How to Deploy Riskquant on Docker to Prioritise Business Risk

How to setup and deploy Riskquant on Docker using a Vultr VPS

How to Deploy Riskquant on Docker to Prioritise Business Risk
Deploying Riskquant using Docker

Setting up Riskquant with Docker

For those who are more technically minded and want to have a crack themselves - here's a quick tutorial I've together based on my instance of the tool.

Startup the Vultr Instance

  1. Fire up a brand new Ubuntu 18.04 VPS from the provider of your choice or deploy it locally on your device and log in, I'm using a $6 VPS based in Australia from Vultr which is more than enough to test the functionality out as long as you add swap space.

2. Before starting ensure the VPS is up to date by running the following commands:

sudo apt-get update
sudo apt-get upgrade
Vultr Dashboard for deploying VPS instances
Deploy an inexpensive $6 Vultr Node

3. [optional] This part is optional. If you are running with less than 1 GB of Ram I would suggest adding some swap to your VPS to assist docker during the container build process as tensor flow is a rather large download/file extraction.

Create a /swapfile, 1GB in size, I would adjust the size to fit your needs but 1 GB should be fine.

sudo fallocate -l 1G /swapfile

Ensure the /swapfile is only accessible by root to avoid any security implications:

sudo chmod 600 /swapfile

Mark the /swapfile to be used as the system as swap space

sudo mkswap /swapfile

After making the file we need to allow the system to use it

sudo swapon /swapfile

Finally, ensure that the swap file is permanent by editing /etc/fstab and adding the following line at the bottom of the file

echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab

Ensure that the swap space (Swp) is showing on your system via top or htop

Htop command output
Check that the Swap Space is Present with 82.5MB used out of 3GB here

For a full reference of the swap space setup follow the Digital Ocean tutorial for setting up swap space.

Install Docker

4. Install docker, we're using the version included in ubuntu 18.04 so this may not be the most up-to-date version but works fine with Riskquant.

sudo apt install

5. To build the docker image we need to clone the GitHub Riskquant repository to our server with a simple git clone command using a sub-user on the VPS.

Deploy Riskquant

git clone

6. Once downloaded enter the folder we just cloned with the below command

cd riskquant/

7. For the tool to run in docker we first need to build the image locally that we just cloned.

docker build -t riskquant .

8. Once built, we need to create a data sub-directory within the Riskquant master directory which is used to house both the CSV file we parse to the tool and the tool's CSV output.

mdkir data

9. let's create a CSV file for the tool to read from

nano input.csv

Ctrl-O + enter when done.

when defining the risks you want to parse into the CSV you'll need to use the following format:

  • Identifier: An identifying label, which need not be unique - but is intended to provide a way of identifying the scenario.
  • Name: The name of the scenario, which should be more descriptive than the identifier.
  • Probability (Frequency): The number of times that the loss will occur over some time
  • Low loss magnitude: A dollar value of the best-case scenario, given that the loss did occur.
  • High loss magnitude: A dollar value of the worst-case scenario. Our detection systems didn't work, and the problem persisted for a long time.

10. As we're running the tool in a docker container, we need to link the data folder we just created when we run the container so it can read the risks we've defined in the input.csv contained within the data folder for it to make its analysis.

docker container run --rm -it   -v "/root/riskquant/data/":/data/   riskquant --file /data/input.csv

11. When the tool runs it should create a new risk priortised.csv which you can pull calculations from


Have a crack with your own risks. I'd love to hear about anyone using some of the more advanced methods that the tool supports and any interesting observations.

If you run into any issues or are keen to share your ideas and outputs please comment below and I'll do my best to help out - enjoy!