volumeml

volumeml is a Python library designed to facilitate the management and manipulation of large datasets, particularly those residing in object storage like Amazon S3 or Google Cloud Storage. It aims to simplify common machine learning workflows involving data that may not fit into memory. The library provides an interface for reading, writing, and performing basic transformations on data distributed across multiple files, abstracting away the complexities of parallel processing and data partitioning.

Key features of volumeml include its ability to handle data in various formats, such as CSV, Parquet, and JSON, and its integration with popular data processing frameworks. It allows users to define data schemas and perform operations like filtering, selecting columns, and aggregating data without requiring explicit loading of the entire dataset into RAM. This makes it suitable for scenarios where dataset sizes exceed the available memory of a single machine.

volumeml also supports lazy evaluation, meaning that operations are not executed until their results are explicitly needed. This can lead to performance improvements by allowing the library to optimize the order of operations and only process the necessary data. The library's design emphasizes ease of use, providing a more intuitive approach to working with out-of-core datasets for machine learning tasks. It aims to be a useful tool for data scientists and engineers dealing with big data challenges in their machine learning projects.