Blog posts related to Dell Technologies solutions for Red Hat OpenShift Container Platform
Wed, 21 Feb 2024 15:08:20 -0000
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This blog describes how to set up OpenShift Virtualization on OpenShift Container Platform clusters using nodes that are equipped with different NVIDIA GPU (Graphics Processing Unit) cards. The tables at the end of this blog show component versions and combinations of GPU workloads that the Dell OpenShift team validated across nodes in OpenShift cluster versions 4.14 and 4.12. NVIDIA and CUDA (Compute Unified Device Architecture) drivers are installed on RHEL8 operating system VMs, and a sample Spark application is created to consume the GPU/vGPU resource.
For a comprehensive overview of NVIDIA vGPU, GPU Operator, and OpenShift Virtualization, as well as the architecture of our validated environment, see OpenShift Virtualization with NVIDIA virtual GPU - Part 1.
Before you start
Steps
oc label node <node-name> --overwrite nvidia.com/gpu.workload.config=vm-vgpu
You can assign the following values to the label: container, vm-passthrough, and vm-vgpu. The GPU operator uses the value of this label when determining which operands to deploy to support the workload type.
2. Annotate the HyperConverged CR to enable mediated devices:
oc annotate --overwrite -n openshift-cnv hco kubevirt-hyperconverged kubevirt.kubevirt.io/jsonpatch='[{"op": "add", "path": "/spec/configuration/developerConfiguration/featureGates/-", "value": "DisableMDEVConfiguration" }]'
3. Build the vGPU manager image:
a. Download the vGPU Software from Software Downloads in the NVIDIA Licensing Portal for the platform, platform version, and vGPU product version you want.
The vGPU software bundle is packaged as NVIDIA-GRID-Linux-KVM-<version>.zip.
b. Extract the bundle to obtain the NVIDIA vGPU Manager for Linux (NVIDIA-Linux-x86_64-<version>-vgpu-kvm.run file) in the Host_Drivers folder.
4. On your administration node, clone the driver container image repository, and change to the vgpu-manager/rhel8 directory:
git clone https://gitlab.com/nvidia/container-images/driver
cd driver/vgpu-manager/rhel8
5. Export the variables with the name of your private registry, where the driver image is pushed into the NVIDIA vGPU manager version, Red Hat CoreOS version (in the format rhcos4.x, where x is the supported minor OCP version), and the CUDA base image version for building the driver image. Build the image using Docker or Podman and push the image to the private registry:
export PRIVATE_REGISTRY=docker.io/indira0408 VERSION=525.125.06 OS_TAG=rhcos4.14 CUDA_VERSION=12.0
docker build --build-arg DRIVER_VERSION=${VERSION} --build-arg CUDA_VERSION=${CUDA_VERSION} -t ${PRIVATE_REGISTRY}/vgpu-manager:${VERSION}-${OS_TAG} .
podman push docker.io/indira0408/vgpu-manager:525.125.06-rhcos4.14
6. Create an imagePullSecret with user credentials for authenticating to the private registry in the nvidia-gpu-operator namespace. Create a clusterPolicy CR with the following custom configuration, and pass the vGPU manager image you created in the previous step in the clusterPolicy:
sandboxWorloads.enabled=true
vgpuManager.enabled=true
vgpuManager.repository=docker.io/indira0408
vgpuManager.image=vgpu-manager
vgpuManager.version=525.125.06
vgpuManager.imagePullSecrets=private-registry-secret
7. After the ClusterPolicy status changes to “Ready,” edit the HyperConverged CR to allow PCI/mediated devices. For examples of HyperConverged CRs for different PCI and mediated devices, see the Dell ISG OpenShift-bare-metal git page.
8. Create a RHEL 8.6 VM and assign the vGPU device. For instructions on how to create a VM on OpenShift, see Creating virtual machines.
9. Optionally, you can change the vGPU profile by labeling the node with a vGPU profile name.
The GPU operator re-creates the vGPU manager drivers. Update the PCI Devices and mediated devices in the HyperConverged CR:
oc label node cnv-vgpu1 nvidia.com/vgpu.config=A40-8Q
Installing NVIDIA drivers on an RHEL 8.6 VM
Note: A vGPU-assigned VM must have the vGPU driver installed. The vGPU software's "Guest_Drivers" folder contains the package and runfile installers for drivers. You can install either the data center driver or the vGPU driver on a VM that has been assigned a single physical GPU through GPU Passthrough mode. Get the data center drivers for the operating system, architecture, and version that you want from NVIDIA Unix Drivers.
sudo subscription-manager register –username <username> --password <password>
2. Install make and compilation tools on the VM:
yum install -y make
yum group install ”Development Tools” -y
3. Disable the Nouveau kernel:
echo ’blacklist nouveau’ | sudo tee -a /etc/modprobe.d/blacklist.conf
4. Reboot the VM to apply the change:
reboot
5. Install Kernel headers:
yum install -y kernel-devel-$(uname -r) kernel-headers-$(uname -r)
The NVIDIA driver requires that the kernel headers and development packages for the running version of the kernel be installed at the time of the driver installation.
6. Install the NVIDIA drivers using the runfile installer. Copy the NVIDIA-Linux-x86_64-525.125.06-grid.run file in Guest drivers folder in the downloaded vGPU software to the VM.
chmod +x NVIDIA-Linux-x86_64-525.125.06-grid.run
sh NVIDIA-Linux-x86_64-525.125.06-grid.run
7. Select the options you require and install the drivers.
8. Run the nvidia-smi command to view the GPU device, NVIDIA, and CUDA drivers.
Installing Spark application on VMs to consume vGPU
Prerequisites
NVIDIA and CUDA drivers are installed on the VM.
Steps
1. Install the Open-JDK package on the VM:
yum install java-1.8.0-openjdk -y
2. Choose the required version of Spark tarball from Downloads | Apache Spark and feed the required URL to wget to get the package:
wget https://dlcdn.apache.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz
3. Unpack the tar file into the /opt directory:
tar -xvf spark-3.5.0-bin-hadoop3.tgz
mv spark-3.5.0-bin-hadoop3 /opt/
mv /opt/spark-3.5.0-bin-hadoop3/ /opt/spark
4. Choose the Spark release you want, and then download the NVIDIA RAPIDS Accelerator for Apache Spark plug-in jar file into the /opt/spark/jars directory from Spark Rapids Download:
wget https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/23.10.0/rapids-4-spark_2.12-23.10.0.jar
5. Export variables for the Spark home and Java home directories inside bash_profile and reload the bash_profile:
export SPARK_HOME=/opt/spark
export PATH=$SPARK_HOME/bin:$PATH
export JAVA_HOME="/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.392.b08-4.el8.x86_64/jre/"
export PATH=$JAVA_HOME/bin:$PATH
source .bash_profile
6. Download the GPU discovery script from the following GitHub link and save the script locally (/root/getGpusResources.sh):
wget https://github.com/apache/spark/blob/master/examples/src/main/scripts/getGpusResources.sh
7. Launch the spark shell with the following configuration settings and run a small compute program to use the vGPU device:
/opt/spark/bin/spark-shell --jars /opt/spark/jars/rapids-4-spark_2.12-23.10.0.jar --conf spark.plugins=com.nvidia.spark.SQLPlugin --conf spark.executor.resource.gpu.discoveryScript=/root/getGpusResources.sh --conf spark.executor.resource.gpu.vendor=nvidia.com --conf spark.rapids.sql.enabled=true --conf spark.executor.resource.gpu.amount=1
scala> val df = sc.makeRDD(1 to 1000000000, 6).toDF
scala> val df2 = sc.makeRDD(1 to 1000000000, 6).toDF
scala> df.select( $"value" as "a").join(df2.select($"value" as "b"), $"a" === $"b").count
8. Run nvidia-smi in the other terminal to monitor vGPU utilization:
watch nvidia-smi
The output shows the Java process and Volatile GPU-utilization percentage.
Validated scenarios and versions
References
Thu, 15 Feb 2024 08:55:16 -0000
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Red Hat OpenShift Virtualization enables users to run virtual machines (VMs) alongside containers on the same platform, simplifying management and reducing the complexity of maintaining separate infrastructures and management tools. OpenShift Virtualization unifies the operations and management of VMs and containers on the same platform, helping organizations to benefit from their existing virtualization investments. The seamless deployment of OpenShift Virtualization makes configuration quick and easy for administrators. An enhanced web console provides a graphical portal to manage these virtualized resources. For more information, see OpenShift Virtualization.
NVIDIA virtual GPU
NVIDIA virtual GPU (vGPU) products leverage NVIDIA GPU capabilities to accelerate compute-intensive workloads, Artificial Intelligence/Machine Learning (AI/ML), data processing, scientific computing, and professional workstations across on-premises, hybrid, and multicloud environments.
NVIDIA vGPU technology enables multiple VMs to access and share the resources of a single physical GPU through virtualization capabilities. You can install the NVIDIA vGPU software in data centers, cloud platforms, and virtual desktop infrastructure (VDI). The vGPU software stack divides the GPU, enabling efficient GPU resource sharing, improved performance for graphics-intensive applications within virtualized environments, and flexibility in allocating GPU resources to different VMs based on workload demands.
NVIDIA vGPU technology on OpenShift accelerates both containerized and VM-based workloads through the use of GPU devices. vGPU creates a mediated device (mdev) that represents a virtual GPU instance. The performance of the physical GPU is divided among these virtual devices and made available on OpenShift Container Platform. Although you can assign multiple vGPU devices to VMs, you can only allocate a vGPU device to one VM at a time.
Common use cases in OpenShift environments include AI/ML model training and inference, data processing, and complex simulations. Scalable and efficient GPU resource utilization can significantly improve performance.
NVIDIA GPU operator for OpenShift
The NVIDIA GPU operator for OpenShift is a Kubernetes operator that automates the deployment and management of the components of GPU-enabled workloads, including device drivers, container runtimes, and monitoring tools. The operator enables OpenShift Virtualization to attach GPUs or virtual GPUs to workloads running on OpenShift Container Platform. Users can easily provision and manage GPU-enabled VMs that run complex AI/ML workloads, and the operator can work in tandem with vGPU technology to streamline the management of GPU resources.
The GPU operator is responsible for configuring every node in the cluster with the required components to support GPU devices in Red Hat OpenShift. It is flexible enough to support heterogenous clusters that may contain multiple GPU device types.
The GPU operator uses the Kubernetes operator framework to automate the deployment and management of all the NVIDIA software components on worker nodes depending on what GPU workload is configured to run on those nodes. These components include the NVIDIA drivers (to enable CUDA), the Kubernetes device plug-in for GPUs, the NVIDIA Container Toolkit, and automatic node labeling using GPU Feature Discovery, DGCM-based monitoring, and more.
Architecture
A CSI-enabled storage provider is configured on the cluster to provision storage for VMs. The PowerStore 5000T standard deployment model provides organizations with all the benefits of a unified storage platform for block, file, and NFS storage, while also enabling flexible growth with the intelligent scale-up and scale-out capability of appliance clusters.
Dell Container Storage Modules (CSMs) enable simple and consistent integration and automation experiences, extending enterprise storage capabilities. Storage modules for Dell PowerStore expose enterprise features of storage arrays to Kubernetes, enabling developers to effortlessly leverage these features in their deployments, making PowerStore an ideal candidate for a VM storage solution. For information about deploying Dell CSI drivers on OpenShift, see Dell CSM.
OpenShift VMs are configured to use a separate network with a VLAN that is different from the MachineNetwork that is configured in the OpenShift cluster. To achieve isolation and security, create a network for VMs on a dedicated network interface on OpenShift nodes using an IP address range that does not overlap with the cluster’s MachineNetwork. The nodes are configured with a second network, and VMs are built on this network using the Kubernetes NMState operator. For more information, see OpenShift Virtualization Networking.
Further, the OpenShift worker nodes are enabled with:
An OpenShift worker node can run either GPU-accelerated container VMs with GPU passthrough, or GPU-accelerated VMs with vGPU, but not a combination. The prerequisites for running containers and VMs with GPUs vary, with the primary difference being the required drivers. During the GPU operator deployment, OpenShift worker nodes are labeled with the details of the detected GPU devices. The labels are used for scheduling pods to be deployed by the GPU operator. The ClusterPolicy custom resource (CR) that is included with the GPU operator installs the required drivers and components as determined by the node labels. For example, the data center driver is needed for containers, the vfio-pci driver is needed for GPU passthrough, and the NVIDIA vGPU Manager is needed for creating vGPU devices. For more information, see NVIDIA GPU Operator with OpenShift Virtualization.
The architecture diagram shows how the GPU operator is configured to deploy different software components on worker nodes depending on what GPU workload is configured to run on those nodes:
For containerized workloads that do not require the capability of an entire GPU, you can configure an OpenShift cluster with Multi Instance GPU (MIG). By allowing the partitioning of a single physical GPU into multiple smaller instances, NVIDIA MIG technology enables each instance to be allocated to different containers, providing isolation and resource allocation for different tasks. MIG is different from vGPU in that the isolation is implemented by the device firmware. Also, MIGuses hardware boundaries, whereas vGPU is a higher-level, software-only approach.
When the driver installation is complete, OpenShift Virtualization automatically creates vGPUs and PCI Host devices based on GPU device configuration information that is provided in the HyperConverged CR. These devices are then assigned to VMs.
For detailed instructions for installing OpenShift Virtualization on the hardware depicted in Figure 1, as well as component versions and GPU workload combinations that have been validated across nodes, see OpenShift Virtualization with NVIDIA virtual GPU - Part 2.
References
Wed, 06 Dec 2023 10:35:35 -0000
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Red Hat® OpenShift® Container Platform is an industry-leading Kubernetes platform that enables a cloud-native development environment together with a cloud operations experience, giving you the ability to choose where you build, deploy, and run applications, all through a consistent interface. Powered by the open source-based OpenShift Kubernetes Engine, Red Hat OpenShift provides cluster management, platform services for managing workloads, application services for building cloud-native applications, and developer services for enhancing developer productivity.
OpenShift Container Platform enables you to host and run Windows-based workloads on Windows compute nodes alongside the traditional Linux workloads that are hosted on Red Hat Enterprise Linux CoreOS (RHCOS) or Red Hat Enterprise Linux compute nodes. For more information, see Red Hat OpenShift support for Windows Containers.
As a prerequisite for installing Widows workloads, the Windows Machine Config Operator must be installed on a cluster that is configured with hybrid networking using OVN-Kubernetes. The operator configures Windows compute nodes and orchestrates the process of deploying and managing Windows workloads on a cluster.
Open Virtual Network (OVN) is the only supported networking configuration for installing Windows compute nodes. OpenShift Container Platform uses the OVN-Kubernetes network plug-in as its default network provider. You can configure the OpenShift Networking OVN-Kubernetes network plug-in to enable Linux and Windows nodes to host Linux and Windows workloads respectively. For more information, see About the OVN-Kubernetes network plugin.
You will need an already installed cluster, built using the IPI installation method or the Assisted Installer. For more information about deploying an OpenShift cluster on Dell bare-metal servers, see the Red Hat OpenShift Container Platform 4.12 on Dell Infrastructure Implementation Guide.
Create a custom manifest file to configure the Hybrid OVN-Kubernetes network during the cluster deployment by running the following commands:
cat cluster-network-03-config.yml
apiVersion: operator.openshift.io/v1
kind: Network
metadata:
name: cluster
spec:
defaultNetwork:
ovnKubernetesConfig:
hybridOverlayConfig:
hybridClusterNetwork:
- cidr: 10.132.0.0/14
hostPrefix: 23
To add the server to the cluster as a worker node, you need bare-metal server with a Windows operating system. For the supported Windows versions, see Red Hat OpenShift 4.13 support for Windows Containers release notes.
The WMCO operator scans for the secret created during boot, and creates another user data secret with the data that is required to interact with the Windows server using the SSH protocol. After the SSH connection is established, the operator starts processing the Windows servers that are listed in the configmap and begins to transfer files and configure the nodes. The CSRs that are generated are auto-approved, and the Windows instance is added to the cluster.
OpenShift Container platform is hosted on Dell PowerEdge R650 servers, enabling hybrid networking with OVN-Kubernetes. The Dell-validated environment consisted of three compute nodes. The validation team added a Windows instance to the cluster as a fourth node. The following table shows the cluster version information:
OpenShift cluster version | 4.13.21 |
Kubernetes version | 1.26.9 |
WCMO operator version | 8.1.0+0.1699557880.p |
Windows instance version | Windows server 2019 (Version 1809) |
Configuring hybrid networking - OVN-Kubernetes network plugin
Wed, 15 Nov 2023 14:20:48 -0000
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Red Hat OpenShift Container Platform is an enterprise-grade Kubernetes platform for deploying and managing secure and hardened Kubernetes clusters at scale. This Kubernetes distribution enables users to easily configure and use GPU resources to accelerate deep learning (DL) and machine learning (ML) workloads.
The NVIDIA H100 Tensor Core GPU, an integral part of the NVIDIA data center platform, is a high-performance GPU that is designed and optimized for AI workloads that are intended for data center and cloud-based applications. The GPU features major advances to accelerate AI, HPC, memory bandwidth, interconnect, and communication at data center scale. For more information, see NVIDIA H100 Tensor Core GPU.
NVIDIA AI Enterprise
NVIDIA AI Enterprise is an end-to-end, secure, cloud-native suite of AI software that enables organizations to solve new challenges while increasing operational efficiency. NVIDIA AI Enterprise accelerates the data science pipeline and streamlines development and deployment of production AI, including generative AI, computer vision, speech AI, and more. For more information, see NVIDIA AI Enterprise.
NVIDIA NGC catalog
The NVIDIA NGC catalog is a curated set of GPU-optimized software for AI, HPC, and Visualization. The NGC catalog simplifies building, customizing, and integrating GPU-optimized software into workflows on a variety of platforms, accelerating the time to solutions for users. The catalog includes containers, pre-trained models, Helm charts for Kubernetes deployments, and industry-specific AI toolkits. These toolkits consist of software development kits (SDKs) for NVDIA AI Enterprise that can be deployed on OpenShift Container Platform.
Prerequisites for installing NVIDIA AI Enterprise on OpenShift Container Platform
NVIDIA license system
The NVIDIA license system is used to provide software licenses to licensed NVIDIA software products. The licenses are available from the NVIDIA Licensing Portal (access requires NVIDIA login credentials). The NVIDIA license system supports the following types of service instances: a Cloud License Service (CLS) instance that is hosted on the NVIDIA Licensing Portal, and a Delegated License Service (DLS) instance that is hosted on-premises at a location that is accessible from your private network, such as inside your data center.
A DLS instance is fully disconnected from the NVIDIA Licensing Portal. Licenses are downloaded from the portall and uploaded manually to the instance. The following figure depicts the flow:
The following DLS software image types are available:
Setting up a DLS instance
1. Download the latest "NLS License Server (DLS) 2.1 for Container Platforms" software from the NVIDIA Licensing Portal.
2. To import DLS appliance and PostgreSQL, run the following commands:
podman load --input dls_appliance_2.1.0.tar.gz
podman load --input dls_pgsql_2.1.0.tar.gz
3. Upload the DLS appliance artifact and the PostgreSQL database artifact images to a private repository.
4. Edit the deployment files for the DLS appliance artifact, and then use the PostgreSQL database artifact to pull these artifacts from the private repository.
You must provide an IP address for DLS_PUBLIC_IP. Optionally, you can edit the DLS default ports in the nls-si-0-deployment.yaml and nls-si-0-service.yaml deployment files. If a registry secret is required to pull the images from the private repository, edit the deployment files for the DLS appliance and the PostgreSQL database to reference the secret.
5. Create a Postgres instance by running the following command:
oc create -f directory/postgres-nls-si-0-deployment.yaml
6. Fetch the IP address of the Postgres pod that you created in the previous step, and then set the DLS_DB_HOST environment variable in the nls-si-0-deployment.yaml file to the IP address of the postgres pod:
oc create -f directory/nls-si-0-deployment.yaml
7. Access the DLS instance at https://<worker-node-ip>:30001. Register the default admin user dls_admin with a new password during the first login.
8. Create a license server on the NVIDIA Licensing Portal, and then add the licenses for the products that you want to allot to this license server.
9. Register the on-premises DLS instance by uploading the DLS token file dls_instance_token_mm-dd-yyyy-hh-mm-ss.tok to the NVIDIA Licensing Portal. Bind the license server that you created in the preceding step to the registered service instance.
10. Download the license file license_mm-dd-yyyy-hh-mm-ss.bin from the license server on the portal and upload it to your on-premises DLS instance. The licenses on the server are made available to the DLS instance.
11. Generate the client configuration token file from the DLS instance. The client configuration token contains information about the service instance, license servers, and fulfillment conditions to be used to serve a license in response to a client request.
12. Copy the client configuration token to clients so that the service instance has the necessary information to serve licenses to clients.
Installing NVIDIA AI Enterprise on OpenShift
1. Install the Node Feature Discovery (NFD) operator.
Install the NFD operator from the embedded Red Hat OperatorHub. After the operator is installed, create an NFD API so that the NFD operator can label the cluster nodes that have GPUs.
2. Install the NVIDIA GPU operator.
Install the NVIDIA GPU operator from the embedded Red Hat OperatorHub. The GPU operator enables Kubernetes cluster engineers to manage GPU nodes just like CPU nodes in the cluster. The operator installs and manages the life cycle of software components so that GPU-accelerated applications can be run on Kubernetes. This operator is installed in the nvidia-gpu-operator namespace by default.
3. Create an NGC secret.
Create an image pull secret object n the nvidia-gpu-operator namespace. This object is for storing the NGC API key to authenticate your access to the NGC container registry. Generate the API key from the NGC catalog.
Use the following credentials for the NGC secret:
Figure 3. NGC secret
4. Create a ConfigMap with configuration data.
Create a configmap in the nvidia-gpu-operator namespace with the client configuration token as data.
kind: ConfigMap
apiVersion: v1
metadata:
name: licensing-config
data:
client_configuration_token.tok: >-
eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJqdGkiOiIwY2QxZ<...>
gridd.conf: '# empty file'
5. Create a Cluster Policy Custom Resource instance.
When you install the NVIDIA GPU operator in OpenShift Container Platform, a custom resource definition for a cluster policy is created. The policy configures the GPU stack that will be deployed, configuring the image names and repository, pod restrictions or credentials, and so on. When creating the cluster policy from the OpenShift web console, make the following customizations:
1. Enter the configmap containing the client configuration token that you created in the NVIDIA GPU/vGPU driver configuration file and enable the NLS.
2. Enable the deployment of the NVIDIA driver through the operator. The image repository is nvcr.io/nvaie.
3. Enter the NGC secret name in the driver configuration.
4. Specify the image name and NVIDIA vGPU driver version in the NVIDIA GPU/vGPU driver configuration section. Get this information from the NGC catalog, as shown in the following figure:
kind: ConfigMap
apiVersion: v1
metadata:
name: licensing-config
data:
client_configuration_token.tok: >-
eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJqdGkiOiIwY2QxZ<...>
gridd.conf: '# empty file'
Figure 4. Configmap with Client configuration token
For a cluster on OpenShift Container Platform version 4.12, the NVIDIA GPU driver image is vgpu-guest-driver-3-1 and the version is 525.105.17. The GPU operator installs all the components that are required to set up the NVIDIA GPUs in the OpenShift cluster.
Validation
Environment overview: The Dell OpenShift validation team used Dell PowerEdge servers hosting Red Hat OpenShift Platform 4.12 to validate the NVIDIA AI Enterprise on OpenShift. The validated environment consisted of three compute nodes hosted on PowerEdge R760, R750 and R7525 servers and equipped respectively with NVIDIA GPU H100, A40, and A100. For more information about deploying an OpenShift cluster on Dell-powered bare metal servers, see the Red Hat OpenShift Container Platform 4.12 on Dell Infrastructure Implementation Guide.
A containerized DLS instance is present on the same OpenShift cluster with all the required licenses.
The team created a TensorFlow pod using the "tensorflow-3-1" image from the nvcr.io/nvaie repository by running the following commands:
apiVersion: v1
kind: Pod
metadata:
name: gpu
spec:
nodeSelector:
nvidia.com/gpu.product: NVIDIA-H100-PCIe
containers:
- image: nvcr.io/nvaie/tensorflow-3-1:23.03-tf1-nvaie-3.1-py3
name: tensorflow
command: ["/bin/sh","-c"]
resources:
limits:
nvidia.com/gpu: 1
requests:
nvidia.com/gpu: 1
restartPolicy: Never
The ResNet-50 convolutional neural network with FP32 and FP16 precision from inside the TensorFlow pod ran successfully.
To run the test, the team used the following commands:
cd /workspace/nvidia-examples/cnn
python resnet.py --layers 50 -b 64 -i 200 -u batch --precision fp16
python resnet.py --layers 50 -b 64 -i 300 -u batch --precision fp32
References
Tue, 10 Oct 2023 09:55:24 -0000
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Red Hat OpenShift Virtualization enables users to run virtual machines (VMs) alongside containers on the same platform, simplifying management and reducing the complexity of maintaining separate infrastructures and management tools. OpenShift Virtualization unifies the operations and management of VMs and containers on the same platform, helping organizations to benefit from their existing investments in virtualization.
The integration of VMs and containers on the same platform reduces the operational overhead and maximizes the hardware usage. The seamless deployment of OpenShift Virtualization makes configuration quick and easy for administrators. An enhanced web console provides a graphical portal to manage these virtualized resources. The feature enables multiple virtualization tasks, including:
OpenShift Virtualization is available as an operator in the OpenShift Operator Hub. The operator is installed from the CLI or the OpenShift web console, The Operator Lifecycle Manager (OLM) deploys operator pods for OpenShift Virtualization components such as compute, storage, networking, scaling, and templating.OLM also deploys the hyperconverged-cluster-operator pod, which is responsible for the deployment, configuration, and life cycle of other components, and the helper pods hco-webhook and hyperconverged-cluster-cli-download. For more information, see OpenShift Virtualization architecture | Virtualization | OpenShift Container Platform 4.12.
This blog describes a Dell-validated environment overview, the advantages of having a dedicated network for the VMs, how to configure the network on the cluster by using the NMState operator, and how to enable virtualization on the Red Hat Container platform.
The Dell OpenShift team used Dell PowerEdge R760 servers to host the Red Hat OpenShift 4.12 Container Platform and to validate OpenShift Virtualization with a dedicated network for VMs. For more information about deploying an OpenShift cluster on Dell powered bare metal servers, see the Red Hat OpenShift Container Platform 4.12 on Dell Infrastructure Implementation Guide.
The OpenShift MachineNetwork uses the 192.168.32.0/24 network. A dedicated VLAN with the IP address range 192.168.4.0/24 is created for the VMs. A dedicated physical interface on OpenShift nodes is configured for the VM network using NMState.
We installed the OpenShift Virtualization operator and created a hyper-converged custom resource on the cluster.
Lastly, we installed CSI PowerStore drivers on the cluster for NFS storage to load the ISOs for the VMs.
OpenShift VMs can use a dedicated network with a VLAN that is different from the one used by the OpenShift cluster. A network for VMs is created on a dedicated network interface on OpenShift nodes, with an IP address range that does not overlap with the cluster’s MachineNetwork.
Configuring a dedicated network for VMs allows for isolation between the VM network and the cluster or external network, helping administrators to manage VMs easily. A dedicated network also helps enhance security and increase performance.
The Kubernetes NMState operator provides a Kubernetes API for performing state-driven network configuration across the OpenShift cluster’s nodes. For more information, see About the Kubernetes NMState Operator - Kubernetes NMState | Networking | OpenShift Container Platform 4.12 .
OpenShift Virtualization uses NMstate to report on and configure the state of the node network, making it possible to modify network policy configuration. For example, you can create a Linux bridge on all nodes by applying a single configuration manifest to the cluster.
You can install the NMState operator from the Operator hub on the OpenShift web console., and then create an NMstate custom resource. NodeNetworkConfigurationPolicy describes the requested network configuration on nodes. Update the node network configuration, including adding and removing interfaces, by applying a NodeNetworkConfigurationPolicy manifest to the cluster.
To atttach a VM to an additional network, we performed the following steps:
After installing the NMState operator on the cluster, we applied the following NodeNetworkConfigurationPolicy to create a Linux bridge that attaches to the second Ethernet:
apiVersion: nmstate.io/v1
kind: NodeNetworkConfigurationPolicy
metadata:
name: br1-eno12409-policy
spec:
nodeSelector:
kubernetes.io/hostname: cnv-21
desiredState:
interfaces:
- name: br1
description: Linux bridge with eno12409 as a port
type: linux-bridge
state: up
ipv4:
address:
- prefix-length: 24
ip: 192.168.4.21
dhcp: false
enabled: true
bridge:
options:
stp:
enabled: false
port:
- name: eno12409
We created a VM by booting Red Hat Enterprise Linux 8.6 ISO. A network attachment definition is created in the same namespace as the pod or VM. We added a network interface to the VM, and assigned the new VM an IP address from the dedicated network.
We also performed a live migration on the VM without interrupting the virtual workload or access, and then verified that the VM IP address remained the same.
Tue, 10 Oct 2023 09:55:24 -0000
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ObjectScale is a software-defined object storage offering from Dell Technologies. It is designed to deliver enterprise-grade, high-performance object storage in a Kubernetes-native environment.
ObjectScale has a layered architecture, with every function in the system built as an independent layer, making the functions horizontally scalable across all nodes and enabling high availability. The S3-compatible ObjectScale software forms the underlying cloud storage service, providing protection, geo-replication, and data access.
Red Hat® OpenShift® Container Platform is a Kubernetes distribution that provides production-oriented container and workload automation. Its declarative deployment approach, dynamic scaling, and self-healing capabilities make OpenShift a suitable platform to host ObjectScale.
The following diagram shows the ObjectScale topology:
Deployment overview
The deployment of ObjectScale consists of three steps:
The bare-metal CSI drivers deployed as part of ObjectScale provide enhanced performance and serviceability. ObjectScale instance provides an easy-to-use web portal for its configuration and management. ObjectStores, user accounts, and buckets are some of the resources that are required to be created before the storage is ready to be consumed.
ObjectStores are independent storage systems with an individualized life cycle. One or more ObjectStores are deployed by each ObjectScale instance. ObjectStores are created, updated, and deleted independently from all other ObjectStores, and managed by the shared ObjectScale instance. Cluster resources such as storage, CPU, and RAM are defined for each ObjectStore based on workload demand inputs that are specified at ObjectStore creation. Resources that are reserved for an ObjectStore at creation may be adjusted at any time.
The minimum requirements for each OpenShift compute node are:
Dell PowerEdge R750 and R7525 servers hosting the Red Hat OpenShift 4.8 are validated for ObjectScale deployment. The validated environment consisted of three compute nodes, each having 12 X 800 GB SSDs. The OpenShift NodePort service was used to access the ObjectScale UI.
ObjectScale supports several modern and traditional use cases. Common use cases on OpenShift container platform are:
The ease of deployment, scalability, fault tolerance, and security capabilities of OpenShift make it a preferred choice for hosting ObjectScale to fulfill object storage demands. ObjectStores running inside OpenShift, can be co-located and managed with the applications they support. This help reduce CapEx and deployment costs while improving time-to-market.
Authors
Indira Kannamedi (indira_kannamedi@dell.com)
Nitesh Mehra (nitesh_mehra@dell.com)
Tue, 28 Mar 2023 13:24:54 -0000
|Read Time: 0 minutes
This blog, published on the Red Hat corporate website, was written for Red Hat Summit 2019. It discusses how Dell EMC and Red Hat work together on joint solutions, including the Dell EMC Ready Architecture for Red Hat OpenShift Container Platform.
https://www.redhat.com/en/blog/how-dell-emc-and-red-hat-work-together-joint-solutions