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Running Deepseek AI Locally on a HPC Cluster
At this point, many of us have used Deepseek-R1 AI either with the official API or locally on our home computer, but the API is partially censored, and your local computer might lack the computing power to run the larger models. Therefore, in this blog post, I describe how to set up Deepseek-R1 AI on a remote HPC (high-performance computing) cluster. Second, we will explore how to use python to interact with our local instance of Deepseek-R1 AI using ollama-python.
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Is there an Olympic home advantage?
With the conclusion of the Olympic games and an outstanding perfomance of French athletes, I started to wonder if this year they were particularly good. So here is a quick look at historic data on whether there is a home advantage at the Olympics
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Logging when using multiple CPUs
Working with multiple CPUs can greatly speed up processing times, but logging can become challenging because python does not have a straightforward way to log into a single file when using many CPUs. So in this post, I provide a quick and easy way to do just that. Here is how to log into a single file with many CPUs.
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Working with documents of the German Bundestag
Working with data from parliamentary proceedings can be challenging because often it is a lot of effort to collect all the data. Recently the German Parliament has simplified this process by offering an API to download the documents. Here I present BundestagsAPy, a lightweight python wrapper for the Bundestags DIP API.
