Search Results for "compute"

Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines without first obtaining control over those machines. This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data without first putting it all in one (central) place. ...

Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without...

...The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention.

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of...

Jupytab allows you to explore in Tableau data which is generated dynamically by a Jupyter Notebook. You can thus create Tableau data sources in a very flexible way using all the power of Python. This is achieved by having Tableau access data through a web server created by Jupytab.

This toolbox contains a set of Python procedures in order to compute examples associated with the computation of Realizable Sign Conditions (RiSC) due to the approach by Basu, Pollack and Roy. The RiSC approach is a way to solve and analyze systems of polynomial equations and inequalities over the real numbers in the field of real-algebraic geometry. The involved computations often consist of highly sophisticated notions, not easy to illustrate by hand.