Difference between revisions of "GPUs in Red Cloud"
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− | Red Cloud supports GPU computing | + | [[Red Cloud]] supports GPU computing featuring '''[https://www.nvidia.com/en-us/data-center/tesla-t4/ Nvidia Tesla T4]''' and '''[https://www.nvidia.com/en-us/data-center/tesla-v100/ Nvidia Tesla V100]''' GPUs. To use a GPU, launch an instance with one of the following 2 flavors (instance types): |
{| border="1" cellspacing="0" cellpadding="10" align="center" style="text-align:center;" | {| border="1" cellspacing="0" cellpadding="10" align="center" style="text-align:center;" | ||
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! RAM | ! RAM | ||
|- | |- | ||
− | | | + | | ''c4.t1.m20'' || 4 || 1 '''[https://www.nvidia.com/en-us/data-center/tesla-t4/ Nvidia Tesla T4]''' || 20 GB |
|- | |- | ||
− | | | + | | ''c14.g1.m60'' || 14 || 1 '''[https://www.nvidia.com/en-us/data-center/tesla-v100/ Nvidia Tesla V100]''' || 60 GB |
|} | |} | ||
+ | |||
+ | == Availability == | ||
+ | Red Cloud has 20 T4 GPUs and 4 V100 GPUs. You can see how many are available for use [https://gpus.redcloud.cac.cornell.edu/usage 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 '''''[[OpenStack#Instance_States|shelved]]''''' to free the resources. | ||
+ | |||
+ | '''If you are new to Red Cloud''' please review [[Red_Cloud#How_To_Read_This_Documentation|how to read this documentation]] before launching an instance, especially the section on [[Red_Cloud#Accounting:_Don.27t_Use_Up_Your_Subscription_by_Accident.21|accounting]]. | ||
+ | |||
+ | == Launching A GPU Instance == | ||
+ | When '''[[OpenStack#Launch_an_Instance|launching a GPU instance]]''', you can use the base [[Red_Cloud_Linux_Instances|Linux]] or [[Red_Cloud_Windows_Instances|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 "'''[https://cvw.cac.cornell.edu/gpu/ Introduction to GPGPU and CUDA Programming: Overview]'''" | ||
+ | |||
+ | === GPU images === | ||
+ | |||
+ | * [https://redcloud.cac.cornell.edu/dashboard/ngdetails/OS::Glance::Image/e096c762-473c-440b-9516-19211c255ad2 gpu-accelerated-ubuntu-2020-08] (based on Ubuntu 18.04 LTS) | ||
+ | * [https://redcloud.cac.cornell.edu/dashboard/ngdetails/OS::Glance::Image/516f21bc-07a8-4546-b052-982028a3d04e gpu-accelerated-centos-2020-08] (based on CentOS 7.8) | ||
+ | |||
+ | These images include the following software: | ||
+ | # CUDA 10.1 | ||
+ | # Anaconda Python 3 with these packages | ||
+ | ## TensorFlow | ||
+ | ## PyTorch | ||
+ | ## Keras | ||
+ | # Docker-containerized Jupyter Notebook servers, and | ||
+ | # Matlab R2019a. | ||
+ | |||
+ | See '''[[Red Cloud GPU Image Usage]]''' page for more details and sample code. |
Revision as of 14:27, 5 November 2020
Red Cloud supports GPU computing featuring Nvidia Tesla T4 and Nvidia Tesla V100 GPUs. To use a GPU, launch an instance with one of the following 2 flavors (instance types):
Flavor | CPUs | GPUs | RAM |
---|---|---|---|
c4.t1.m20 | 4 | 1 Nvidia Tesla T4 | 20 GB |
c14.g1.m60 | 14 | 1 Nvidia Tesla V100 | 60 GB |
Availability
Red Cloud has 20 T4 GPUs and 4 V100 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.
If you are new to Red Cloud please review how to read this documentation before launching an instance, especially the section on accounting.
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 images
- 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 3 with these packages
- TensorFlow
- PyTorch
- Keras
- Docker-containerized Jupyter Notebook servers, and
- Matlab R2019a.
See Red Cloud GPU Image Usage page for more details and sample code.