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This is a living document containing NNI team's current priorities as well as release notes for previous releases. Future roadmap last updated 11/07/2018.

NNI Roadmap The following is a summary of the NNI team's backlog for the next 6 months. Some completed items are included to provide the context and progress of the work. If you have any questions or suggestions about this roadmap, you are highly encouraged to submit a github issue directly to nni project.

OS and installation supports

• Support Linux
  • Support pip install
  • Support source codes install
  • Support package install
• Support Mac
• Support Windows

Algorithms (Tuner, Assessors and Trials)

• Support hyperopt_tpe (TPE)
• Support hyperopt_annealing
• Support hyperopt_random
• Support evolution_tuner
• Support medianstop algorithm for early stop assessor
• Support automatic model selection
• Support Ensemble solution
• Support ENAS
• New Tuners Supports
  • Hyperband
  • Grid search

Training Services

• Support Kubernetes
• Support other Cloud based training services (Azure Kubernetes Service, etc.)
• Support more efficient trial job training by leveraging optimizations in system level

WebUX

• Web UX refactor v1
• Web UX enhancement and new features
  • Multiple experiments

Code Refactor

• DB refactor. Use Key-value DB to replace SQLite.

Examples and PR

• .ipynb samples
• Fashionmnist