Major Updates

Neural Architecture Search

• Retiarii Framework (NNI NAS 2.0) Beta Release with new features:

  • Support new high-level APIs: Repeat and Cell (#3481)
  • Support pure-python execution engine (#3605)
  • Support policy-based RL strategy (#3650)
  • Support nested ModuleList (#3652)
  • Improve documentation (#3785)

  Note: there are more exciting features of Retiarii planned in the future releases, please refer to Retiarii Roadmap for more information.

• Add new NAS algorithm: Blockwise DNAS FBNet (#3532, thanks the external contributor @alibaba-yiwuyao)

Model Compression

• Support Auto Compression Framework (#3631)
• Support slim pruner in Tensorflow (#3614)
• Support LSQ quantizer (#3503, thanks the external contributor @chenbohua3)
• Improve APIs for iterative pruners (#3507 #3688)

Training service & Rest

• Support 3rd-party training service (#3662 #3726)
• Support setting prefix URL (#3625 #3674 #3672 #3643)
• Improve NNI manager logging (#3624)
• Remove outdated TensorBoard code on nnictl (#3613)

Hyper-Parameter Optimization

• Add new tuner: DNGO (#3479 #3707)
• Add benchmark for tuners (#3644 #3720 #3689)

WebUI

• Improve search parameters on trial detail page (#3651 #3723 #3715)
• Make selected trials consistent after auto-refresh in detail table (#3597)
• Add trial stdout button on local mode (#3653 #3690)

Examples & Documentation

• Convert all trial examples' from config v1 to config v2 (#3721 #3733 #3711 #3600)
• Add new jupyter notebook examples (#3599 #3700)

Dev Excellent

• Upgrade dependencies in Dockerfile (#3713 #3722)
• Substitute PyYAML for ruamel.yaml (#3702)
• Add pipelines for AML and hybrid training service and experiment config V2 (#3477 #3648)
• Add pipeline badge in README (#3589)
• Update issue bug report template (#3501)

Bug Fixes & Minor Updates

• Fix syntax error on Windows (#3634)
• Fix a logging related bug (#3705)
• Fix a bug in GPU indices (#3721)
• Fix a bug in FrameworkController (#3730)
• Fix a bug in export_data_url format (#3665)
• Report version check failure as a warning (#3654)
• Fix bugs and lints in nnictl (#3712)
• Fix bug of optimize_mode on WebUI (#3731)
• Fix bug of useActiveGpu in AML v2 config (#3655)
• Fix bug of experiment_working_directory in Retiarii config (#3607)
• Fix a bug in mask conflict (#3629, thanks the external contributor @Davidxswang)
• Fix a bug in model speedup shape inference (#3588, thanks the external contributor @Davidxswang)
• Fix a bug in multithread on Windows (#3604, thanks the external contributor @Ivanfangsc)
• Delete redundant code in training service (#3526, thanks the external contributor @maxsuren)
• Fix typo in DoReFa compression doc (#3693, thanks the external contributor @Erfandarzi)
• Update docstring in model compression (#3647, thanks the external contributor @ichejun)
• Fix a bug when using Kubernetes container (#3719, thanks the external contributor @rmfan)

Major updates

Neural Architecture Search

• Improve NAS 2.0 (Retiarii) Framework (Alpha Release)

  • Support local debug mode (#3476)
  • Support nesting ValueChoice in LayerChoice (#3508)
  • Support dict/list type in ValueChoice (#3508)
  • Improve the format of export architectures (#3464)
  • Refactor of NAS examples (#3513)
  • Refer to here <https://github.com/microsoft/nni/issues/3301>__ for Retiarii Roadmap

Model Compression

• Support speedup for mixed precision quantization model (Experimental) (#3488 #3512)
• Support model export for quantization algorithm (#3458 #3473)
• Support model export in model compression for TensorFlow (#3487)
• Improve documentation (#3482)

nnictl & nni.experiment

• Add native support for experiment config V2 (#3466 #3540 #3552)
• Add resume and view mode in Python API nni.experiment (#3490 #3524 #3545)

Training Service

• Support umount for shared storage in remote training service (#3456)
• Support Windows as the remote training service in reuse mode (#3500)
• Remove duplicated env folder in remote training service (#3472)
• Add log information for GPU metric collector (#3506)
• Enable optional Pod Spec for FrameworkController platform (#3379, thanks the external contributor @mbu93)

WebUI

• Support launching TensorBoard on WebUI (#3454 #3361 #3531)
• Upgrade echarts-for-react to v5 (#3457)
• Add wrap for dispatcher/nnimanager log monaco editor (#3461)

Bug Fixes

• Fix bug of FLOPs counter (#3497)
• Fix bug of hyper-parameter Add/Remove axes and table Add/Remove columns button conflict (#3491)
• Fix bug that monaco editor search text is not displayed completely (#3492)
• Fix bug of Cream NAS (#3498, thanks the external contributor @AliCloud-PAI)
• Fix typos in docs (#3448, thanks the external contributor @OliverShang)
• Fix typo in NAS 1.0 (#3538, thanks the external contributor @ankitaggarwal23)

Major updates

Neural architecture search

• Improve NAS 2.0 (Retiarii) Framework (Improved Experimental)

  • Improve the robustness of graph generation and code generation for PyTorch models (#3365)
  • Support the inline mutation API ValueChoice (#3349 #3382)
  • Improve the design and implementation of Model Evaluator (#3359 #3404)
  • Support Random/Grid/Evolution exploration strategies (i.e., search algorithms) (#3377)
  • Refer to here for Retiarii Roadmap

Training service

• Support shared storage for reuse mode (#3354)
• Support Windows as the local training service in hybrid mode (#3353)
• Remove PAIYarn training service (#3327)
• Add "recently-idle" scheduling algorithm (#3375)
• Deprecate preCommand and enable pythonPath for remote training service (#3284 #3410)
• Refactor reuse mode temp folder (#3374)

nnictl & nni.experiment

• Migrate nnicli to new Python API nni.experiment (#3334)
• Refactor the way of specifying tuner in experiment Python API (nni.experiment), more aligned with nnictl (#3419)

WebUI

• Support showing the assigned training service of each trial in hybrid mode on WebUI (#3261 #3391)
• Support multiple selection for filter status in experiments management page (#3351)
• Improve overview page (#3316 #3317 #3352)
• Support copy trial id in the table (#3378)

Documentation

• Improve model compression examples and documentation (#3326 #3371)
• Add Python API examples and documentation (#3396)
• Add SECURITY doc (#3358)
• Add 'What's NEW!' section in README (#3395)
• Update English contributing doc (#3398, thanks external contributor @Yongxuanzhang)

Bug fixes

• Fix AML outputs path and python process not killed (#3321)
• Fix bug that an experiment launched from Python cannot be resumed by nnictl (#3309)
• Fix import path of network morphism example (#3333)
• Fix bug in the tuple unpack (#3340)
• Fix bug of security for arbitrary code execution (#3311, thanks external contributor @huntr-helper)
• Fix NoneType error on jupyter notebook (#3337, thanks external contributor @tczhangzhi)
• Fix bugs in Retiarii (#3339 #3341 #3357, thanks external contributor @tczhangzhi)
• Fix bug in AdaptDL mode example (#3381, thanks external contributor @ZeyaWang)
• Fix the spelling mistake of assessor (#3416, thanks external contributor @ByronCHAO)
• Fix bug in ruamel import (#3430, thanks external contributor @rushtehrani)

Major updates

Neural architecture search

• Support an improved NAS framework: Retiarii (experimental)
  • Feature roadmap
  • Related issues and pull requests
  • Documentation
• Support a new NAS algorithm: Cream (#2705)
• Add a new NAS benchmark for NLP model search (#3140)

Training service

• Support hybrid training service (#3097 #3251 #3252)
• Support AdlTrainingService, a new training service based on Kubernetes (#3022, thanks external contributors Petuum @pw2393)

Model compression

• Support pruning schedule for fpgm pruning algorithm (#3110)
• ModelSpeedup improvement: support torch v1.7 (updated graph_utils.py) (#3076)
• Improve model compression utility: model flops counter (#3048 #3265)

WebUI & nnictl

• Support experiments management on WebUI, add a web page for it (#3081 #3127)
• Improve the layout of overview page (#3046 #3123)
• Add navigation bar on the right for logs and configs; add expanded icons for table (#3069 #3103)

Others

• Support launching an experiment from Python code (#3111 #3210 #3263)
• Refactor builtin/customized tuner installation (#3134)
• Support new experiment configuration V2 (#3138 #3248 #3251)
• Reorganize source code directory hierarchy (#2962 #2987 #3037)
• Change SIGKILL to SIGTERM in local mode when cancelling trial jobs (#3173)
• Refector hyperband (#3040)

Documentation

• Port markdown docs to reStructuredText docs and introduce githublink (#3107)
• List related research and publications in doc (#3150)
• Add tutorial of saving and loading quantized model (#3192)
• Remove paiYarn doc and add description of reuse config in remote mode (#3253)
• Update EfficientNet doc to clarify repo versions (#3158, thanks external contributor @ahundt)

Bug fixes

• Fix exp-duration pause timing under NO_MORE_TRIAL status (#3043)
• Fix bug in NAS SPOS trainer, apply_fixed_architecture (#3051, thanks external contributor @HeekangPark)
• Fix _compute_hessian bug in NAS DARTS (PyTorch version) (#3058, thanks external contributor @hroken)
• Fix bug of conv1d in the cdarts utils (#3073, thanks external contributor @athaker)
• Fix the handling of unknown trials when resuming an experiment (#3096)
• Fix bug of kill command under Windows (#3106)
• Fix lazy logging (#3108, thanks external contributor @HarshCasper)
• Fix checkpoint load and save issue in QAT quantizer (#3124, thanks external contributor @eedalong)
• Fix quant grad function calculation error (#3160, thanks external contributor @eedalong)
• Fix device assignment bug in quantization algorithm (#3212, thanks external contributor @eedalong)
• Fix bug in ModelSpeedup and enhance UT for it (#3279)
• and others

Release 1.9 - 10/22/2020

Major updates

Neural architecture search

• Support regularized evolution algorithm for NAS scenario (#2802)
• Add NASBench201 in search space zoo (#2766)

Model compression

• AMC pruner improvement: support resnet, support reproduction of the experiments (default parameters in our example code) in AMC paper (#2876 #2906)
• Support constraint-aware on some of our pruners to improve model compression efficiency (#2657)
• Support "tf.keras.Sequential" in model compression for TensorFlow (#2887)
• Support customized op in the model flops counter (#2795)
• Support quantizing bias in QAT quantizer (#2914)

Training service

• Support configuring python environment using "preCommand" in remote mode (#2875)
• Support AML training service in Windows (#2882)
• Support reuse mode for remote training service (#2923)

WebUI & nnictl

• The "Overview" page on WebUI is redesigned with new layout (#2914)
• Upgraded node, yarn and FabricUI, and enabled Eslint (#2894 #2873 #2744)
• Add/Remove columns in hyper-parameter chart and trials table in "Trials detail" page (#2900)
• JSON format utility beautify on WebUI (#2863)
• Support nnictl command auto-completion (#2857)

UT & IT

• Add integration test for experiment import and export (#2878)
• Add integration test for user installed builtin tuner (#2859)
• Add unit test for nnictl (#2912)

Documentation

• Refactor of the document for model compression (#2919)

Bug fixes

• Bug fix of naïve evolution tuner, correctly deal with trial fails (#2695)
• Resolve the warning "WARNING (nni.protocol) IPC pipeline not exists, maybe you are importing tuner/assessor from trial code?" (#2864)
• Fix search space issue in experiment save/load (#2886)
• Fix bug in experiment import data (#2878)
• Fix annotation in remote mode (python 3.8 ast update issue) (#2881)
• Support boolean type for "choice" hyper-parameter when customizing trial configuration on WebUI (#3003)

Release 1.8 - 8/27/2020

Major updates

Training service

• Access trial log directly on WebUI (local mode only) (#2718)
• Add OpenPAI trial job detail link (#2703)
• Support GPU scheduler in reusable environment (#2627) (#2769)
• Add timeout for web_channel in trial_runner (#2710)
• Show environment error message in AzureML mode (#2724)
• Add more log information when copying data in OpenPAI mode (#2702)

WebUI, nnictl and nnicli

• Improve hyper-parameter parallel coordinates plot (#2691) (#2759)
• Add pagination for trial job list (#2738) (#2773)
• Enable panel close when clicking overlay region (#2734)
• Remove support for Multiphase on WebUI (#2760)
• Support save and restore experiments (#2750)
• Add intermediate results in export result (#2706)
• Add command to list trial results with highest/lowest metrics (#2747)
• Improve the user experience of nnicli with examples (#2713)

Neural architecture search

• Search space zoo: ENAS and DARTS (#2589)
• API to query intermediate results in NAS benchmark (#2728)

Model compression

• Support the List/Tuple Construct/Unpack operation for TorchModuleGraph (#2609)
• Model speedup improvement: Add support of DenseNet and InceptionV3 (#2719)
• Support the multiple successive tuple unpack operations (#2768)
• Doc of comparing the performance of supported pruners (#2742)
• New pruners: Sensitivity pruner (#2684) and AMC pruner (#2573) (#2786)
• TensorFlow v2 support in model compression (#2755)

Backward incompatible changes

• Update the default experiment folder from $HOME/nni/experiments to $HOME/nni-experiments. If you want to view the experiments created by previous NNI releases, you can move the experiments folders from $HOME/nni/experiments to $HOME/nni-experiments manually. (#2686) (#2753)
• Dropped support for Python 3.5 and scikit-learn 0.20 (#2778) (#2777) (2783) (#2787) (#2788) (#2790)

Others

• Upgrade TensorFlow version in Docker image (#2732) (#2735) (#2720)

Examples

• Remove gpuNum in assessor examples (#2641)

Documentation

• Improve customized tuner documentation (#2628)
• Fix several typos and grammar mistakes in documentation (#2637 #2638, thanks @tomzx)
• Improve AzureML training service documentation (#2631)
• Improve CI of Chinese translation (#2654)
• Improve OpenPAI training service documenation (#2685)
• Improve documentation of community sharing (#2640)
• Add tutorial of Colab support (#2700)
• Improve documentation structure for model compression (#2676)

Bug fixes

• Fix mkdir error in training service (#2673)
• Fix bug when using chmod in remote training service (#2689)
• Fix dependency issue by making _graph_utils imported inline (#2675)
• Fix mask issue in SimulatedAnnealingPruner (#2736)
• Fix intermediate graph zooming issue (#2738)
• Fix issue when dict is unordered when querying NAS benchmark (#2728)
• Fix import issue for gradient selector dataloader iterator (#2690)
• Fix support of adding tens of machines in remote training service (#2725)
• Fix several styling issues in WebUI (#2762 #2737)
• Fix support of unusual types in metrics including NaN and Infinity (#2782)
• Fix nnictl experiment delete (#2791)

Release 1.7.1 - 8/1/2020

Bug Fixes

• Fix pai training service error handling #2692
• Fix pai training service codeDir copying issue #2673
• Upgrade training service to support latest pai restful API #2722

Release 1.7 - 7/8/2020

Major Features

Training Service

• Support AML(Azure Machine Learning) platform as NNI training service.
• OpenPAI job can be reusable. When a trial is completed, the OpenPAI job won't stop, and wait next trial. refer to reuse flag in OpenPAI config.
• Support ignoring files and folders in code directory with .nniignore when uploading code directory to training service.

Neural Architecture Search (NAS)

• Provide NAS Open Benchmarks (NasBench101, NasBench201, NDS) with friendly APIs.

• Support Classic NAS (i.e., non-weight-sharing mode) on TensorFlow 2.X.

Model Compression

• Improve Model Speedup: track more dependencies among layers and automatically resolve mask conflict, support the speedup of pruned resnet.

• Added new pruners, including three auto model pruning algorithms: NetAdapt Pruner, SimulatedAnnealing Pruner, AutoCompress Pruner, and ADMM Pruner.

• Added model sensitivity analysis tool to help users find the sensitivity of each layer to the pruning.

• Easy flops calculation for model compression and NAS.

• Update lottery ticket pruner to export winning ticket.

Examples

• Automatically optimize tensor operators on NNI with a new customized tuner OpEvo.

Built-in tuners/assessors/advisors

• Allow customized tuners/assessor/advisors to be installed as built-in algorithms.

WebUI

• Support visualizing nested search space more friendly.
• Show trial's dict keys in hyper-parameter graph.
• Enhancements to trial duration display.

Others

• Provide utility function to merge parameters received from NNI
• Support setting paiStorageConfigName in pai mode

Documentation

• Improve documentation for model compression
• Improve documentation and examples for NAS benchmarks.
• Improve documentation for AzureML training service
• Homepage migration to readthedoc.

Bug Fixes

• Fix bug for model graph with shared nn.Module
• Fix nodejs OOM when make build
• Fix NASUI bugs
• Fix duration and intermediate results pictures update issue.
• Fix minor WebUI table style issues.

Release 1.6 - 5/26/2020

Major Features

New Features and improvement

• support __version__ for SDK version
• support windows dev install
• Improve IPC limitation to 100W
• improve code storage upload logic among trials in non-local platform

HPO Updates

• Improve PBT on failure handling and support experiment resume for PBT

NAS Updates

• NAS support for TensorFlow 2.0 (preview) TF2.0 NAS examples
• Use OrderedDict for LayerChoice
• Prettify the format of export
• Replace layer choice with selected module after applied fixed architecture

Model Compression Updates

• Model compression PyTorch 1.4 support

Training Service Updates

• update pai yaml merge logic
• support windows as remote machine in remote mode Remote Mode

Web UI new supports or improvements

• Show trial error message
• finalize homepage layout
• Refactor overview's best trials module
• Remove multiphase from webui
• add tooltip for trial concurrency in the overview page
• Show top trials for hyper-parameter graph

Bug Fix

• fix dev install
• SPOS example crash when the checkpoints do not have state_dict
• Fix table sort issue when experiment had failed trial
• Support multi python env (conda, pyenv etc)

New Features and Documentation

Hyper-Parameter Optimizing

• New tuner: Population Based Training (PBT)
• Trials can now report infinity and NaN as result

Neural Architecture Search

• New NAS algorithm: TextNAS
• ENAS and DARTS now support visualization through web UI.

Model Compression

• New Pruner: GradientRankFilterPruner
• Compressors will validate configuration by default
• Refactor: Adding optimizer as an input argument of pruner, for easy support of DataParallel and more efficient iterative pruning. This is a broken change for the usage of iterative pruning algorithms.
• Model compression examples are refactored and improved
• Added documentation for implementing compressing algorithm

Training Service

• Kubeflow now supports pytorchjob crd v1 (thanks external contributor @jiapinai)
• Experimental DLTS support

Overall Documentation Improvement

• Documentation is significantly improved on grammar, spelling, and wording (thanks external contributor @AHartNtkn)

Fixed Bugs

• ENAS cannot have more than one LSTM layers (thanks external contributor @marsggbo)
• NNI manager's timers will never unsubscribe (thanks external contributor @guilhermehn)
• NNI manager may exhaust head memory (thanks external contributor @Sundrops)
• Batch tuner does not support customized trials (#2075)
• Experiment cannot be killed if it failed on start (#2080)
• Non-number type metrics break web UI (#2278)
• A bug in lottery ticket pruner
• Other minor glitches