Using NVIDIA A100 in Red Hat OpenShift: Compatibility Check

Setting up compute nodes with NVIDIA A100 GPUs in a Red Hat OpenShift cluster can be an effective way to leverage GPU resources for accelerated workloads, such as AI/ML, HPC, and other data-intensive tasks. Below is a detailed guide and considerations for integrating NVIDIA A100 GPUs with different memory capacities into an OpenShift cluster:

Red Hat OpenShift and NVIDIA A100 Compatibility

1. Hardware Compatibility:

• NVIDIA A100 GPUs: Both A100 80GB and A100 40GB models are supported by OpenShift, as long as the hardware is configured correctly and the appropriate drivers and CUDA libraries are installed.
• Compute Nodes: Each node in the cluster can have different GPU configurations. Node1 can have 2x A100 80GB, and Node2 can have 2x A100 40GB without issues, provided the node’s hardware supports these GPUs.

2. Software Requirements:

• OpenShift Version: Ensure you are using a version of OpenShift that supports GPU workloads. OpenShift 4.6 and newer versions provide enhanced support for GPUs.
• NVIDIA GPU Operator: This operator simplifies the deployment and management of GPU drivers and related software in OpenShift. It automatically manages GPU resources and installs the necessary components.

Steps to Integrate A100 GPUs in OpenShift

1. Prepare the Nodes:

• Install NVIDIA Drivers: Ensure that the latest NVIDIA drivers supporting A100 GPUs are installed on the nodes. This typically involves installing the CUDA toolkit and NVIDIA container toolkit. # Example commands to install NVIDIA driver and CUDA toolkit on a Red Hat-based system sudo yum install -y kernel-devel-$(uname -r) epel-release sudo yum install -y dkms sudo bash NVIDIA-Linux-x86_64-<version>.run sudo yum install -y cuda
• Install NVIDIA Docker Runtime:
  bash sudo distribution=$(. /etc/os-release;echo $ID$VERSION_ID) sudo curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | sudo tee /etc/yum.repos.d/nvidia-docker.repo sudo yum install -y nvidia-docker2 sudo systemctl restart docker

2. Deploy NVIDIA GPU Operator:

• The NVIDIA GPU Operator will automate the installation and management of all necessary components to use NVIDIA GPUs in OpenShift.
• Install the GPU Operator via the OpenShift web console or CLI.
  bash oc create -f https://github.com/NVIDIA/gpu-operator/releases/download/v<version>/gpu-operator-certified.v<version>.yaml

3. Configure OpenShift for GPU Workloads:

• Ensure the nodes with GPUs are labeled accordingly so that workloads requiring GPUs are scheduled on the correct nodes. # Example to label nodes oc label node <node1> feature.node.kubernetes.io/gpu.present=true oc label node <node2> feature.node.kubernetes.io/gpu.present=true
• Verify that the GPU resources are available and properly recognized by the OpenShift cluster.
  bash oc describe node <node1> | grep nvidia oc describe node <node2> | grep nvidia

4. Create GPU-Enabled Workloads:

• Deploy workloads that require GPUs by specifying resource requests in the pod specification. For example: ```yaml apiVersion: v1 kind: Pod metadata: name: gpu-pod spec: containers:
  • name: gpu-container
image: nvidia/cuda:11.2.0-runtime-ubi8
resources:
limits:
nvidia.com/gpu: 1 # Request 1 GPU
```

Considerations for Mixed GPU Environments

1. Resource Allocation:

• When scheduling pods with GPU requirements, ensure that OpenShift’s scheduler efficiently handles mixed GPU capacities. Pods requiring less GPU memory may be better suited to nodes with A100 40GB, whereas memory-intensive tasks can be directed to nodes with A100 80GB.

2. Workload Placement:

• You can use node selectors and affinity/anti-affinity rules to control where workloads are placed, ensuring optimal use of the available GPU resources.

3. Monitoring and Management:

• Use tools like NVIDIA’s nvidia-smi and monitoring solutions integrated with OpenShift to keep track of GPU utilization and health.
• Regularly update the NVIDIA GPU Operator and drivers to maintain compatibility and performance.

Example: Deploying a GPU-Enabled Application

Suppose you want to deploy a TensorFlow application that leverages GPUs. Your pod specification might look like this:

apiVersion: v1
kind: Pod
metadata:
  name: tensorflow-gpu
spec:
  containers:
  - name: tensorflow-container
    image: tensorflow/tensorflow:latest-gpu
    resources:
      limits:
        nvidia.com/gpu: 1  # Request 1 GPU
    command: ["python", "-c", "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"]

Deploy this pod using:

oc create -f tensorflow-gpu.yaml

This pod will use one of the available NVIDIA GPUs in the cluster.

Conclusion

In conclusion, deploying NVIDIA in Red Hat OpenShift environments on the best GPU dedicated server enables accelerated data processing, machine learning training, and other GPU-intensive tasks. Setting up the compute nodes with NVIDIA A100 GPUs in a Red Hat OpenShift cluster on the best GPU dedicated server is an effective way to leverage GPU resources for accelerated workloads such as AI/ML, HPC, and other data-intensive tasks.