deephyper/deephyper: Changelog - DeepHyper 0.4.0

global updates contributors of DeepHyper now appear on a dedicated page, see DeepHyper Authors, submit a PR if we forgot you! lighter installation via pip install deephyper packed with the minimum requirements for hyperparameter search. update API documentation removed deephyper.benchmark make neural architecture search features optional with pip install deephyper[nas] make auto-sklearn features optional with pip install deephyper[popt] (Pipeline OPTimization) improve epistemic uncertainty quantification for Random Forest surrogate model in Bayesian Optimisation moved deephyper/scikit-optimize as a sub package deephyper.skopt new tutorials dedicated to ALCF systems, see Tutorials - Argonne Leadership Computing Facility deephyper.search renamed AMBS to CBO (Centralised Bayesian Optimization) at deephyper.search.hps.CBO added new scalable Distributed Bayesian Optimization algorithm at deephyper.search.hps.DBO (experimented with up to 4,096 parallel workers) moved problem.add_starting_point of HpProblem to CBO(..., initial_points=[...]) added generative-model based transfer-learning for hyper-parameter optimisation (Example - Transfer Learning for Hyperparameter Search) added filtration of duplicated configurations in CBO CBO(..., filter_duplicated=True) notify failures to the optimiser for it to learn them (Example - Notify Failures in Hyperparameter optimization) added new multi-point acquisition strategy for better scalability in CBO CBO(..., acq_func="UCB, multi_point_strategy="qUCB", ...) added the possibility to switch between synchronous/asynchronous communication in CBO CBO(..., sync_communication=True, ...) deephyper.evaluator added MPI-based Evaluators (better scaling, lower initialisation overhead): MPICommEvaluator and MPIPoolEvaluator added @profile(run_function) decorator for run-function to collect execution times/durations of the black-box, this allow to profile the worker utilisation (Example - Profile the Worker Utilisation) added @queued(Evaluator) decorator for any evaluator class to manage a queue of resources added SerialEvalutor to adapt to serial-search (one by one) added deephyper.evaluator.callback.TqdmCallback to display progress bar when running a search the run-function can now return other values than the objective to be logged in the results.csv for example {"objective": 0.9, "num_parameters": 20000, ...} asyncio is patched automatically when using notebooks/ipython deephyper.ensemble an ensemble API is provided to have uncertainty quantification estimates after running a neural architecture search or hyperparameter search (Tutorial - From Neural Architecture Search to Automated Deep Ensemble with Uncertainty Quantification