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Using cloud vGPUs: Difference between revisions
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Update to make clear that the CUDA toolkit isn't installed per default.
(g1-18gb-c4-22gb should be used since the CPUs and RAM are proportional to the node. With 18 CPU cores per vGPU, the vgpu1-c18-56gb flavour does not allow all vGPUs on a node to be used.) |
(Update to make clear that the CUDA toolkit isn't installed per default.) |
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This guide describes how to allocate vGPU resources to a virtual machine (VM), installing the necessary drivers and checking whether the vGPU can be used. Repository access as well as access to the vGPUs, is currently only available within [https://arbutus.cloud.computecanada.ca Arbutus Cloud]. | This guide describes how to allocate vGPU resources to a virtual machine (VM), installing the necessary drivers and checking whether the vGPU can be used. Repository access as well as access to the vGPUs, is currently only available within [https://arbutus.cloud.computecanada.ca Arbutus Cloud]. Please note that the documentation below only covers the vGPU driver installation, the CUDA toolkit is not pre-installed. | ||
The [https://developer.nvidia.com/cuda-toolkit-archive CUDA toolkit] can be installed directly from Nvidia or used from [https://docs.computecanada.ca/wiki/Accessing_CVMFS CVMFS software stack]. | |||
== Supported flavors == <!--T:23--> | == Supported flavors == <!--T:23--> | ||
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To use a vGPU within a VM, the instance needs to be deployed on one of the flavors listed below. | To use a vGPU within a VM, the instance needs to be deployed on one of the flavors listed below. The vGPU will be available to the operating system via the PCI bus. While finalizing the setup for more vGPU profiles, the only flavor accessible right now is: | ||
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