Hierarchical Data Format prevalent in high performance scientific computing, sits directly on top of sequential or parallel file systems, providing block and stream operations on standardized or custom binary/text objects. Scientific computing platforms such as Python, R, Matlab, Fortran, Julia [and many more...] come with the necessary libraries to read write HDF5 dataset. This edition simplifies interactions with popular linear algebra libraries, provides compiler assisted seamless object persistence, Standard Template Library support and equipped with novel error handling architecture.
H5CPP is a novel approach to persistence in the field of machine learning, it provides high performance sequential and block access to HDF5 containers through modern C++All file and dataset io descriptors implement raii idiom and close underlying resource when going out of scope, and may be seamlessly passed to HDF5 CAPI calls when implicit conversion enabled. Similarly templates can take CAPI
hid_tidentifiers as arguments where applicable provided conversion policy allows. See conversion policy for details.