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CHANGELOG.md

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CHANGELOG.md

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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Added

  • Definition spec field to specify a class representative of key value pairs for definitions with primitives which are dictionaries
  • Auto generation of documentation for operation implementations, models, and sources. Generated docs include information on configuration options and inputs and outputs for operation implementations.
  • Async helpers got an aenter_stack method which creates and returns and contextlib.AsyncExitStack after entering all the context's passed to it.
  • Example of how to use Data Flow Facilitator / Orchestrator / Operations by writing a Python meta static analysis tool, shouldi

Changed

  • OperationImplementation add_label and add_orig_label methods now use op.name instead of ENTRY_POINT_ORIG_LABEL and ENTRY_POINT_NAME.
  • Make output specs and remap arguments optional for Operations CLI commands.
  • Feature skeleton project is now operations skeleton project

Fixed

  • MemoryOperationImplementationNetwork instantiates OperationImplementations using their withconfig() method.
  • MemorySource now decorated with entry_point
  • MemorySource takes arguments correctly via config_set and config_get
  • skel modules have long_description_content_type set to "text/markdown"
  • Base Orchestrator __aenter__ and __aexit__ methods were moved to the Memory Orchestrator because they are specific to that config.
  • Async helper aenter_stack uses inspect.isfunction so it will bind lambdas

[0.2.0] - 2019-05-23

Added

  • Support for zip file source
  • Async helper for running tasks concurrently
  • Gitter badge to README
  • Documentation on the Data Flow Facilitator subsystem
  • codesec plugin containing operations which gather security related metrics on code and binaries.
  • auth plugin containing an scrypt operation as an example of thread pool usage.

Changed

  • Standardized the API for most classes in DFFML via inheritance from dffml.base
  • Configuration of classes is now done via the args() and config() methods
  • Documentation is now generated using Sphinx

Fixed

  • Corrected maxsplit in util.cli.parser
  • Check that dtype is a class in Tensorlfow DNN
  • CI script no longer always exits 0 for plugin tests
  • Corrected render type in setup.py to markdown

[0.1.2] - 2019-03-29

Added

  • Contribution guidelines
  • Logging documentation
  • Example usage of Git features
  • New Model and Feature creation script
  • New Feature skeleton directory
  • New Model skeleton directory
  • New Feature creation tutorial
  • New Model creation tutorial
  • Added update functionality to the CSV source
  • Added support for Gzip file source
  • Added support for bz2 file source
  • Travis checks for additions to CHANGELOG.md
  • Travis checks for trailing whitespace
  • Added support for lzma file source
  • Added support for xz file source
  • Added Data Flow Facilitator

Changed

  • Restructured documentation to docs folder and moved from rST to markdown
  • Git feature cloc logs if no binaries are in path

Fixed

  • Enable source.file to read from /dev/fd/XX

[0.1.1] - 2019-03-08

Changed

  • Corrected formatting in README for PyPi

[0.1.0] - 2019-03-07

Added

  • Feature class to collect a feature in a dataset
  • Git features to collect feature data from Git repos
  • Model class to wrap implementations of machine learning models
  • Tensorflow DNN model for generic usage of the DNN estimator
  • CLI interface and framework
  • Source class to manage dataset storage