Package: datamate
DatamateDatamate is a lightweight data and configuration management framework for structuring data in machine learning projects on a hierarchical filesystem.Datamate provides a simple framework to work with heterogenous data by automating
input and output of arrays and configurations to disk.
It provides an interface to the system's filesystem through pointers to files
and representations of the hierarchical structure.Typical usecases are:automating pathing and orchestrating dataseamless input and output operations to a hierarchical filesystemkeep track of configurations, e.g. for preprocessing, experiments, analysesstructured preprocessing with minimal overhead code---cause configuration-based, preprocessed data can automatically be computed only once and then referenced tofor instance to skip slow computations when restarting the kernel in youreverything_in_here.ipynbnotebookinteractive prototyping in data-heterogenous applications: hierarchical file views in notebooks, pandas integration, configuration diffs, simultaneous write and readExamplesDatamate'sDirectoryinstances can point to (processed) data on the disk (relative to a root directory),
allowing seamless I/O.E.g., to store a numpy array>>>importdatamate>>>datamate.set_root_dir("./data")>>>directory=datamate.Directory("experiment_01")# pointer to ./data/experiment_01>>>directory.array=np.arange(5)# creates parent directory and writes array to h5 file>>>directoryexperiment_01/-Lastmodified:April04,202208:24:56└──array.h5displaying:1directory,1filesTo retrieve the array:>>>importdatamate>>>datamate.set_root_dir("./data")>>>directory=datamate.Directory("experiment_01")>>>directory.array[:]array([0,1,2,3,4])More detailed examples inexamples/01. Introduction to Datamate.ipynb.InstallationUsing pip:pip install datamateRelated frameworksDatamate is adapted fromartisanto focus on flexibility in interactive jupyter notebooks with only optional configuration and type enforcement.Because cloud-based and relational database solutions for ML-workflows can be little
beginner friendly or little flexible, Datamate is simply based on I/O of arrays and configurations on
disk with pythonic syntax, and it targets interactive and notebook-based workflows.Datamate plays well with other, more advanced tools for data and experiment management.
E.g. combine datamate with hydra for dynamic configuration-based workflows with command line integration.
For a full-fledged cloud-based solution to track ML experiments, check out e.g. wandb.Other than that, object-relational mappers are frameworks to provide APIs to
relational databases. This is particularly useful, when handling data over networks.
E.g. one remote server hosts data that is accessed by many people in collaboration.ML experiment management:hydra, for dynamic configuration management with automatic command line integration.wandb, cloud-based experiment running, data logging, and visualization.Relational databases, object-relational mapper:datajoint, for scientific workflow management based on relational principles.sqlalchemy, for fully exposing SQL features and details.PonyORM, for syntactic sugar when accessing SQL databases.ContributionContributions welcome!