Bayesforge™ is a Linux machine image that curates the very best Open Source software for the Data Scientist who needs advanced analytical tools, as well as for Quantum Computing and Computational Mathematics Practitioners who seek to work with one of the major QC frameworks.
The image contains Open Source software from D-Wave, Rigetti as well as the IBM Quantum Experience, together with advanced QC frameworks such as Quantum Fog, and quantum compilers such as Qubiter or ProjectQ. All software is made accessible through the Jupyter WebUI which, due to its modular architecture, allows the user to code in Python (version 3 and 2), R and Octave (even Bash scripting is supported).
A complete list of all the R and Python modules that are installed on the image is accessible through Jupyter reports (in the top level Utility folder).
We are currently readying a separate image that will also include a SageMath kernel. If you have ideas/request for Open Source software that you think we should include with Bayesforge we'd love to hear from you.
Bayesforge is brought to you by artiste-qb.net, a Canadian company that was founded two years ago to speed along the adoption of advanced Bayesian Network analytics as well as Quantum Computation.
Our goal is to build bridges for the (data) scientist to develop models and code in a familiar environment, as well as on a suitable abstraction level, to make accessing these new and exciting Quantum Computing resources as seamless and easy as possible.
Please contact us if you are interested in deploying Bayeforge in an enterprise setting and require first level Tech Support or IT consulting services to make that happen.
Feel free to email us if you have questions or feedback on Bayesforge. We are always looking for suggestions on additional Open Source software to include, and other ways to improve the product. Or just reach out to say hello!
The image is based on Ubuntu Server and free tier eligible i.e. Amazon will allow you to use it for up to a year without a charge as long as you stay within 30 GB of storage volume and use a free tier EC2 instance (currently that is a t2.micro instance which gives you one CPU core and 1GB of RAM).
The image itself takes 9GB which leaves enough space for small analytical projects.
The machine image kicks-off a dedicated Jupyter Instance that runs on port 1955, and we use port 80 for the Apache web server. Amazon has the policy that machine images need to be accessible via ssh, which means port 22 also needs to be open.
When spinning up the Bayesforge EC2 instance, an AWS security group will already be created by default to open these ports. Please note that unless you have a static IP address you will need to ignore the warning on IP address restrictions:
In order to access the Bayesforge image low level via ssh console, Amazon requires you to create a key pair (this will only have to be done once). In most cases you should have no need to access Bayesforge on this low level, but it is an Amazon policy that every customer must have this access granted:
Amazon explains how to go about this in this User Guide.
Once the keys are created, they show up in the drop down box, and you can launch your Bayesforge instance with a single click.
Make sure that you select the t2.micro EC2 instance type if you want to take adantage of the free tier offer.
After clicking the launch button you can check the images status in your EC2 console. Once the image has started up, simply copy and paste your public DNS URL into the browser to connect to your instance web server (see example below in which the Public DNS entry is highlighted blue). This will open the Bayesforge License landing page. After accepting the license you can follow the on screen instructions to open your Jupyter instance.
Note that the 750 hours included for each micro instance in the free tier offer means that you can leave the Bayesforge EC2 instance operational 24/7 without incurring any monthly cost for an entire year.