This notebook contains various points of analysis for our Spec 2000 benchmark API, so you can see what kind of data is stored in our benchmark database, how to model the data, use this API to your fullest advantage, and a practical example of using this API. This notebook showcases the necessary steps to use the API such as pre-processing the data, performing integrity checks, and utilizing the necessary Python frameworks to develop visualizations. For this notebook, we decided to focus on the “peak_floating_point_rate” field and you can see a visualization of this rate over time within the notebook and how this field changes over time. We incorporated various metrics for making sense of the data by providing data such as the mean, standard deviation, and distribution of the “peak_floating_point_rate” over time.
Google Colab support coming soon!