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Roadmap

Before 0.7.0

Refactoring

  • run_pipeline = evaluate_pipeline | evaluate_pipeline_error | compute_pipeline_error | train_and_evaluate
  • loss = validation_error | error | pipeline_error
  • XParameter = XSpace

Before 0.8.0

Features

  • Utility to get best HPs and architecture to pass to run_pipeline

Documentation

  • Fill up the core documentation pages
  • Fix NAS examples
  • remove graph_dense API

Refactoring

  • FunctionSpace = CodeSpace
  • Rename default-x to prior-x

Before 0.9.0

Features

  • Evolution as acq sampler
  • Generate plot after each evaluation

Fixes

  • Open never closes (talk to Nils)
  • Deadlock in ASHA-like optimizers (talk to Neeratyoy)

Documentation

  • Document summary function

Refactoring

  • Use max_cost_total everywhere instead of budget
  • Merge GP and hierarchical GP
  • Merge gpytorch branch
  • Rethink summary/status API
  • Utility to get incumbent losses over time
  • Restructure folder structure
  • Improve placement of _post_evaluation_hook_function
  • maintained vs unmaintained optimizers
  • Read and sample at the same time metahyper
  • Metahyper into neps

Tests and tooling

  • Add priorband to experimental
  • Add simple regression tests to run on each push

Before 1.0.0 version

Features

  • Seamless ddp via cli launcher
  • Finegrained control over HP user prior
  • Top vs all vs bottom distribution plots
  • Tensorboard visualizations (incumbent plot, ..)
  • Loss distribution plot
  • Print search space upon run
  • Add comprehensive regression tests to run manually on the cluster on each version release
  • Utility to generate code for best architecture
  • Core Feature set in terms of research
  • 3.11 support

Fixes

  • Printing architecture search spaces / search spaces in general
  • Metahyper Refine jobtimelimit feature
  • Optimize dependencies

Refactoring

  • clean up search spaces classes, unused methods
  • break up search space and config aspect
  • Remove hnas branch

Documentation

  • Keep a changelog

Later version

Features

  • neps_examples callable for options of examples
  • Optional argparse adder like pytorch lightning
  • Utility neps.clean to manage existing run results
  • Collect data optionally via phone-home to webserver
  • Add Info dict to status
  • Seed (setting context manager?)
  • BO improvements via Hebo tricks + Mll replacement
  • Checkout Rich logging

Miscellaneous

  • User Mattermost Channel
  • Twitter handle and domain, e.g., neural-pipeline.search
  • Doing research with NePS / Documentation on that or full setup