GPUs in Red Cloud
(This page under development)
|c4.t1.m20||4||1 Nvidia Tesla T4||20 GB|
|c14.g1.m60||14||1 Nvidia Tesla V100||60 GB|
Red Cloud has 20 T4s and 4 V100s available. You can see how many are in use at any given time here. Always check to see if GPU resources are available to start your instance. If you are new to Red Cloud you should review how to read this documentation before launching an instance, especially the section on accounting. GPUs are not oversubscribed. This means when you create a GPU instance with a certain number of GPUs, you are reserving the physical hardware for the duration of the life of your instance unless it is shelved to free the resources. If the resources are not available when you attempt to start an instance - because someone else has reserved them - then you will receive an error that they cannot be created. Therefore, it is good to check availability before starting an instance, and also shelving instances when not in use.
Launching A GPU Instance
When launching an instance, you can use either the base Linux or Windows instances and install your own GPU libraries, or select CUDA source images such as (...). Next, select a GPU-enabled flavor and configure the instance as you would any other instance. Once your instance is launched, you will have access to the GPU within the VM and can install software (e.g., pytorch, tensorflow) that will use the GPU.
For more information on GPU and CUDA computing, see the Cornell Virtual Workshop "Introduction to GPGPU and CUDA Programming: Overview"
- gpu-accelerated-ubuntu-2020-08 (based on Ubuntu 18.04 LTS)
- gpu-accelerated-centos-2020-08 (based on CentOS 7.8)
These images include the following software:
- CUDA 10.1
- Anaconda python with these packages
- Docker-containerized Jupyter Notebook servers, and
- Matlab R2019a.
See Red Cloud GPU Image Usage page for more details and sample code.