23 SLE Micro上のNVIDIA GPU #

transactional-update shell

zypper ar https://download.nvidia.com/suse/sle15sp5/ nvidia-sle15sp5-main
zypper --gpg-auto-import-keys refresh

zypper install -y --auto-agree-with-licenses nvidia-open-driver-G06-signed-kmp nvidia-compute-utils-G06

sed -i '/NVreg_OpenRmEnableUnsupportedGpus/s/^#//g' /etc/modprobe.d/50-nvidia-default.conf

exit

reboot

nvidia-smi

Wed Feb 28 12:31:06 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.06              Driver Version: 545.29.06    CUDA Version: 12.3     |
|-----------------------------------------+----------------------+----------------------+
| 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  NVIDIA A100-PCIE-40GB          Off | 00000000:17:00.0 Off |                    0 |
| N/A   29C    P0              35W / 250W |      4MiB / 40960MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA A100-PCIE-40GB          Off | 00000000:CA:00.0 Off |                    0 |
| N/A   30C    P0              33W / 250W |      4MiB / 40960MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

transactional-update shell

zypper ar https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
zypper --gpg-auto-import-keys install -y nvidia-container-toolkit

exit

reboot

nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml

podman run --rm --device nvidia.com/gpu=all --security-opt=label=disable -it registry.suse.com/bci/bci-base:latest bash

zypper ar http://developer.download.nvidia.com/compute/cuda/repos/sles15/x86_64/ cuda-sle15-sp5
zypper in -y cuda-libraries-devel-12-3 cuda-minimal-build-12-3 cuda-demo-suite-12-3

/usr/local/cuda-12/extras/demo_suite/deviceQuery

/usr/local/cuda-12/extras/demo_suite/deviceQuery Starting...

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

Detected 2 CUDA Capable device(s)

Device 0: "NVIDIA A100-PCIE-40GB"
  CUDA Driver Version / Runtime Version          12.2 / 12.1
  CUDA Capability Major/Minor version number:    8.0
  Total amount of global memory:                 40339 MBytes (42298834944 bytes)
  (108) Multiprocessors, ( 64) CUDA Cores/MP:     6912 CUDA Cores
  GPU Max Clock rate:                            1410 MHz (1.41 GHz)
  Memory Clock rate:                             1215 Mhz
  Memory Bus Width:                              5120-bit
  L2 Cache Size:                                 41943040 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:       49152 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 3 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:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  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 / 23 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

Device 1: <snip to reduce output for multiple devices>
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
> Peer access from NVIDIA A100-PCIE-40GB (GPU0) -> NVIDIA A100-PCIE-40GB (GPU1) : Yes
> Peer access from NVIDIA A100-PCIE-40GB (GPU1) -> NVIDIA A100-PCIE-40GB (GPU0) : Yes

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.3, CUDA Runtime Version = 12.3, NumDevs = 2, Device0 = NVIDIA A100-PCIE-40GB, Device1 = NVIDIA A100-PCIE-40GB
Result = PASS

exit

kubectl get nodes

NAME       STATUS   ROLES                       AGE   VERSION
node0001   Ready    control-plane,etcd,master   13d   v1.28.9+rke2r1

tail -n8 /var/lib/rancher/rke2/agent/etc/containerd/config.toml

[plugins."io.containerd.grpc.v1.cri".containerd.runtimes."nvidia"]
  runtime_type = "io.containerd.runc.v2"
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes."nvidia".options]
  BinaryName = "/usr/bin/nvidia-container-runtime"

kubectl apply -f - <<EOF
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: nvidia
handler: nvidia
EOF

helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
helm repo update

helm upgrade -i nvdp nvdp/nvidia-device-plugin --namespace nvidia-device-plugin --create-namespace --version 0.14.5 --set runtimeClassName=nvidia

kubectl get pods -n nvidia-device-plugin
NAME                              READY   STATUS    RESTARTS      AGE
nvdp-nvidia-device-plugin-jp697   1/1     Running   2 (12h ago)   6d3h

kubectl get node node0001 -o json | jq .status.capacity
{
  "cpu": "128",
  "ephemeral-storage": "466889732Ki",
  "hugepages-1Gi": "0",
  "hugepages-2Mi": "0",
  "memory": "32545636Ki",
  "nvidia.com/gpu": "1",                      <----
  "pods": "110"
}

kubectl apply -f - <<EOF
apiVersion: v1
kind: Pod
metadata:
  name: nbody-gpu-benchmark
  namespace: default
spec:
  restartPolicy: OnFailure
  runtimeClassName: nvidia
  containers:
  - name: cuda-container
    image: nvcr.io/nvidia/k8s/cuda-sample:nbody
    args: ["nbody", "-gpu", "-benchmark"]
    resources:
      limits:
        nvidia.com/gpu: 1
    env:
    - name: NVIDIA_VISIBLE_DEVICES
      value: all
    - name: NVIDIA_DRIVER_CAPABILITIES
      value: all
EOF

kubectl logs nbody-gpu-benchmark
Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
	-fullscreen       (run n-body simulation in fullscreen mode)
	-fp64             (use double precision floating point values for simulation)
	-hostmem          (stores simulation data in host memory)
	-benchmark        (run benchmark to measure performance)
	-numbodies=<N>    (number of bodies (>= 1) to run in simulation)
	-device=<d>       (where d=0,1,2.... for the CUDA device to use)
	-numdevices=<i>   (where i=(number of CUDA devices > 0) to use for simulation)
	-compare          (compares simulation results running once on the default GPU and once on the CPU)
	-cpu              (run n-body simulation on the CPU)
	-tipsy=<file.bin> (load a tipsy model file for simulation)

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
GPU Device 0: "Turing" with compute capability 7.5

> Compute 7.5 CUDA device: [Tesla T4]
40960 bodies, total time for 10 iterations: 101.677 ms
= 165.005 billion interactions per second
= 3300.103 single-precision GFLOP/s at 20 flops per interaction

transactional-update pkg install nvidia-gl-G06

.
├── base-images
│   └── SLE-Micro.x86_64-5.5.0-Default-SelfInstall-GM2.install.iso
├── eib-config-iso.yaml
├── kubernetes
│   ├── config
│   │   └── server.yaml
│   ├── helm
│   │   └── values
│   │       └── nvidia-device-plugin.yaml
│   └── manifests
│       └── nvidia-runtime-class.yaml
└── rpms
    └── gpg-keys
        └── nvidia-container-toolkit.key

mkdir -p kubernetes/config kubernetes/helm/values kubernetes/manifests

cat << EOF > kubernetes/config/server.yaml
cni: cilium
selinux: true
EOF

cat << EOF > kubernetes/manifests/nvidia-runtime-class.yaml
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: nvidia
handler: nvidia
EOF

cat << EOF > kubernetes/helm/values/nvidia-device-plugin.yaml
runtimeClassName: nvidia
EOF

mkdir -p rpms/gpg-keys
curl -o rpms/gpg-keys/nvidia-container-toolkit.key https://nvidia.github.io/libnvidia-container/gpgkey

podman run --rm --privileged -it -v /path/to/eib-files/:/eib \
registry.suse.com/edge/edge-image-builder:1.0.2 \
build --definition-file eib-config-iso.yaml