Kubeflow
- Kubeflow Charmers | bundle
- Cloud
Channel | Revision | Published |
---|---|---|
latest/stable | 414 | 01 Dec 2023 |
latest/candidate | 294 | 24 Jan 2022 |
latest/beta | 430 | 30 Aug 2024 |
latest/edge | 423 | 26 Jul 2024 |
1.9/stable | 426 | 31 Jul 2024 |
1.9/beta | 420 | 19 Jul 2024 |
1.9/edge | 425 | 31 Jul 2024 |
1.8/stable | 414 | 22 Nov 2023 |
1.8/beta | 411 | 22 Nov 2023 |
1.8/edge | 413 | 22 Nov 2023 |
1.7/stable | 409 | 27 Oct 2023 |
1.7/beta | 408 | 27 Oct 2023 |
1.7/edge | 407 | 27 Oct 2023 |
1.6/stable | 329 | 07 Sep 2022 |
1.6/beta | 326 | 23 Aug 2022 |
1.6/edge | 328 | 07 Sep 2022 |
1.4/stable | 321 | 30 Jun 2022 |
1.4/edge | 320 | 30 Jun 2022 |
juju deploy kubeflow --channel edge
Deploy Kubernetes operators easily with Juju, the Universal Operator Lifecycle Manager. Need a Kubernetes cluster? Install MicroK8s to create a full CNCF-certified Kubernetes system in under 60 seconds.
Platform:
If you want to use Kubeflow backed with NVIDIA GPUs, your nodes must have runtimeClassName: nvidia
parameter specified.
Contents:
MicroK8s
When deploying with MicroK8s , this parameter is added by default when enabling the gpu addon.
microk8s enable gpu
Other Kubernetes clusters
In case of other Kubernetes deployments, this parameter may not be specified. Please refer to this documentation for a general guide on how to setup NVIDIA GPU Operator.