Cluster:Compute nodes

Contents

List of compute nodes

NOTE: Asmodeus and Demogorgon are ordered, but not installed yet, and taints are currently not yet in place.

The following GPU nodes are currently part of the cluster. There are more nodes which act as API servers or provide the Ceph filesystem and web services, but these are not available for standard users.

CCU name	Access	Platform	GPUs	Labels	Taints
Asmodeus	all	Supermicro	4 x A100 HGX 320 GB, subdivided in 16 GPUs @ 20 GB	gpumem=20, gpuarch=nvidia-a100, nvidia-compute-capability-sm80=true
Glasya	trr161	Dual Xeon Rack	4 x Titan RTX @ 24 GB	gpumem=24, gpuarch=nvidia-rtx, nvidia-compute-capability-sm80=true	gpumem=24:NoSchedule
Belial	exc-cb	Supermicro	8 x Quadro RTX 6000 @ 24 GB	gpumem=24, gpuarch=nvidia-rtx, nvidia-compute-capability-sm75=true	gpumem=24:NoSchedule
Fierna	exc-cb	Supermicro	8 x Quadro RTX 6000 @ 24 GB	gpumem=24, gpuarch=nvidia-rtx, nvidia-compute-capability-sm75=true	gpumem=24:NoSchedule
Vecna	exc-cb, inf	nVidia DGX-2	16 x V100 @ 32 GB	gpumem=32, gpuarch=nvidia-v100, nvidia-compute-capability-sm80=true	gpumem=32:NoSchedule
Zariel	trr161	nVidia DGX A100	8 x A100 @ 40 GB	gpumem=40, gpuarch=nvidia-a100, nvidia-compute-capability-sm80=true	gpumem=40:NoSchedule
Tiamat	exc-cb	Supermicro	4 x A100 @ 40 GB	gpumem=40, gpuarch=nvidia-a100, nvidia-compute-capability-sm80=true	gpumem=40:NoSchedule
Demogorgon	exc-cb	Delta	8 x A40 @ 48 GB	gpumem=48, gpuarch=nvidia-a40, nvidia-compute-capability-sm80=true	gpumem=48:NoSchedule

The CCU name is the internal name used in the Kubernetes cluster, as well as the configured hostname of the node. Nodes are not accessible from the outside world, you have to access the cluster via kubectl through the API-server.

In the column "Access" you can find which Kubernetes user groups can access this node.

Group	Desciption
exc-cb	Centre for the Advanced Study of Collective Behaviour
trr161	SFB Transregio 161 "Quantitative Methods for Visual Computing"
inf	Department of Computer Science
cvia	Computer Vision and Image Analysis Group

Targeting a specific node

Targeting a specific node can be done in two different ways, either selecting a node name directly, or requiring certain labels on the node. See table below for node names and associated labels. See the Kubernetes API documentation on how to assign pods to nodes, or refer to the following examples, which are probably self-explaining.

Selecting a node name

Example: GPU-enabled pod which runs only on the node "belial":

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  nodeSelector:
    kubernetes.io/hostname: belial
  containers:
  - name: gpu-container
    image: nvcr.io/nvidia/tensorflow:20.09-tf2-py3
    command: ["sleep", "1d"]
    resources:
      requests:
        cpu: 1
        nvidia.com/gpu: 1
        memory: 10Gi
      limits:
        cpu: 1
        nvidia.com/gpu: 1
        memory: 10Gi
   # more specs (volumes etc.)

Requiring a certain label on the node

Example: GPU-enabled pod which requires compute capability of at least sm-75:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  nodeSelector:
    compute-capability-atleast-sm75: true
    # note: if a node has e.g. the label "compute-capability-sm80", it also has the
    # corresponding "atleast"-label for all lower or equal compute capabilities. Same holds for "gpumem".
  containers:
  - name: gpu-container
    image: nvcr.io/nvidia/tensorflow:20.09-tf2-py3
    command: ["sleep", "1d"]
    resources:
      requests:
        cpu: 1
        nvidia.com/gpu: 1
        memory: 10Gi
      limits:
        cpu: 1
        nvidia.com/gpu: 1
        memory: 10Gi
   # more specs (volumes etc.)

Acquiring GPUs with more than 20 GB

By default, Kubernetes schedules GPU pods only on the smallest class of GPU with 20 GB of memory. The way how this is achieved is that nodes with higher grade GPUs are assigned a "node taint", which makes the node only available to pods which specify that they are "tolerant" against the taint.

So if your tasks for example requires a GPU with *exactly* 32 GB, you have to

1. make the pod tolerate the taint "gpumem=32:NoSchedule" (see table below).
2. make the pod require the node label "gpumem" to be exactly 32.

See the Kubernetes API documentation on taints and tolerations for more details.

Example:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  nodeSelector:
    gpumem=32
  tolerations:
  - key: "gpumem"
    # Note: to be able to run on a GPU with any amount of memory, 
    # replace the operator/value pair by just 'operator: "Exists"'.
    operator: "Equal"
    value: "32"
    effect: "NoSchedule"
  containers:
  - name: gpu-container
    image: nvcr.io/nvidia/tensorflow:20.09-tf2-py3
    command: ["sleep", "1d"]
    resources:
      requests:
        cpu: 1
        nvidia.com/gpu: 1
        memory: 10Gi
      limits:
        cpu: 1
        nvidia.com/gpu: 1
        memory: 10Gi
   # more specs (volumes etc.)

If you need a GPU with *at least* 32 GB, but also would be happy with more, you have to

1. make the pod tolerate the taint "gpumem=32" *and* "gpumem=40".
2. make the pod require the node label "gpumem" to be larger than 31.

Example:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  # the standard node selector is insufficient here.
  # needs to use the more expressive "nodeAffinity".
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: gpumem
            operator: Gt
            value: 31
        # note: this also works to specify a minimum compute capability
        - matchExpressions:
          - key: nvidia-compute-capability-sm
            operator: Gt
            value: 79
  tolerations:
  - key: "gpumem"
    # not sure if this works, maybe you need tolerations for all the
    # different values, each with an "Equal" operator.
    operator: "Gt"
    value: 31
    effect: "NoSchedule"
  # ... rest of the specs like before