Running Virtual Machine


  1. Visited and remember your namespace.
  2. Configured kubectl tool, see kubectl section.
  3. Computer with installed ssh command.

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:

  1. Generate ssh key
  2. Create a secret with public ssh key.
  3. Choose ssh compatible docker image
  4. Create and run manifest

SSH Keys

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 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/

In this case, id_rsa is a private key (keep it secretly and never send), is a public key. Passphrase is not required but recommended.

Generate SSH key – Microsoft Windows

In this case, follow guide here.

Create Secret

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/ -n [namespace]

Replace [namespace] with name of your namespace from Rancher. If you generated or are using different key type than RSA, replace with correct public key location.

Docker Image

We created 4 base images for public use:

  1. – Debian 10 (Buster) based image
  2. – Debian 11 (Bullseye) based image
  3. – Ubuntu 20.04 (Focal) based image
  4. – 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: vm-[namespace]

and replace [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 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 Workload -> Pods in Rancher to see your manifest is running: vm-ssh-simple

Logging In

If manifest is running, you can log in using ssh command. Login name is always user, it is not meant to be replaced.

ssh user@vm-[namespace]

Again, replace [namespace] with your namespace from Rancher and you should see something like this:

anubis: ~ $ ssh
Warning: Permanently added '' (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.

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]

Where 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.

However, using conda, installation of conda packages is possible even in running container. See caveats below.

Rebuilding Image

If you are not familiar with docker build. Check our documentation. For docker image, docker registry is needed. You can use our registry, that can store your docker image. Images can be referred as:[project]/[image]:[tag]. See docs.

To rebuild one of the images above, use the following Dockerfile example:


RUN apt-get update && apt-get -t install vim less && apt-get clean && rm -rf /var/lib/apt/lists/

This Dockerfile creates new version of the docker image with installed vim and 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[login]/[image]:[tag] - < Dockerfile

Replace [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

with credentials you can get on

After login, you can push your new image using:

docker push[login]/[image]:[tag]

Replace image in the manifest above with this new name[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:

/bin/bash -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:

(base) user@vm-example-0:~$

You are now ready to install packages, e.g., mc package:

(base) user@vm-example-0:~$ mamba install mc

After a while, mamba finishes and you are able to use the installed package mc.

All packages are installed into the /home/user/conda directory.


  • In this simple case, content of /home/user/conda is 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 4GB is exceeded, container will be evicted and restarted.

Persistent Home

As mentioned above, disks inside container are not persistent. It means that everything that is installed by conda/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
  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 Storage -> 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., and 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:

  cpu: "1"
  memory: "4Gi"
  ephemeral-storage: "4Gi"


  1. Number of CPU is set as number such as 1, or fraction such as 100m meaning 0.1 CPU. For Memory and Disk, Mi or Gi units are used meaning Mega Bytes and Giga Bytes, resp.

  2. For conda/mamba, at least 4GB Memory is required, or you get killed message when trying to run the conda command.

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:

  cpu: "1"
  memory: "4Gi"
  ephemeral-storage: "4Gi" 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                                                 |

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 conda/mamba. In the latter case, at least 20GB disk is needed (ephemeral-storage line).

Using 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


  1. Install CUDA version as close as possible to the version displayed via nvidia-smi, currently is is version 11.4.2. Use mamba search cuda to list versions available.

  2. If you get error:

    stderr: ./cuda-installer: error while loading shared libraries: cannot open shared object file: No such file or directory

    You missed correct setting of conda, especially, echo 'export LD_LIBRARY_PATH=/home/user/conda/lib' >> ~/.bashrc is 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:

  1. Under volumeMounts:
    - name: shm
      mountPath: /dev/shm
  2. Whole new section volumes:
    - name: shm
     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.

Note: 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.