Skip to content

v2.7.0
Compare
Choose a tag to compare
@HideakiImamura HideakiImamura released this 05 Apr 03:38
v2.7.0
cfa9011
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

This is the release note for v2.7.0.

Highlights

New optuna-dashboard Repository

A new dashboard optuna-dashboard is being developed in a separate repository under the Optuna organization. Install it with pip install optuna-dashboard and run it with optuna-dashboard $STORAGE_URL.
The previous optuna dashboard command is now deprecated.

Deprecate n_jobs Argument of Study.optimize

The GIL has been an issue when using the n_jobs argument for multi-threaded optimization. We decided to deprecate this option in favor of the more stable process-level parallelization. Details available in the tutorial. Users who have been parallelizing at the thread level using the n_jobs argument are encouraged to refer to the tutorial for process-level parallelization.

If the objective function is not affected by the GIL, thread-level parallelism may be useful. You can achieve thread-level parallelization in the following way.

with ThreadPoolExecutor(max_workers=5) as executor:
    for _ in range(5):
        executor.submit(study.optimize, objective, 100)

New Tutorial and Examples

Tutorial pages about the usage of the ask-and-tell interface (#2422) and best_trial (#2427) have been added, as well as an example that demonstrates parallel optimization using Ray (#2298) and an example to explain how to stop the optimization based on the number of completed trials instead of the total number of trials (#2449).

Improved Code Quality

The code quality was improved in terms of bug fixes, third party library support, and platform support.

For instance, the bugs on warm starting CMA-ES and visualization.matplotlib.plot_optimization_history were resolved by #2501 and #2532, respectively.

Third party libraries such as PyTorch, fastai, and AllenNLP were updated. We have updated the corresponding integration modules and examples for the new versions. See #2442, #2550 and #2528 for details.

From this version, we are expanding the platform support. Previously, changes were tested in Linux containers. Now, we also test changes merged into the master branch in macOS containers as well (#2461).

Breaking Changes

  • Deprecate dashboard (#2559)
  • Deprecate n_jobs in Study.optimize (#2560)

New Features

  • Support object representation of StudyDirection for create_study arguments (#2516)

Enhancements

  • Change caching implementation of MOTPE (#2406, thanks @y0z!)
  • Fix to replace numpy.append (#2419, thanks @nyanhi!)
  • Modify after_trial for NSGAIISampler (#2436, thanks @jeromepatel!)
  • Print a URL of a related release note in the warning message (#2496)
  • Add log-linear algorithm for 2d Pareto front (#2503, thanks @parsiad!)
  • Concatenate the argument text after the deprecation warning (#2558)

Bug Fixes

  • Use 2.0 style delete API of SQLAlchemy (#2487)
  • Fix Warm Starting CMA-ES with a maximize direction (#2501)
  • Fix visualization.matplotlib.plot_optimization_history for multi-objective (#2532)

Installation

  • Bump torch to 1.8.0 (#2442)
  • Remove Cython from install_requires (#2466)
  • Fix Cython installation for Python 3.9 (#2474)
  • Avoid catalyst 21.3 (#2480, thanks @crcrpar!)

Documentation

  • Add ask and tell interface tutorial (#2422)
  • Add tutorial for re-use of the best_trial (#2427)
  • Add explanation for get_storage in the API reference (#2430)
  • Follow-up of the user-defined pruner tutorial (#2446)
  • Add a new example max_trial_callback to optuna/examples (#2449, thanks @jeromepatel!)
  • Standardize on 'hyperparameter' usage (#2460)
  • Replace MNIST with Fashion MNIST in multi-objective optimization tutorial (#2468)
  • Fix links on SuccessiveHalvingPruner page (#2489)
  • Swap the order of load_if_exists and directions for consistency (#2491)
  • Clarify n_jobs for OptunaSearchCV (#2545)
  • Mention the paper is in Japanese (#2547, thanks @crcrpar!)
  • Fix typo of the paper's author name (#2552)

Examples

  • Add an example of Ray with joblib backend (#2298)
  • Added RL and Multi-Objective examples to examples/README.md (#2432, thanks @jeromepatel!)
  • Replace sh with bash in README of kubernetes examples (#2440)
  • Apply #2438 to pytorch examples (#2453, thanks @crcrpar!)
  • More Examples Folders after #2302 (#2458, thanks @crcrpar!)
  • Apply urllib patch for MNIST download (#2459, thanks @crcrpar!)
  • Update Dockerfile of MLflow Kubernetes examples (#2472, thanks @0x41head!)
  • Replace Optuna's Catalyst pruning callback with Catalyst's Optuna pruning callback (#2485, thanks @crcrpar!)
  • Use whitespace tokenizer instead of spacy tokenizer (#2494)
  • Use Fashion MNIST in example (#2505, thanks @crcrpar!)
  • Update pytorch_lightning_distributed.py to remove MNIST and PyTorch Lightning errors (#2514, thanks @0x41head!)
  • Use OptunaPruningCallback in catalyst_simple.py (#2546, thanks @crcrpar!)
  • Support fastai 2.3.0 (#2550)

Tests

  • Add MOTPESampler in parametrize_multi_objective_sampler (#2448)
  • Extract test cases regarding Pareto front to test_multi_objective.py (#2525)

Code Fixes

  • Fix mypy errors produced by numpy==1.20.0 (#2300, thanks @0x41head!)
  • Simplify the code to find best values (#2394)
  • Use _SearchSpaceTransform in RandomSampler (#2410, thanks @sfujiwara!)
  • Set the default value of state of create_trial as COMPLETE (#2429)

Continuous Integration

Other

  • Add -f option to make clean command idempotent (#2439)
  • Bump master version to 2.7.0dev (#2444)
  • Document how to write a new tutorial in CONTRIBUTING.md (#2463, thanks @crcrpar!)
  • Bump up version number to 2.7.0 (#2561)

Thanks to All the Contributors!

This release was made possible by authors, and everyone who participated in reviews and discussions.

@0x41head, @AmeerHajAli, @Crissman, @HideakiImamura, @c-bata, @crcrpar, @g-votte, @himkt, @hvy, @jeromepatel, @keisuke-umezawa, @not522, @nyanhi, @nzw0301, @parsiad, @sfujiwara, @sile, @toshihikoyanase, @y0z

Assets 2
Loading