At SkyHiGh (IIK's production instance):
- General purpose flavors, all IIK affiliates are eligible:
  - dx5.8c90r.v100-8g: A flavor with 90GB RAM, 8 vCPU's and a 1/4 of a Tesla v100 (8GB GPU-RAM).
  - de3
  .5c25r
  - .
  a100-4g: A flavor with 25GB RAM, 5 vCPUs and 1/10 of a Tesla A100 (4GB GPU-RAM)de3.
  - 12c60r.a100-10g: A flavor with 60GB RAM, 12 vCPUs and 1/4 of a Tesla A100 (10GB GPU-RAM)
    Only
- Flavors only available for SFI NORCICS:
  - de3.24c120r.a100-20g: A flavor with 120GB RAM, 24 vCPUs and 1/2 of a Tesla A100 (20GB GPU-RAM)
  - Only available for SFI NORCICS
  - de3.48c240r.a100-40g: A flavor with 240GB RAM, 48 vCPUS and 1/1 of a Tesla A100 (40GB GPU-RAM)
    Only
- Flavors only available for SFI NORCICSNorwegian Biomtetrics Lab:
  - dx4.24c60r.p40-24g: A flavor with 60GB RAM, 24 vCPUs and 1/1 of a Tesla P40 (24GB GPU-RAM)
  - de2.24c240r.
  de2.24c256r.
  - a100-20g: A flavor with
  248GB
  - 240GB RAM, 24 vCPU's and 1/2 of a Tesla A100 (20GB GPU-RAM)
  - Only available for Norwegian Biometrics Lab
  - de3.24c120r.a100d-20g: A flavor with 120GB RAM, 24 vCPU's and 1/4 of a Tesla A100 80GB (20GB GPU-RAM)
  - Only available for Norwegian Biometrics lab
At SkyLow (IIK's development instance):
- dx4.8c20r.m10-8G: A flavor with 20GB RAM, 8 vCPU's and one core of a Tesla M10 card (8GB GPU-RAM).
At stackit (NTNU IT's production platform):
- gpu.a100.10Gdx4.24c110r.p100: A flavor with 60GB 110GB RAM, 7 24 vCPU's and 1/4 of a Tesla A100 p100 card (10GB 16GB GPU-RAM)

GPU-enabled images

We provide images with pre-installed Nvidia driver and CUDA package. These images contains the word "GRID" in their names and are regular ubuntu/centOS images with the following additions:

There is a script which installs the correct version of the Nvidia GRID driver at boot. After a driver-update on the hypervisor this script will also update the driver in the VM.
The CUDA-tools are pre-installed.

The installation of the drivers requires a restart; så the newly created instance will reboot shortly after creation.

Starting a GPU-instance

To start a GPU-enabled instance you simply create a VM as you usually would, but make sure to select:

a GPU-enabled flavor
a GRID-enabled image

After creating the VM it will boot, install the GRID-driver, and then reboot. So, do not be supprised if you suddenly loose access to your freshly booted machine. It will come back!

Warning
It is vital that the VM uses the same driver as the hypervisor. If you update this driver your vGPU will cease to function. When the driver is updated on the hypervisor a new driver will also be installed in your VM.

Warning
Do not install nvtop. Its dependencies will break the driver.

Note
CUDA-versions are tightly coupled with the driver version. The CUDA-version bundled with the image is proved working. You can therfore not expect that a newer CUDA-version would work.

cuDNN

Many of our GPU users will probably need Nvidia's cuDNN library. This is not pre-installed in our images, because Nvidia requires all users to register for the Nvidia Developer Program before dowloading. So, please follow the instructions here, to install it on your VM; and use tar file options. DO NOT USE THE DEB OR RPM ALTERNATIVE. Be sure to download the cuDNN version that corresponds to our current CUDA version.

Verify the GPU instance

After installation the presence of a GPU can be verified with lspci:

Code Block
ubuntu@gputest:~$ lspci \| grep NVIDIA 00:05.0 VGA compatible controller: NVIDIA Corporation GV100GL [Tesla V100 PCIe 32GB] (rev a1)

You can also verify that a license for the GPU is acquired successfully (yes, we need licences to use our GPUs...):

Code Block

ubuntu@gputest:~$ journalctl -u nvidia-gridd | tail
 ....
Jun 30 08:37:14 gputest nvidia-gridd[1159]: Acquiring license for GRID vGPU Edition.
Jun 30 08:37:14 gputest nvidia-gridd[1159]: Calling load_byte_array(tra)
Jun 30 08:37:17 gputest nvidia-gridd[1159]: License acquired successfully. (Info: http://openstack-nvidia.lisens.ntnu.no:7070/request; Quadro-Virtual-DWS,5.0)

The "nvidia-smi" tool will show you the GPU status

- dx4.48c220r.2p100: A flavor with 220GB RAM, 48 vCPU's and two Tesla p100 cards (2*16GB GPU-RAM)
At stackit (NTNU IT's production platform):
- dx4.28c120r.a100-20g: A flavor with 120GB RAM, 28vCPU's and 1/2 of a Tesla a100 (20GB GPU-RAM)
- dx5s.96c470r.a100d-80g.e3400g: A flavor with 470GB RAM, 96 vCPUs and a Tesla a100d (80 GB GPU-RAM) and 3.4TiB with compute-local flash storage.
  - Only available for an IV-EPT project.

GPU-enabled images

We provide an image with pre-installed Nvidia driver and CUDA package. This image contains the word "GRID" in its name and are a regular Ubuntu Server LTS image with the following additions:

There is a script which installs the correct version of the Nvidia GRID driver at boot. After a driver-update on the hypervisor this script will also update the driver in the VM.
The CUDA-tools are pre-installed.

The installation of the drivers requires a restart; so the newly created instance will reboot shortly after creation.

Starting a GPU-instance

To start a GPU-enabled instance you simply create a VM as you usually would, but make sure to select:

a GPU-enabled flavor
a GRID-enabled image

After creating the VM it will boot, install the GRID-driver, and then reboot. So, do not be supprised if you suddenly loose access to your freshly booted machine. It will come back!

Warning
It is vital that the VM uses the same driver as the hypervisor. If you update this driver your vGPU will cease to function. When the driver is updated on the hypervisor a new driver will also be installed in your VM.

Warning
Do not install nvtop. Its dependencies will break the driver.

Note
CUDA-versions are tightly coupled with the driver version. The CUDA-version bundled with the image is proved working. You can therfore not expect that a newer CUDA-version would work.

cuDNN

Many of our GPU users will probably need Nvidia's cuDNN library. This is not pre-installed in our image, because Nvidia requires all users to register for the Nvidia Developer Program before dowloading. So, please follow the instructions here, to install it on your VM; and use tar file options. DO NOT USE THE DEB OR RPM ALTERNATIVE. Be sure to download the cuDNN version that corresponds to our current CUDA version.

Verify the GPU instance

After installation the presence of a GPU can be verified with lspci:

Code Block
ubuntu@gputest:~$ lspci \| grep NVIDIA 00:05.0 3D controller: NVIDIA Corporation Device 20f1 (rev a1)

You can also verify that a license for the GPU is acquired successfully (yes, we need licences to use our GPUs...):

Code Block

ubuntu@gputest:~$ journalctl -u nvidia-gridd | tail 
Sep 04 08:54:23 jammy-gpu systemd[1]: Starting NVIDIA Grid Daemon...
Sep 04 08:54:24 jammy-gpu nvidia-gridd[724]: Started (724)
Sep 04 08:54:24 jammy-gpu systemd[1]: Started NVIDIA Grid Daemon.
Sep 04 08:54:24 jammy-gpu nvidia-gridd[724]: Configuration parameter ( ServerAddress  ) not set
Sep 04 08:54:24 jammy-gpu nvidia-gridd[724]: vGPU Software package (0)
Sep 04 08:54:24 jammy-gpu nvidia-gridd[724]: Ignore service provider and node-locked licensing
Sep 04 08:54:24 jammy-gpu nvidia-gridd[724]: NLS initialized
Sep 04 08:54:24 jammy-gpu nvidia-gridd[724]: Acquiring license. (Info: nvidiadls02.it.ntnu.no; NVIDIA Virtual Compute Server)
Sep 04 08:54:27 jammy-gpu nvidia-gridd[724]: License acquired successfully. (Info: nvidiadls02.it.ntnu.no, NVIDIA Virtual Compute Server; Expiry: 2023-9-5 8:54:16 GMT)

The "nvidia-smi" tool will show you the GPU status

Code Block

ubuntu@gputest:~$ nvidia-smi 
Mon Sep  4 08:58:11 2023       
+-----------

Code Block

ubuntu@gputest:~$ nvidia-smi
Tue Jun 30 08:57:13 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.130      Driver Version: 418.130      CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  NameNVIDIA-SMI 525.125.06   Driver Version: 525.125.06    Persistence-M| Bus-IdCUDA Version: 12.0        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GRID V100D-8Q       On   | 00000000:00:05.0 Off ||
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GRID V100D-8C       On   | 00000000:00:05.0 Off |                  N/A |
| N/A   N/A    P0    N/A /  N/A |      0MiB /  8192MiB |      0%      Default |
|                            N/A |
| N/A |  N/A    P0    N/A /  N/A |    528MiB /  8192MiB |       0%      DefaultDisabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+----------------------------------------------------------------------------------+
| Processes:---------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
||        ID   ID        GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Starting from CUDA 12.x, the samples are no longer included in the install packages. If you want to verify that the GPU/license works, you have to download them from github:

Code Block
$ git clone https://github.com/NVIDIA/cuda-samples.git

And the you can compile and run a sampleThere are some CUDA-tools installed in /root of your VM that can be used to test that the GPU works. For instance you could do like so:

Code Block

ubuntu@gputestubuntu@jammy-gpu:~$ sudo su -
root@gputest:~# cd NVIDIA_CUDA-10.1_cuda-samples/Samples/1_Utilities/deviceQuery
root@gputestubuntu@jammy-gpu:~/NVIDIA_CUDA-10.1_cuda-samples/Samples/1_Utilities/deviceQuery#deviceQuery$ make
  ... lots-of-text-from-make ... 
root@gputestubuntu@jammy-gpu:~/NVIDIA_CUDA-10.1_cuda-samples/Samples/1_Utilities/deviceQuery#deviceQuery$ ./deviceQuery 
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GRID V100D-8Q8C"
  CUDA Driver Version / Runtime Version          1012.10 / 1012.10
  CUDA Capability Major/Minor version number:    7.0
  Total amount of global memory:                 8192 MBytes (8589934592 bytes)
  (80080) Multiprocessors, ( 64064) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1380 MHz (1.38 GHz)
  Memory Clock rate:                             877 Mhz
  Memory Bus Width:                              4096-bit
  L2 Cache Size:                                 6291456 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       6553649152 bytes
  Total amount of shared memory per blockmultiprocessor:        4915298304 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 7 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                No
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 0 / 5
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 1012.10, CUDA Runtime Version = 1012.10, NumDevs = 1
Result = PASS

...

Code Block

# Enable the repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
   && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Install the package
sudo apt update && sudo apt -y install nvidia-docker2

# Restart the docker daemon
sudo systemctl restart docker

# Run a test to verifiy that it works
sudo docker run --rm --gpus all nvidia/cuda:1112.0.1-base-ubuntu22.04 nvidia-smi

# Optionally run a test with Tensorflow that actually runs a bit of code on the GPU via docker
sudo docker run --gpus all -it --rm tensorflow/tensorflow:latest2.14.0-gpu \
   python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

...

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Old Version 15

New Version Current

Key

GPU-enabled images

Starting a GPU-instance

cuDNN

Verify the GPU instance

GPU-enabled images

Starting a GPU-instance

cuDNN

Verify the GPU instance