- Visited https://rancher.cloud.e-infra.cz and remember your namespace.
kubectltool, see kubectl section.
- Computer with installed
Running Simple Virtual Machine
Running true virtual machine is of course not possible in containerized infrastructure. However following steps show how to run something very close to a virtual machine.
Running VM consists of several steps:
- Generate ssh key
- Create a
secretwith public ssh key.
- Choose ssh compatible docker image
- Create and run manifest
In the following, prefer RSA key type for maximum compatibility.
Generate SSH key – Linux/MacOS
Ssh keys are usually located in home directory in
.ssh sub directory and named like
id_rsa.pub. If there is no such directory of files, you can generate new keys using
ssh-keygen command that generates secret and public key. Names of the files are print out during key generation, e.g.:
$ ssh-keygen Generating public/private rsa key pair. Enter file in which to save the key (/home/user/.ssh/id_rsa): Enter passphrase (empty for no passphrase): Enter same passphrase again: Your identification has been saved in /home/user/.ssh/id_rsa. Your public key has been saved in /home/user/.ssh/id_rsa.pub.
In this case,
id_rsa is a private key (keep it secretly and never send),
id_rsa.pub is a public key. Passphrase is not required but recommended.
Generate SSH key – Microsoft Windows
In this case, follow guide here.
For this step, the
kubectl needs to be installed and configured. Create the secret issuing:
kubectl create secret generic ssh-publickey --from-file=ssh-publickey=~/.ssh/id_rsa.pub -n [namespace]
[namespace] with name of your namespace from Rancher. If you generated or are using different key type than RSA, replace
id_rsa.pub with correct public key location.
We created 4 base images for public use:
cerit.io/pub/ssh-base:d10– Debian 10 (Buster) based image
cerit.io/pub/ssh-base:d11– Debian 11 (Bullseye) based image
cerit.io/pub/ssh-base:ubuntu20.04– Ubuntu 20.04 (Focal) based image
cerit.io/pub/ssh-base:ubuntu22.04– Ubuntu 22.04 (Jellyfish) based image
These images can be directly used or can be used as base images for creating own more advanced images, see below.
Download manifest. Edit line 6:
annotations: external-dns.alpha.kubernetes.io/hostname: vm-[namespace].dyn.cloud.e-infra.cz
[namespace] with your namespace. This namespace must be the same as the namespace used for the secret.
If desired, replace
image name on line 34:
with any other image mentioned above like
cerit.io/pub/ssh-base:ubuntu22.04. Save the file and run the manifest:
kubectl create -f vm-simple.yaml -n [namespace]
We suppose that the downloaded and edited file has name
vm-simple.yaml. Replace again
[namespace] with your namespace from Rancher. This command run the manifest. You can check
Pods in Rancher to see your manifest is running:
If manifest is running, you can log in using ssh command. Login name is always
user, it is not meant to be replaced.
[namespace] with your namespace from Rancher and you should see something like this:
anubis: ~ $ ssh firstname.lastname@example.org Warning: Permanently added 'vm-hejtmanek1-ns.dyn.cloud.e-infra.cz' (ED25519) to the list of known hosts. X11 forwarding request failed on channel 0 The programs included with the Debian GNU/Linux system are free software; the exact distribution terms for each program are described in the individual files in /usr/share/doc/*/copyright. Debian GNU/Linux comes with ABSOLUTELY NO WARRANTY, to the extent permitted by applicable law. user@vm-example-0:~$
At this point, you have running your VM for general purpose.
Delete Running VM
If you VM is not needed any more, we kindly request to delete it. It can be deleted issuing:
kubectl delete -f vm-simple.yaml -n [namespace]
vm-simple.yaml is the file used for create and
[namespace] is your namespace from Rancher.
Installing Additional Software
There are two options, how to install additional packages to the VM. You can either rebuild the docker image or you can use existing one, install
conda package manager for further package installation. Using docker, you can install all standard packages from the base system, i.e., Debian or Ubuntu, these packages will be part of the new image and will be always (even after container restart) available.
Note: You cannot install any system package in the running container.
conda, installation of
conda packages is possible even in running container. See caveats below.
If you are not familiar with docker build. Check our documentation. For docker image, docker registry is needed. You can use our https://hub.cerit.io registry, that can store your docker image. Images can be referred as:
cerit.io/[project]/[image]:[tag]. See docs.
To rebuild one of the images above, use the following
FROM cerit.io/pub/ssh-base:d10 RUN apt-get update && apt-get -t install vim less && apt-get clean && rm -rf /var/lib/apt/lists/
Dockerfile creates new version of the docker image with installed
less packages. Store the example into the file
Dockerfile, change the list of installed packages as desired. You can build the docker image using:
docker build -t cerit.io/[login]/[image]:[tag] - < Dockerfile
[login] with our project name in
[image]:[tag] with image name and tag.
To store the image into registry, you need to login to the registry first using:
docker login cerit.io
with credentials you can get on https://hub.cerit.io.
After login, you can push your new image using:
docker push cerit.io/[login]/[image]:[tag]
image in the manifest above with this new name
cerit.io/[login]/[image]:[tag] and delete and run the manifest again.
Conda Package Manager
Using conda or
mamba tool, you can install new packages even in the running container. First, instal
conda/mamba issuing one by one the following commands:
wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh /bin/bash Mambaforge-Linux-x86_64.sh -f -b -p /home/user/conda /home/user/conda/bin/conda init echo '. ~/.bashrc' >> ~/.profile echo 'export LD_LIBRARY_PATH=/home/user/conda/lib' >> ~/.bashrc
Log out and log in again. You should see now prompt like this:
You are now ready to install packages, e.g.,
(base) user@vm-example-0:~$ mamba install mc
After a while,
mamba finishes and you are able to use the installed package
All packages are installed into the
- In this simple case, content of
/home/user/condais not preserved across container restarts. You need to configure persistent home, see below.
- Size of whole container is limited to
4GB. This is set by:
ephemeral-storage: "4Gi", if needed, this value can be increased. If size of
4GBis exceeded, container will be evicted and restarted.
As mentioned above, disks inside container are not persistent. It means that everything that is installed by
mamba is lost if the container is restarted or re-created. To deal with this, a persistent home needs to be created.
You can download manifest that contains definition of persistent home. The persistent home is linked with name of deployment and its version. Name is in the manifest:
kind: StatefulSet metadata: name: vm-pvc-example
Default version is
-0, so in this case, persistent home will be linked with
vm-pvc-example-0. Corresponding PVC is called
home-vm-pvc-example-0. You can find this PVC in Rancher under
PersistentVolumeClaims. This is usable for case when home content is damaged and needs to be deleted. You can delete the PVC from this Rancher UI and start over with empty one.
You need to change the same items in this manifest as in the simple case, i.e.,
image. You run it in the same way as the simple case using
kubectl create -f vm-persistent.yaml -n [namespace]
Both examples above request 1 CPU, 4GB Memory, and 4GB disk. You can change these limits modifying the following part of the manifest:
limits: cpu: "1" memory: "4Gi" ephemeral-storage: "4Gi"
Number of CPU is set as number such as
1, or fraction such as
100mmeaning 0.1 CPU. For Memory and Disk,
Giunits are used meaning Mega Bytes and Giga Bytes, resp.
mamba, at least 4GB Memory is required, or you get
killedmessage when trying to run the
Work with GPU
To work with GPU, manifest resource section must contain GPU request. You can download manifest or use your own with the following addition:
limits: cpu: "1" memory: "4Gi" ephemeral-storage: "4Gi" nvidia.com/gpu: 1
If you run this manifest, NVIDIA drivers and
nvidia-smi commands will be available in the container:
user@vm-pvc-example-0:~$ nvidia-smi Wed May 25 17:53:50 2022 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 470.57.02 Driver Version: 470.57.02 CUDA Version: 11.4 | |-------------------------------+----------------------+----------------------+ | 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 NVIDIA A40 On | 00000000:A3:00.0 Off | 0 | | 0% 34C P8 31W / 300W | 0MiB / 45634MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ user@vm-pvc-example-0:~$
However, CUDA or Tensorflow or Pytorch frameworks need to be installed separately.
CUDA can be installed either modifying running container via Dockerfile as mentioned above or using
mamba. In the latter case, at least 20GB disk is needed (
mamba, installation is possible via:
mamba install cudatoolkit-dev=11.4.0 cudatoolkit=11.4.2
After successful install, you can query CUDA is working issuing:
The output should look like this:
/home/user/conda/pkgs/cuda-toolkit/extras/demo_suite/deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "NVIDIA A40" CUDA Driver Version / Runtime Version 11.4 / 11.4 CUDA Capability Major/Minor version number: 8.6 Total amount of global memory: 45634 MBytes (47850782720 bytes) (84) Multiprocessors, (128) CUDA Cores/MP: 10752 CUDA Cores GPU Max Clock rate: 1740 MHz (1.74 GHz) Memory Clock rate: 7251 Mhz Memory Bus Width: 384-bit L2 Cache Size: 6291456 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 1536 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Enabled Device supports Unified Addressing (UVA): Yes Device supports Compute Preemption: Yes Supports Cooperative Kernel Launch: Yes Supports MultiDevice Co-op Kernel Launch: Yes Device PCI Domain ID / Bus ID / location ID: 0 / 39 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.4, CUDA Runtime Version = 11.4, NumDevs = 1, Device0 = NVIDIA A40 Result = PASS
Install CUDA version as close as possible to the version displayed via
nvidia-smi, currently is is version 11.4.2. Use
mamba search cudato list versions available.
If you get error:
stderr: ./cuda-installer: error while loading shared libraries: libxml2.so.2: cannot open shared object file: No such file or directory
You missed correct setting of
echo 'export LD_LIBRARY_PATH=/home/user/conda/lib' >> ~/.bashrcis missing.
For many GPU applications, increased shared memory (SHM) is required. Default size of shared memory is 64kB for containers. Increasing SHM is done via mounting additional volume into
/dev/shm. You can download example manifest.
Added sections are:
- name: shm mountPath: /dev/shm
- Whole new section
volumes: - name: shm emptyDir: medium: Memory sizeLimit: 1Gi
Indentation of this section must match line
containers, this is important!
These two sections add 1GB of shared memory,
sizeLimit denotes SHM size.
sizeLimit of SHM is used from
memory limits in the
resources sections, meaning, if
memory is set to 5GB and
sizeLimit is 1GB, then 4GB of memory is available for applications. Quering SHM size using
df command is misleading, it does not show true size of SHM.