance.
Red Cloud resources (CPU cores, RAM, GPUs) are not oversubscribed. When you create a GPU instance, you are reserving the physical hardware for the duration of the life of your instance (and your subscription will be charged accordingly) until the instance is deleted or shelved to free the resources.
Launching A GPU Instance
When launching a GPU instance, you can use the base Linux or Windows image and install your own software or libraries that utilizes the GPU. To speed up time to science, CAC also provides 2 Linux GPU images with GPU software installed.
For more information on GPU and CUDA computing, see the Cornell Virtual Workshop "Introduction to GPGPU and CUDA Programming: Overview"
- gpu-accelerated-ubuntu-2022-02 (based on Ubuntu 20.04 LTS)
- gpu-accelerated-rocky-8-2022-02 (based on Rocky Linux 8.5)
- gpu-accelerated-centos-7-2022-02 (based on CentOS 7.9)
These images include the following software:
- CUDA 11.6
- Anaconda Python 3 with these packages
- Docker-containerized Jupyter Notebook servers, and
- Matlab R2021a.
See Red Cloud GPU Image Usage page for more details and sample code.