GPU pods
Info
In this section you will request GPUs. Make sure you don't waste those and delete your pods when not using the GPUs.
Warning
The NRP is in a transition in how some specific high-memory GPUs are requested. The old syntax involved setting node affinity, the new
only requires the gpu type in the container resource requests and limits.
Running GPU pods
Use this definition to create your own pod and deploy it to kubernetes:
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod-example
spec:
containers:
- name: gpu-container
image: tensorflow/tensorflow:latest-gpu
command: ["sleep", "infinity"]
resources:
limits:
nvidia.com/gpu: 1
requests:
nvidia.com/gpu: 1
This example requests 1 GPU device. You can have up to 8 per node if you're using jobs, and up to 2 for pods. If you request GPU devices in your pod, kubernetes will auto schedule your pod to the appropriate node. There's no need to specify the location manually.
You should always delete your pod when your computation is done to let other users use the GPUs.
Consider using Jobs with actual script instead of sleep whenever possible to ensure your pod is not wasting GPU time.
If you have never used Kubernetes before, see the tutorial.
Requesting special GPUs
Certain kinds of GPUs are advertised on nodes as a special resource, f.e. "nvidia.com/rtx-8000". You have to request that resource instead of the "nvidia.com/gpu" one.
The current list is:
| GPU Type | resource |
|---|---|
| A40 | nvidia.com/a40 |
| A100 | nvidia.com/a100 |
| Nvidia RTX A6000 | nvidia.com/rtxa6000 |
| Quadro RTX 8000 | nvidia.com/rtx8000 |
| Grace Hopper GH200 | nvidia.com/gh200 |
| A100 MIG 1g.10gb | nvidia.com/mig-small |
For example, modifying the above example for one of these GPUs, the new yaml would be:
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod-example
spec:
containers:
- name: gpu-container
image: tensorflow/tensorflow:latest-gpu
command: ["sleep", "infinity"]
resources:
limits:
nvidia.com/a100: 1
requests:
nvidia.com/a100: 1
For Grace Hopper node make sure you're also using the image with arm support (nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 is a good one) and tolerating the arm64 architecture:
Requesting many GPUs
Since 1 and 2 GPU jobs are blocking nodes from getting 4 and 8-GPU jobs, there are some nodes reserved for those. Once you submit a job with 4 or 8 GPUs request, a controller will automatically add toleration which will allow you to use the node reserved for more GPUs. You don't need to do anything manually for that.
Choosing GPU type
See requesting high-end GPUs for special types of GPU
We have a variety of GPU flavors attached to Nautilus. This table describes the types of GPUs available for use, but is not up to date - it's better to use the actual cluster information (f.e. kubectl get nodes -L nvidia.com/gpu.product).
Credit: GPU types by NRP Nautilus
If you need more graphical memory, use this table or official specs to choose the type:
| GPU Type | Memory size (GB) |
|---|---|
| NVIDIA-GeForce-GTX-1070 | 8G |
| NVIDIA-GeForce-GTX-1080 | 8G |
| Quadro-M4000 | 8G |
| NVIDIA-A100-PCIE-40GB-MIG-2g.10gb | 10G |
| NVIDIA-GeForce-GTX-1080-Ti | 12G |
| NVIDIA-GeForce-RTX-2080-Ti | 12G |
| NVIDIA-TITAN-Xp | 12G |
| Tesla-T4 | 16G |
| NVIDIA-A10 | 24G |
| NVIDIA-GeForce-RTX-3090 | 24G |
| NVIDIA-GeForce-RTX-4090 | 24G |
| NVIDIA-TITAN-RTX | 24G |
| NVIDIA-RTX-A5000 | 24G |
| Quadro-RTX-6000 | 24G |
| Tesla-V100-SXM2-32GB | 32G |
| NVIDIA-A40 | 48G |
| NVIDIA-L40 | 48G |
| NVIDIA-RTX-A6000 | 48G |
| Quadro-RTX-8000 | 48G |
| NVIDIA-A100-SXM4-80GB | 80G |
NOTE: Not all nodes are available to all users. You can consult about your available resources in Matrix and on resources page. Labs connecting their hardware to our cluster have preferential access to all our resources.
Warning
This is the old syntax below. For higher end GPUs, the use the syntax documented above!
To use a specific type of GPU, add the affinity definition to you pod yaml file. The example below specifies 1080Ti GPU:
spec:
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: nvidia.com/gpu.product
operator: In
values:
- NVIDIA-GeForce-GTX-1080-Ti
To make sure you did everything correctly after you've submited the job, look at the corresponding pod yaml (kubectl get pod ... -o yaml) and check that resulting nodeAffinity is as expected.
Selecting CUDA version
In general the higher CUDA versions support the lower and same driver version. The nodes are labelled with the major and minor CUDA and driver versions. You can check those at the resources page or list with this command (it will also choose only GPU nodes):
kubectl get nodes -L nvidia.com/cuda.driver.major,nvidia.com/cuda.driver.minor,nvidia.com/cuda.runtime.major,nvidia.com/cuda.runtime.minor -l nvidia.com/gpu.product
If you're using the container image with higher CUDA version, you have to pick the nodes supporting it. Example:
spec:
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: nvidia.com/cuda.runtime.major
operator: In
values:
- "12"
- key: nvidia.com/cuda.runtime.minor
operator: In
values:
- "2"
Also you can choose the driver above something if you know which one you need (this will pick drivers above 535):