Data Flow Facilitator for Machine Learning (dffml) v0.3.1 Release NotesRelease Date: 2019-12-12 // almost 2 years ago
- ✅ Integration tests using the command line interface.
run_dataflowto run a dataflow and test for the same. ### 🔄 Changed
- 🔋 Features were moved from ModelContext to ModelConfig
- CI is now run via GitHub Actions
- ✅ CI testing script is now verbose
- args and config methods of all classes no longer require implementation. BaseConfigurable handles exporting of arguments and creation of config objects for each class based off of the CONFIG property of that class. The CONFIG property is a class which has been decorated with dffml.base.config to make it a dataclass.
- 🔌 Speed up development service install of all plugins in development mode
- 🔌 Speed up named plugin load times ### 🛠 Fixed
- DataFlows with multiple possibilities for a source for an input, now correctly look through all possible sources instead of just the first one.
- DataFlow MemoryRedundancyCheckerContext was using all inputs in an input set and all their ancestors to check redundancy (a hold over from pre uid days). It now correctly only uses the inputs in the parameter set. This fixes a major performance issue.
- MySQL packaging issue.
- Develop service running one off operations correctly json-loads dict types.
- Operations with configs can be run via the development service
- JSON dumping numpy int* and float* caused crash on dump.
- CSV source always loads
src_urlsas strings. ### ✂ Removed
- 🚚 CLI command
operationsremoved in favor of
- Duplicate dataflow diagram code from development service