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CoreWeave Kubernetes Cloud


The Kubernetes environment enables a flexible and reliable method of deploying workloads and services on CoreWeave's Accelerated Compute Cloud.

Deployment examples

Please see the folders in this repository for ready to deploy Kubernetes manifest examples.

Node Labels

Selecting the right hardware for your workload is important. All compute nodes are tagged with a set of labels specifying the hardware type that is available inside. Affinity Rules should be leveraged on workloads to ensure that the desired type of hardware (ie. GPU model) gets assigned to the Pod. The following labels are currently available.

Label Possible Values Description i9, i7, i5, celeron, xeon, epyc The CPU family of the CPU in the node 1G, 10G The uplink speed from the node to the backbone 4-8 Number of GPUs provisioned in the node. Using this selector is not recommended as the GPU resource requests are the correct method of selecting GPU count requirement GeForce_GTX_1070_Ti (see list) GPU model provisioned in the node 6, 8, 11, 16 GPU VRAM in Gigabytes on the GPUs provisioned in the node true, false Denotes if GPUs are interconnected with NVLink 1, 2, 3, 4 PCI Express Version for GPU interfaces 2.5, 5, 8, 16 PCI Express Link Speed for GPU interfaces in GT/s 1, 4, 16 PCI Express Lanes (Bus width) for GPU interfaces ORD1, EWR1, EWR2, BUF1 The region the node is placed in

GPU Availability

Vendor Generation Model VRAM GB Label
NVIDIA Pascal P106-100 6 P106-100
NVIDIA Pascal GTX 1060 6 GeForce_GTX_1060_6GB
NVIDIA Pascal P104-100 8 P104-100
NVIDIA Pascal GTX 1070 8 GeForce_GTX_1070
NVIDIA Pascal GTX 1070 Ti 8 GeForce_GTX_1070_Ti
NVIDIA Pascal GTX 1080 Ti 11 GeForce_GTX_1080_Ti
NVIDIA Volta Titan V 6GB 6 Titan_V_6
NVIDIA Volta Tesla V100 16 Tesla_V100

Included System Resources per GPU Model

GPU Model vCPU RAM GB Great For
P106-100 0.5 6 Batch processing, blockchain compute
GTX 1060 0.5 6 Video transcoding, batch processing
P104-100 0.5 8 Batch processing, blockchain compute, hashcat
GTX 1070 1 8 Video transcoding, rendering, batch processing
GTX 1070 Ti 1 8 Video transcoding, rendering, batch processing
P102-100 1 10 Batch processing, blockchain compute, hashcat
GTX 1080 Ti 1 11 Machine learning, rendering, batch processing
Titan V 6GB 2 10 Batch processing, hashcat, blockchain compute
Tesla V100 3 16 AI inference, rendering, batch processing, hashcat
Tesla V100 NVLINK 4 Xeon Gold 32 Deep learning, neural network training, HPC

Getting Started

Install Kubernetes Command Line Tools

Cut-and-paste instructions are below. For more detail please reference the official documentation.

Mac OS

brew install kubectl


curl -LO`curl -s`/bin/linux/amd64/kubectl
chmod +x ./kubectl
sudo mv ./kubectl /usr/local/bin/kubectl

Set Up Access

You will have received a pre-populated kube-config file from CoreWeave as part of your onboarding package. The snippet below assumes that you have no other Kubernetes credentials stored on your system, if you do you will need to open both files and copy the cluster, context and user from the supplied kube-config file into your existing ~/.kube/config file.

Replace ~/Downloads with the path to the kube-config supplied by CoreWeave.

mkdir -p ~/.kube/
mv ~/Downloads/kube-config ~/.kube/config

Verify Access

Since your new account will not have any resources, listing the secrets is a good start to make sure proper communication with the cluster.

$ kubectl get secret                                                                                                                                                                                                                            git:(master|…
NAME                           TYPE                                  DATA   AGE
default-token-frqgm     3      5d3h

Once access is verified you can deploy the examples found in this repository.

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