Koda

• Koda
  • Koda Distinguishing Features
  • Why to Use Koda

Koda

Koda is a Python + C++ library that provides a data representation and domain-specific language (a.k.a DSL) for vectorized data transformations and computations on the transformed data with high performance. The data can be in the form of protos, tables, interconnected objects, graphs, tensors and more.

Such transformations and computations can be designed in an interactive REPL (e.g. Colab), as well compiled into computation graphs and served in production (i.e. C++ environments).

Koda Distinguishing Features

• Vectorization for complex data: supports vectorized operations with C++ grade performance not only for tables and arrays of primitives, but also for nested dicts, protos, structs, and graphs.
• Immutability: enables modifications and keeping multiple, slightly varied versions of data without duplicating memory.
• Modular data: data can be efficiently (usually for O(1)) split and joined, overlaid or enriched, and the data modules can be not just tables, columns and rows, but anything - from single attributes to proto updates to graph modifications.
• Computational graphs, lazy evaluation and serving support: utilizes computational graphs that can be introspected and modified, and enable optimized data processing and serving.

Why to Use Koda

• Interactivity in Colab: transform interactively training data, design decision-making logic and evaluation flows (scoring, ranking, metrics etc.), work with models and more, where your data is tables, protos, structs, graphs etc.
• What-if experiments: change input data, change evaluation logic, and get insights instantly.
• Zoom in on your data: utilize data views, updates, overlays, versions, including when working with large data sources, and use only parts of the data needed at the moment.
• Performance: computation is vectorized and performed in optimized C++.
• Evaluate in distributed environment or serve in production: convert evaluation logic into computational graphs (ASTs) that can be introspected, optimized then evaluated in distributed environment or served in production.