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K8s Launch Kit - CLI for configuring NVIDIA cloud-native solutions

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.

Operation Phases

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.

Usage

\n\n\n

\nK8s 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.\n\n### Discover Cluster Configuration\nDeploy a minimal Network Operator profile to automatically discover your cluster's\nnetwork capabilities and hardware configuration by using --discover-cluster-config.\nThis phase can be skipped if you provide your own configuration file by using --user-config.\nThis phase requires --kubeconfig to be specified.\n\n### Generate Deployment Files\nBased on the discovered or provided configuration, \ngenerate a complete set of YAML deployment files for the selected network profile. \nFiles can be saved to disk using --save-deployment-files.\nThe profile can be defined manually with --fabric, --deployment-type and --multirail flags,\nOR generated by an LLM-assisted profile generator with --prompt (requires --llm-api-key and --llm-vendor).\n\n### Deploy to Cluster\nApply the generated deployment files to your Kubernetes cluster by using --deploy. This phase requires --kubeconfig and can be skipped if --deploy is not specified.\n\nUsage:\n  l8k [flags]\n  l8k [command]\n\nAvailable Commands:\n  completion  Generate the autocompletion script for the specified shell\n  help        Help about any command\n  version     Print the version number\n\nFlags:\n      --ai                             Enable AI deployment\n      --deploy                         Deploy the generated files to the Kubernetes cluster\n      --deployment-type string         Select the deployment type (sriov, rdma_shared, host_device)\n      --discover-cluster-config        Deploy a thin Network Operator profile to discover cluster capabilities\n      --enabled-plugins string         Comma-separated list of plugins to enable (default \"network-operator\")\n      --fabric string                  Select the fabric type to deploy (infiniband, ethernet)\n  -h, --help                           help for l8k\n      --kubeconfig string              Path to kubeconfig file for cluster deployment (required when using --deploy)\n      --llm-api-key string             API key for the LLM API (required when using --prompt)\n      --llm-api-url string             API URL for the LLM API (required when using --prompt)\n      --llm-vendor string              Vendor of the LLM API (required when using --prompt) (default \"openai-azure\")\n      --log-level string               Log level (debug, info, warn, error) (default \"info\")\n      --multirail                      Enable multirail deployment\n      --prompt string                  Path to file with a prompt to use for LLM-assisted profile generation\n      --save-cluster-config string     Save discovered cluster configuration to the specified path (default \"/opt/nvidia/k8s-launch-kit/cluster-config.yaml\")\n      --save-deployment-files string   Save generated deployment files to the specified directory (default \"/opt/nvidia/k8s-launch-kit/deployment\")\n      --spectrum-x                     Enable Spectrum X deployment\n      --user-config string             Use provided cluster configuration file instead of auto-discovery (skips cluster discovery)\n\nUse \"l8k [command] --help\" for more information about a command.\n

Usage Examples

Complete Workflow

Discover cluster config, generate files, and deploy:

l8k --discover-cluster-config --save-cluster-config ./cluster-config.yaml \\\n    --fabric ethernet --deployment-type sriov --multirail \\\n    --save-deployment-files ./deployments \\\n    --deploy --kubeconfig ~/.kube/config\n

Discover Cluster Configuration

l8k --discover-cluster-config --save-cluster-config ./my-cluster-config.yaml\n

Use Existing Configuration

Generate and deploy with pre-existing config:

l8k --user-config ./existing-config.yaml \\\n    --fabric ethernet --deployment-type sriov --multirail \\\n    --deploy --kubeconfig ~/.kube/config\n

Generate Deployment Files

l8k --user-config ./config.yaml \\\n    --fabric ethernet --deployment-type sriov --multirail \\\n    --save-deployment-files ./deployments\n

Generate Deployment Files using Natural Language Prompt

requirements.txt\nl8k --user-config ./config.yaml \\\n --prompt requirements.txt --llm-vendor openai-azure --llm-api-key \\\n --save-deployment-files ./deployments\n\">

echo \"I want to enable multirail networking in my AI cluster\" > requirements.txt\nl8k --user-config ./config.yaml \\\n    --prompt requirements.txt --llm-vendor openai-azure --llm-api-key <OPENAI_AZURE_KEY> \\\n    --save-deployment-files ./deployments\n

Docker container

You can run the l8k tool as a docker container:

docker run -v ~/launch-kubernetes/user-prompt:/user-prompt -v ~/remote-cluster/:/remote-cluster -v /tmp:/output --net=host harbor.mellanox.com/k8s-launch-kit:poc --discover-cluster-config --kubeconfig /remote-cluster/kubeconf.yaml --save-cluster-config /output/config.yaml --log-level debug  --save-deployment-files /output --fabric infiniband --deployment-type rdma_shared --multirail\n

Don't forget to enable --net=host and mount the necessary directories for input and output files with -v.

Documentation

For information on platform support and getting started, visit the official documentation

Get Help

Enterprise Support Get access to knowledge base articles and support cases or submit a ticket.

NVIDIA AI Enterprise Documentation

Visit the NVIDIA AI Enterprise Documentation Hub for release documentation, deployment guides and more.

License Agreement

The product is licensed under Apache 2.0 and contributions are accepted with a DCO. See the contributing document for more information on how to contribute and the release artifacts.

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The tool helps provide flexible deployment workflows for optimal network performance with SR-IOV, RDMA, and other networking technologies.","# K8s Launch Kit - CLI for configuring NVIDIA cloud-native solutions\n\nK8s 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.\n\n## Operation Phases\n\n### Discover Cluster Configuration\nDeploy 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.\n\n### Select the Deployment Profile\nSpecify the desired deployment profile via CLI flags or with the natural language prompt for the LLM.\n\n### Generate Deployment Files\nBased on the discovered/provided configuration, generate a complete set of YAML deployment files tailored to your selected network profile.\n\n## Usage\n\n\n\n\n```\n\nK8s 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.\n\n### Discover Cluster Configuration\nDeploy a minimal Network Operator profile to automatically discover your cluster's\nnetwork capabilities and hardware configuration by using --discover-cluster-config.\nThis phase can be skipped if you provide your own configuration file by using --user-config.\nThis phase requires --kubeconfig to be specified.\n\n### Generate Deployment Files\nBased on the discovered or provided configuration, \ngenerate a complete set of YAML deployment files for the selected network profile. \nFiles can be saved to disk using --save-deployment-files.\nThe profile can be defined manually with --fabric, --deployment-type and --multirail flags,\nOR generated by an LLM-assisted profile generator with --prompt (requires --llm-api-key and --llm-vendor).\n\n### Deploy to Cluster\nApply the generated deployment files to your Kubernetes cluster by using --deploy. This phase requires --kubeconfig and can be skipped if --deploy is not specified.\n\nUsage:\n l8k [flags]\n l8k [command]\n\nAvailable Commands:\n completion Generate the autocompletion script for the specified shell\n help Help about any command\n version Print the version number\n\nFlags:\n --ai Enable AI deployment\n --deploy Deploy the generated files to the Kubernetes cluster\n --deployment-type string Select the deployment type (sriov, rdma_shared, host_device)\n --discover-cluster-config Deploy a thin Network Operator profile to discover cluster capabilities\n --enabled-plugins string Comma-separated list of plugins to enable (default \"network-operator\")\n --fabric string Select the fabric type to deploy (infiniband, ethernet)\n -h, --help help for l8k\n --kubeconfig string Path to kubeconfig file for cluster deployment (required when using --deploy)\n --llm-api-key string API key for the LLM API (required when using --prompt)\n --llm-api-url string API URL for the LLM API (required when using --prompt)\n --llm-vendor string Vendor of the LLM API (required when using --prompt) (default \"openai-azure\")\n --log-level string Log level (debug, info, warn, error) (default \"info\")\n --multirail Enable multirail deployment\n --prompt string Path to file with a prompt to use for LLM-assisted profile generation\n --save-cluster-config string Save discovered cluster configuration to the specified path (default \"/opt/nvidia/k8s-launch-kit/cluster-config.yaml\")\n --save-deployment-files string Save generated deployment files to the specified directory (default \"/opt/nvidia/k8s-launch-kit/deployment\")\n --spectrum-x Enable Spectrum X deployment\n --user-config string Use provided cluster configuration file instead of auto-discovery (skips cluster discovery)\n\nUse \"l8k [command] --help\" for more information about a command.\n```\n\n## Usage Examples\n\n### Complete Workflow\n\nDiscover cluster config, generate files, and deploy:\n\n```bash\nl8k --discover-cluster-config --save-cluster-config ./cluster-config.yaml \\\n --fabric ethernet --deployment-type sriov --multirail \\\n --save-deployment-files ./deployments \\\n --deploy --kubeconfig ~/.kube/config\n```\n\n### Discover Cluster Configuration\n\n```bash\nl8k --discover-cluster-config --save-cluster-config ./my-cluster-config.yaml\n```\n\n### Use Existing Configuration \n\nGenerate and deploy with pre-existing config:\n\n```bash\nl8k --user-config ./existing-config.yaml \\\n --fabric ethernet --deployment-type sriov --multirail \\\n --deploy --kubeconfig ~/.kube/config\n```\n\n### Generate Deployment Files\n\n```bash\nl8k --user-config ./config.yaml \\\n --fabric ethernet --deployment-type sriov --multirail \\\n --save-deployment-files ./deployments\n```\n\n### Generate Deployment Files using Natural Language Prompt\n\n```bash\necho \"I want to enable multirail networking in my AI cluster\" > requirements.txt\nl8k --user-config ./config.yaml \\\n --prompt requirements.txt --llm-vendor openai-azure --llm-api-key \\\n --save-deployment-files ./deployments\n```\n\n## Docker container\n\nYou can run the l8k tool as a docker container:\n\n```bash\ndocker run -v ~/launch-kubernetes/user-prompt:/user-prompt -v ~/remote-cluster/:/remote-cluster -v /tmp:/output --net=host harbor.mellanox.com/k8s-launch-kit:poc --discover-cluster-config --kubeconfig /remote-cluster/kubeconf.yaml --save-cluster-config /output/config.yaml --log-level debug --save-deployment-files /output --fabric infiniband --deployment-type rdma_shared --multirail\n```\n\nDon't forget to enable `--net=host` and mount the necessary directories for input and output files with `-v`.\n\n## Documentation\nFor information on platform support and getting started, visit the official [documentation](https://docs.nvidia.com/networking/software/cloud-orchestration/index.html)\n\n## Get Help\nEnterprise Support Get access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n## NVIDIA AI Enterprise Documentation\nVisit the [NVIDIA AI Enterprise Documentation Hub](https://docs.nvidia.com/ai-enterprise/) for release documentation, deployment guides and more.\n\n## License Agreement\nThe product is licensed under Apache 2.0 and contributions are accepted with a DCO. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498],[361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377],{"_378":472,"_214":472,"_158":380,"_351":473},{"_378":468,"_214":468,"_158":380,"_351":469},{"_378":463,"_214":463,"_158":380,"_351":464},{"_378":459,"_214":459,"_158":380,"_351":460},{"_378":454,"_214":454,"_158":380,"_351":455},{"_378":449,"_214":449,"_158":380,"_351":450},{"_378":441,"_214":441,"_158":380,"_351":442},{"_378":433,"_214":433,"_158":380,"_351":434},{"_378":428,"_214":428,"_158":380,"_351":429},{"_378":423,"_214":423,"_158":380,"_351":424},{"_378":418,"_214":418,"_158":380,"_351":419},{"_378":413,"_214":413,"_158":380,"_351":414},{"_378":408,"_214":408,"_158":380,"_351":409},{"_378":401,"_214":401,"_158":380,"_351":402},{"_378":396,"_214":396,"_158":380,"_351":397},{"_378":389,"_214":389,"_158":380,"_351":390},{"_378":379,"_214":379,"_158":380,"_351":381},"hostName","amazon-adsystem.com","",[382,383],{"_191":388,"_385":379,"_386":30,"_158":387},{"_191":384,"_385":379,"_386":30,"_158":387},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[391,392],{"_191":395,"_385":389,"_386":30,"_158":394},{"_191":393,"_385":389,"_386":30,"_158":394},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593],[540,541,542,543,544],{"_378":454,"_214":454,"_158":380,"_351":564},{"_378":428,"_214":428,"_158":380,"_351":559},{"_378":554,"_214":554,"_158":380,"_351":555},{"_378":549,"_214":549,"_158":380,"_351":550},{"_378":545,"_214":545,"_158":380,"_351":546},"nvidia.partners",[547],{"_191":548,"_385":545,"_386":30,"_158":422},"edge","app.hushly.com",[551],{"_191":552,"_385":549,"_386":30,"_158":553},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[556],{"_191":557,"_385":554,"_386":30,"_158":558},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681],[633,634,635,636],{"_378":648,"_214":648,"_158":380,"_351":649},{"_378":643,"_214":643,"_158":380,"_351":644},{"_378":441,"_214":441,"_158":380,"_351":640},{"_378":554,"_214":554,"_158":380,"_351":637},[638],{"_191":639,"_385":554,"_386":76,"_158":558},"_gh_sess",[641],{"_191":642,"_385":441,"_386":76,"_158":448},"YSC","bizible.com",[645],{"_191":646,"_385":643,"_386":30,"_158":647},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[650],{"_191":651,"_385":648,"_386":30,"_158":652},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_191":723,"_385":500,"_386":30,"_158":724},{"_191":708,"_385":722,"_386":76,"_158":709},{"_191":691,"_385":503,"_386":30,"_158":615},{"_191":691,"_385":603,"_386":30,"_158":615},{"_191":720,"_385":500,"_386":30,"_158":721},{"_191":708,"_385":506,"_386":76,"_158":709},{"_191":718,"_385":500,"_386":30,"_158":719},{"_191":708,"_385":503,"_386":76,"_158":709},{"_191":716,"_385":602,"_386":30,"_158":717},{"_191":708,"_385":624,"_386":76,"_158":709},{"_191":688,"_385":503,"_386":30,"_158":690},{"_191":714,"_385":500,"_386":76,"_158":715},{"_191":712,"_385":500,"_386":30,"_158":713},{"_191":710,"_385":500,"_386":76,"_158":711},{"_191":708,"_385":598,"_386":76,"_158":709},{"_191":706,"_385":500,"_386":30,"_158":707},{"_191":704,"_385":500,"_386":30,"_158":705},{"_191":702,"_385":500,"_386":76,"_158":703},{"_191":700,"_385":607,"_386":30,"_158":701},{"_191":688,"_385":598,"_386":30,"_158":690},{"_191":698,"_385":500,"_386":30,"_158":699},{"_191":696,"_385":500,"_386":76,"_158":697},{"_191":694,"_385":500,"_386":30,"_158":695},{"_191":692,"_385":500,"_386":30,"_158":693},{"_191":691,"_385":500,"_386":30,"_158":615},{"_191":688,"_385":689,"_386":30,"_158":690},{"_191":686,"_385":500,"_386":30,"_158":687},{"_191":684,"_385":500,"_386":76,"_158":685},{"_191":682,"_385":500,"_386":30,"_158":683},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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