Upgrade CUDA from 9.1 to 10.0

[fifar]

Recent deep learning frameworks like TensorFlow and PyTorch require at least CUDA 10.0.

TensorFlow: https://www.tensorflow.org/install/gpu#software_requirements
PyTorch: https://pytorch.org/get-started/locally/

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[rifelpet]

/ok-to-test

[rifelpet]

Thanks for the update! Do you think you could update the documentation with more recent versions that you've tested this on? https://github.com/kubernetes/kops/tree/master/hooks/nvidia-device-plugin#prerequisites otherwise can get this merged and update the docs in a separate PR

[fifar]

Thanks for the update! Do you think you could update the documentation with more recent versions that you've tested this on? https://github.com/kubernetes/kops/tree/master/hooks/nvidia-device-plugin#prerequisites otherwise can get this merged and update the docs in a separate PR

Sure, will update the doc

[rifelpet]

Looks great! Now that I read through the readme I'm wondering about the support for CUDA 9.1. The makefile and docker image will only support CUDA 10.0 correct? It might be weird to have docs that walkthrough setting up CUDA 9.1 if the makefile cant build a CUDA 9.1 image anymore. Though the docs do reference someone else's third party docker image, so theoretically that image should still work with CUDA 9.1.

Perhaps we add something to the readme like

For CUDA 10.0, run DOCKER_REGISTRY= make image push with the desired registry to self-host the docker image. For CUDA 9.1 the image is already hosted according to the InstanceGroup spec example but can be mirrored elsewhere.

[rifelpet]

sorry one more minor thing and then I think its good to merge :) thanks for sticking with this.

that e2e job failure is just a flake so we can retry it if we need to

[rifelpet]

Thanks! Glad we can finally get this up to date
/lgtm
/approve

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This pull-request has been approved by: fifar, rifelpet

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[fifar]

Thanks @rifelpet for your review, especially suggestions

[marcoaleixo]

Hello!

I'm trying to use this new cuda10.0

apiVersion: v1
kind: InstanceGroup
metadata:
  labels:
    kops.k8s.io/cluster: my_cluster
  name: ai_nodes_gpu
spec:
  image: kope.io/k8s-1.15-debian-stretch-amd64-hvm-ebs-2020-01-17
  kubelet:
    featureGates:
      DevicePlugins: "true"
  hooks:
  - execContainer:
      image: marcooliv/nvidia-device-plugin-cuda-10.0

  machineType: p2.xlarge
  maxSize: 1
  minSize: 1
  nodeLabels:
    kops.k8s.io/instancegroup: ai_nodes_gpu
    spot: ai_nodes_gpu
  role: Node
  subnets:
  - us-east-1a

After I create the node I run kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta5/nvidia-device-plugin.yml

Some informations about this nvidia-gpu Node:

Container Runtime Version: docker://18.9.9 Kubelet Version: v1.16.8 Kube-Proxy Version: v1.16.8
The node is created but the nvidia device isn't available.
Am I missing something?

[fifar]

@marcoaleixo The process of a GPU node being ready takes minutes and consists of two steps: 1) the node joins the cluster (say, 2~3 minutes) 2) devices are exposed which is done by the hook container (say, 5~6 minutes). So, after creating the cluster, take a rest, come back and check.
I use this command to check if GPU devices are ready kubectl --namespace=<namespace> get node -o=custom-columns=NAME:.metadata.name,GPUs:.status.capacity.'nvidia\.com/gpu'.

[marcoaleixo]

@fifar thank you for the response.
I'm trying again but It seems my node is in loop?!
Can it take more than 15 minutes?

Your command is returning "none" for all my nodes. In AWS console my Node is ready.

Are you able to test my docker hub image? Or can you share a Node.yaml config?

edit

I think I found the problem:

`kubectl logs -f nvidia-device-plugin-daemonset-bkq57 --namespace=kube-system

2020/05/02 03:49:30 Loading NVML
2020/05/02 03:49:30 Failed to initialize NVML: could not load NVML library.
2020/05/02 03:49:30 If this is a GPU node, did you set the docker default runtime to nvidia?
2020/05/02 03:49:30 You can check the prerequisites at: https://github.com/NVIDIA/k8s-device-plugin#prerequisites
2020/05/02 03:49:30 You can learn how to set the runtime at: https://github.com/NVIDIA/k8s-device-plugin#quick-start
`

[fifar]

@marcoaleixo Not sure what the issue is.

1. You can try my image fifarzh/nvidia-device-plugin:0.2.0-cuda10.0
2. I noticed you had these configurations which I don't set

spec:
  kubelet:
    featureGates:
      DevicePlugins: "true"

[marcoaleixo]

@fifar Yeah, same error "Failed to initialize NVML: could not load NVML library."
Did you make any modification inside your node?
Are you using P2 instance?
Your "apiVersion" is "apiVersion: kops/v1alpha2"?

[fifar]

@marcoaleixo Below is the example

apiVersion: kops.k8s.io/v1alpha2
kind: InstanceGroup
metadata:
  labels:
    kops.k8s.io/cluster: my-cluster-name
  name: gpu
spec:
  hooks:
  - execContainer:
      image: fifarzh/nvidia-device-plugin:0.2.0-cuda10.0
  image: kope.io/k8s-1.15-debian-stretch-amd64-hvm-ebs-2020-01-17
  machineType: 'p2.xlarge'
  maxSize: 1
  minSize: 1
  rootVolumeSize: 256
  nodeLabels:
    kops.k8s.io/instancegroup: gpu
  role: Node
  subnets:
  - us-east-2a

[marcoaleixo]

@fifar even with your configuration didn't work :/

[fifar]

@marcoaleixo sorry, it couldn't help. Please note that my environment is Kubernetes 1.15.5 + kops 1.15.0, check the first row of the test matrix here.

And could you make the gpu node ssh-able and ssh into it, then you can check logs in the directory /nvidia-device-plugin and check init services like kubelet.service and kubelet (if they are running, what statuses of them).

[marcoaleixo]

@fifar Connected via SSH I'm running manually every script and when I run the "nvidia-device-plugin.sh" I'm receiving the error

+++ systemctl stop protokube Failed to stop protokube.service: Unit protokube.service not loaded.

The kubelet is running.
My driver is 410.129 and my CUDA is 10.0, but for some reason kubectl --namespace=default get node -o=custom-columns=NAME:.metadata.name,GPUs:.status.capacity.'nvidia\.com/gpu' return none for all nodes.

Is protokube the main reason of the problem?!

[marcoaleixo]

Well, @fifar
Worked when I build my image without the lines with "systemctl stop protokube" and "systemctl start protokube" inside the file "02-nvidia-docker.sh".

Ty!

[fifar]

Well, @fifar
Worked when I build my image without the lines with "systemctl stop protokube" and "systemctl start protokube" inside the file "02-nvidia-docker.sh".

Ty!

Yeah, these init services are tricky. Good to know you finally get your cluster working. @marcoaleixo