[TECH PREVIEW] Configuration Assistance with NVIDIA Kubernetes Launch Kit
NVIDIA Kubernetes Launch Kit (l8k) is a CLI tool for deploying and managing NVIDIA cloud-native solutions on Kubernetes. The tool helps provide flexible deployment workflows for optimal network performance with SR-IOV, RDMA, and other networking technologies.
For prerequisites, please refer to the NVIDIA Network Operator Deployment Guide with Kubernetes page.
You will need a Kubernetes cluster with NVIDIA Network Operator helm chart installed.
Discover Cluster Configuration
Deploy a minimal Network Operator profile to automatically discover your cluster’s network capabilities and hardware configuration. This phase can be skipped if you provide your own configuration file.
Select the Deployment Profile
Specify the desired deployment profile via CLI flags or with the natural language prompt for the LLM.
Generate Deployment Files
Based on the discovered/provided configuration, generate a complete set of YAML deployment files tailored to your selected network profile.
Kubernetes Launch Kit supports the following use cases:
SR-IOV Network with RDMA
Host Device Network with RDMA
IP over InfiniBand with RDMA Shared Device
MacVLAN Network with RDMA Shared Device
SR-IOV InfiniBand Network with RDMA
Please refer to the Quick Start Guide for Kubernetes page for more details.
Kubernetes Launch Kit is available as a docker container:
mkdir ~/cluster-configuration
cp /etc/kubernetes/admin.conf ~/cluster-configuration/kubeconfig
docker run -v ~/cluster-configuration:/cluster-configuration --net=host nvcr.io/nvidia/cloud-native/k8s-launch-kit:v25.10.0 --discover-cluster-config --kubeconfig /cluster-configuration/kubeconfig --save-cluster-config /cluster-configuration/config.yaml --log-level debug --save-deployment-files /cluster-configuration/deployments --fabric infiniband --deployment-type rdma_shared --multirail
Don’t forget to enable –net=host and mount the necessary directories for input and output files with -v.
K8s Launch Kit (l8k) is a CLI tool for deploying and managing NVIDIA cloud-native solutions on Kubernetes. The tool helps provide flexible deployment workflows for optimal network performance with SR-IOV, RDMA, and other networking technologies.
### Discover Cluster Configuration
Deploy a minimal Network Operator profile to automatically discover your cluster's
network capabilities and hardware configuration by using --discover-cluster-config.
This phase can be skipped if you provide your own configuration file by using --user-config.
This phase requires --kubeconfig to be specified.
### Generate Deployment Files
Based on the discovered or provided configuration,
generate a complete set of YAML deployment files for the selected network profile.
Files can be saved to disk using --save-deployment-files.
The profile can be defined manually with --fabric, --deployment-type and --multirail flags,
OR generated by an LLM-assisted profile generator with --prompt (requires --llm-api-key and --llm-vendor).
### Deploy to Cluster
Apply the generated deployment files to your Kubernetes cluster by using --deploy. This phase requires --kubeconfig and can be skipped if --deploy is not specified.
Usage:
l8k [flags]
l8k [command]
Available Commands:
completion Generate the autocompletion script for the specified shell
help Help about any command
version Print the version number
Flags:
--ai Enable AI deployment
--deploy Deploy the generated files to the Kubernetes cluster
--deployment-type string Select the deployment type (sriov, rdma_shared, host_device)
--discover-cluster-config Deploy a thin Network Operator profile to discover cluster capabilities
--enabled-plugins string Comma-separated list of plugins to enable (default "network-operator")
--fabric string Select the fabric type to deploy (infiniband, ethernet)
-h, --help help for l8k
--kubeconfig string Path to kubeconfig file for cluster deployment (required when using --deploy)
--llm-api-key string API key for the LLM API (required when using --prompt)
--llm-api-url string API URL for the LLM API (required when using --prompt)
--llm-vendor string Vendor of the LLM API (required when using --prompt) (default "openai-azure")
--log-level string Log level (debug, info, warn, error) (default "info")
--multirail Enable multirail deployment
--prompt string Path to file with a prompt to use for LLM-assisted profile generation
--save-cluster-config string Save discovered cluster configuration to the specified path (default "/opt/nvidia/k8s-launch-kit/cluster-config.yaml")
--save-deployment-files string Save generated deployment files to the specified directory (default "/opt/nvidia/k8s-launch-kit/deployment")
--spectrum-x Enable Spectrum X deployment
--user-config string Use provided cluster configuration file instead of auto-discovery (skips cluster discovery)
Use "l8k [command] --help" for more information about a command.
Complete Workflow
Discover cluster config, generate files, and deploy:
l8k --discover-cluster-config --save-cluster-config ./cluster-config.yaml \
--fabric ethernet --deployment-type sriov --multirail \
--save-deployment-files ./deployments \
--deploy --kubeconfig ~/.kube/config
Discover Cluster Configuration
l8k --discover-cluster-config --save-cluster-config ./my-cluster-config.yaml
Use Existing Configuration
Generate and deploy with pre-existing config:
l8k --user-config ./existing-config.yaml \
--fabric ethernet --deployment-type sriov --multirail \
--deploy --kubeconfig ~/.kube/config
Generate Deployment Files
l8k --user-config ./config.yaml \
--fabric ethernet --deployment-type sriov --multirail \
--save-deployment-files ./deployments
Generate Deployment Files using Natural Language Prompt
Kubernetes Launch Kit supports a LLM-assisted profile generation. You can provide a natural language prompt to the tool and it will generate a deployment profile for you. To configure the LLM, you need to provide the API key to OpenAI Azure backend.
echo "I want to enable multirail networking in my AI cluster" > requirements.txt
l8k --user-config ./config.yaml \
--prompt requirements.txt --llm-vendor openai-azure --llm-api-key <OPENAI_AZURE_KEY> \
--save-deployment-files ./deployments
After the cluster configuration is discovered, the tool will save the configuration to a file. You can use this file as a starting point for your own configuration. Own configuration file can be provided to the tool using –user-config flag.
networkOperator:
version: v25.10.0
componentVersion: network-operator-v25.10.0 # TODO: update to NetworkOperator.ComponentVersion later
repository: nvcr.io/nvidia/cloud-native
namespace: nvidia-network-operator
docaDriver:
version: doca3.2.0-25.10-1.2.8.0-2
unloadStorageModules: false
enableNFSRDMA: false
nvIpam:
poolName: nv-ipam-pool
subnets:
- subnet: 192.168.2.0/24
gateway: 192.168.2.1
- subnet: 192.168.3.0/24
gateway: 192.168.3.1
- subnet: 192.168.4.0/24
gateway: 192.168.4.1
- subnet: 192.168.5.0/24
gateway: 192.168.5.1
- subnet: 192.168.6.0/24
gateway: 192.168.6.1
- subnet: 192.168.7.0/24
gateway: 192.168.7.1
- subnet: 192.168.8.0/24
gateway: 192.168.8.1
- subnet: 192.168.9.0/24
gateway: 192.168.9.1
- subnet: 192.168.10.0/24
gateway: 192.168.10.1
sriov:
mtu: 9000
numVfs: 8
priority: 90
resourceName: sriov_resource
networkName: sriov_network
hostdev:
resourceName: hostdev-resource
networkName: hostdev-network
rdmaShared:
resourceName: rdma_shared_resource
hcaMax: 63
ipoib:
networkName: ipoib-network
macvlan:
networkName: macvlan-network
clusterConfig:
capabilities:
nodes:
sriov: true
rdma: true
ib: true
pfs:
- rdmaDevice: mlx5_0
pciAddress: "0000:03:00.0"
networkInterface: enp3s0f0np0
traffic: east-west
- rdmaDevice: mlx5_1
pciAddress: "0000:03:00.1"
networkInterface: enp3s0f1np1
traffic: east-west
- rdmaDevice: mlx5_2
pciAddress: 0000:81:00.0
networkInterface: enp129s0np0
traffic: east-west
nodeSelector:
feature.node.kubernetes.io/pci-15b3.present: "true"
workerNodes:
- cloud-dev-41
- cloud-dev-40