Difference between revisions of "CCU:New GPU Cluster"
m (→Running the first test container on the new cluster) |
m (→Running the first test container on the new cluster) |
||
| Line 138: | Line 138: | ||
kind: Pod | kind: Pod | ||
metadata: | metadata: | ||
| − | name: | + | name: access-pod |
spec: | spec: | ||
containers: | containers: | ||
| Line 180: | Line 180: | ||
| − | Save this into a " | + | Save this e.g. into a "access-pod.yaml", start the pod and verify that it has been created correcly and the filesystems have been mounted successfully, for example with the below commands. You can also check whether you can access the data you have copied over and copy/move it somewhere safe in your private home directory. If you have a large dataset which is probably useful for several people, please contact me so I can move it to the static read-only tree for datasets. |
<syntaxhighlight lang="bash"> | <syntaxhighlight lang="bash"> | ||
| − | > kubectl apply -f | + | > kubectl apply -f access-pod.yaml |
> kubectl get pods | > kubectl get pods | ||
| − | > kubectl describe pod | + | > kubectl describe pod access-pod |
| − | > kubectl exec -it | + | > kubectl exec -it access-pod /bin/bash |
$ ls /abyss/shared/<the directory you created for your data> | $ ls /abyss/shared/<the directory you created for your data> | ||
</syntaxhighlight> | </syntaxhighlight> | ||
Revision as of 18:11, 25 January 2021
Contents
Overview
In January, the old GPU cluster will gradually be dismantled and integrated into a new Kubernetes cluster. The reason is a massive hardware upgrades of the backbone infrastructure:
- New Ceph-based storage cluster with currenly 210TB of NVMe storage to supply all compute nodes with data.
- New network backbone: HDR infiniband (200 GB/s).
- Triple-redundant servers to supply basic services and serve API requests, so that downtime should be minimized.
- As a cherry on top, another GPU server with 4x A100.
Since we reinstall everything from scratch, the usage of the Cluster will also change slightly, both for easier access to storage (getting rid of the somewhat cumbersome need to allocate persistent volumes) and improved security (separate user namespaces).
We first provide a comprehensive list of changes in how to use the cluster, then give a detailed manual for how to move over your data and pods.
Pod configuration on the new cluster
User namespace, pod security and quotas
Each user works in their own namespace now, which is auto-generated when your login is created. The naming convention is as follows:
- Login ID : firstname.lastname
- Username : firstname-lastname
- Namespace: user-firstname-lastname
That means you replace all '.'s in your login ID with a '-' to obtain the username, and prepend "user-" to obtain the namespace.
Thus, you should set your default namespace in the kubeconfig accordingly, and perhaps have to update pod configurations. For security reasons, containers are forced to run with your own user id and a group id of "10000". These will also be the ids used to create files and directories, and decide the permissions you have on the file system. The pod security policy which is active for your namespace will automatically fill in this data. Note that the security policy for pods is very restrictive for now to detect all problematic cases. In particular, you can not switch to root inside containers anymore. Please inform me if security policies disrupt your usual workflow so that we can work something out.
Finally, there is now a mechanism in place to set resource quotas for individual users. The preset is quite generous at the moment since we have plenty of resources, but if you believe your account is too limited, please contact me.
Persistent volume management (or lack thereof)
The ceph storage cluster provides a file system which is mounted on every node in the cluster. Pods are allowed to mount a subset of the filesystem as a host path, see the example pod below. The following directories can be mounted:
- /cephfs/abyss/home/<username>: this is your personal home directory which you can use any way you like.
- /cephfs/abyss/shared: a shared directory where every user has read/write access everywhere, so your data is not secure here - the intention is to have a quick and dirty method to share results between users. To not have total anarchy in this filesystem, please give sensible names and organize in subdirectories. For example, put personal files which you want to make accessible to everyone in "/abyss/shared/users/<username>". I will monitor how it works out and whether we need more rules here.
- /cephfs/abyss/datasets: directory for static datasets, mounted read-only. These are large general-interest datasets for which we only want to store one copy on the filesystem (no separate imagenets for everyone, please). So whenever you have a well-known public dataset in your shared directory which you think is useful to have in the static tree, please contact me and I move it to the read-only region.
Copy data from the old cluster into the new filesystem
The shared file system can be mounted as an nfs volume on the old cluster, so you can create a pod which mounts both the new filesystem as well as your PVs from the old cluster. Please use the following pod configuration as a template and add additional mounts for the PVs you want to copy over:
apiVersion: v1
kind: Pod
metadata:
name: <your-username>-transfer-pod
namespace: exc-cb
spec:
# choose vecna since it has the fastest connection, or another node if you have
# to copy local PVs
nodeSelector:
kubernetes.io/hostname: vecna
containers:
- name: ubuntu
image: ubuntu:20.04
command: ["sleep", "1d"]
volumeMounts:
- mountPath: /abyss/shared
name: cephfs-shared
readOnly: false
volumes:
- name: cephfs-shared
nfs:
path: /cephfs/abyss/shared
server: ccu-node1Afterwards, run a shell in the container and copy your stuff over to /abyss/shared/users/<your-username>. Note that this is not a secure directory as everyone has full read/write access, so copy over to your own home directory on the new cluster as soon as possible. The following should do the trick:
> kubectl exec -it <your-username>-transfer-pod /bin/bash
# cd /cephfs/abyss/shared/users/<your-username>
# cp -r <all-my-stuff> ./Getting started on the new cluster
Login to the new cluster and update your kubeconfig
The frontend for the cluster and login services is located here:
https://ccu-k8s.inf.uni-konstanz.de/
Please choose "login to the cluster" and enter your credentials to obtain the kubeconfig data. Choose "full kubeconfig" on the left for all the details you need. Either backup your old kubeconfig and use this as a new one, or merge them both into a new kubeconfig which allows you to easily switch context between both clusters. In the beginning, this might be useful as you maybe have forgotten some data, and also still need to clean up once everything works.
A kubeconfig for both clusters has the following structure (note this needs to be saved in "~/.kube/config"):
apiVersion: v1
clusters:
- cluster:
certificate-authority-data: LS0tLS1CRUdJTiBDRV ... <many more characters>
server: https://134.34.224.84:6443
name: ccu-old
- cluster:
certificate-authority-data: LS0tLS1CRUdJTiBDRV ... <many more characters>
server: https://ccu-k8s.inf.uni-konstanz.de:7443
name: ccu-new
contexts:
- context:
cluster: ccu-old
namespace: exc-cb
user: credentials-old
name: ccu-old
- context:
cluster: ccu-new
namespace: <your-namespace>
user: credentials-new
name: ccu-new
current-context: ccu-new
kind: Config
preferences: {}
users:
- name: credentials-old
<all the data below your username returned from the old loginapp goes here>
- name: credentials-new
<all the data below your username returned from the new loginapp goes here>
Both the long CA data string and user credentials are returned from the respective loginapps of the clusters. Note: the CA data is different for both clusters, although the first couple of characters are the same. If you have such a kubeconfig for multiple contexts, you can easily switch between the clusters:
> kubectl config use-context ccu-old
> <... work with old cluster>
> kubectl config use-context ccu-new
> <... work with new cluster>Defining different contexts is also a good way to switch between namespaces or users (which should not be necessary for the average user).
Running the first test container on the new cluster
After login and adjusting the kubeconfig to the new cluster and user namespace, you should be able to start your first pod. The following example pod mounts the ceph filesystems into an Ubuntu container image.
apiVersion: v1
kind: Pod
metadata:
name: access-pod
spec:
containers:
- name: ubuntu
image: ubuntu:20.04
command: ["sleep", "1d"]
resources:
requests:
cpu: 100m
memory: 100Mi
limits:
cpu: 1
memory: 1Gi
volumeMounts:
- mountPath: /abyss/home
name: cephfs-home
readOnly: false
- mountPath: /abyss/shared
name: cephfs-shared
readOnly: false
- mountPath: /abyss/datasets
name: cephfs-datasets
readOnly: true
volumes:
- name: cephfs-home
hostPath:
path: "/cephfs/abyss/home/<username>"
type: Directory
- name: cephfs-shared
hostPath:
path: "/cephfs/abyss/shared"
type: Directory
- name: cephfs-datasets
hostPath:
path: "/cephfs/abyss/datasets"
type: Directory
Save this e.g. into a "access-pod.yaml", start the pod and verify that it has been created correcly and the filesystems have been mounted successfully, for example with the below commands. You can also check whether you can access the data you have copied over and copy/move it somewhere safe in your private home directory. If you have a large dataset which is probably useful for several people, please contact me so I can move it to the static read-only tree for datasets.
> kubectl apply -f access-pod.yaml
> kubectl get pods
> kubectl describe pod access-pod
> kubectl exec -it access-pod /bin/bash
$ ls /abyss/shared/<the directory you created for your data>
Moving your workloads to the new cluster
You can now verify that you can start a GPU-enabled pod. Try to create a pod with the following specs to allocate 1 GPU for you somewhere on the cluster.
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod
spec:
containers:
- name: gpu-container
image: docker.io/nvidia/cuda:11.0-base
command: ["sleep", "1d"]
resources:
requests:
cpu: 1
nvidia.com/gpu: 1
memory: 100Mi
limits:
cpu: 1
nvidia.com/gpu: 1
memory: 1GiYou can again switch to a shell in the container and verify GPU capabilities:
> kubectl exec -it gpu-pod /bin/bash
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 A100-SXM4-40GB Off | 00000000:C1:00.0 Off | 0 |
| N/A 27C P0 51W / 400W | 4MiB / 40536MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
Combine with the volume mounts above, and you already have a working environment. For example, you could transfer some code and data of yours to your home directory, and run it in interactive mode in the container as a quick test. Note that there are timeouts in place and an interactive session does not last forever, so it is better to build a custom run script which is executed when the container in the pod starts. See the documentation for more details. TODO: link to respective doc.
Cleaning up
Once everything works for you on the new cluster, please clean up your presence on the old one.
In particular:
- Delete all running pods
- Delete all persistent volume claims. This is the most important step, as it shows me which of the local filesystems of the nodes are not in use anymore, so I can transfer the node over to the new cluster.