Release 2.0 - 13/1/2021¶
Neural architecture search¶
Support hybrid training service (#3097 #3251 #3252)
Support AdlTrainingService, a new training service based on Kubernetes (#3022, thanks external contributors Petuum @pw2393)
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)
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)
Port markdown docs to reStructuredText docs and introduce
List related research and publications in doc (#3150)
Add tutorial of saving and loading quantized model (#3192)
Remove paiYarn doc and add description of
reuseconfig in remote mode (#3253)
Update EfficientNet doc to clarify repo versions (#3158, thanks external contributor @ahundt)
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)
_compute_hessianbug 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 (#3063 #3065 #3098 #3109 #3125 #3143 #3156 #3168 #3175 #3180 #3181 #3183 #3203 #3205 #3207 #3214 #3216 #3219 #3223 #3224 #3230 #3237 #3239 #3240 #3245 #3247 #3255 #3257 #3258 #3262 #3263 #3267 #3269 #3271 #3279 #3283 #3289 #3290 #3295)
Release 1.9 - 10/22/2020¶
Neural architecture search¶
Support regularized evolution algorithm for NAS scenario (#2802)
Add NASBench201 in search space zoo (#2766)
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)
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)
Refactor of the document for model compression (#2919)
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¶
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
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)
Neural architecture search¶
Search space zoo: ENAS and DARTS (#2589)
API to query intermediate results in NAS benchmark (#2728)
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)
TensorFlow v2 support in model compression (#2755)
Backward incompatible changes¶
Update the default experiment folder from
$HOME/nni-experiments. If you want to view the experiments created by previous NNI releases, you can move the experiments folders from
$HOME/nni-experimentsmanually. (#2686) (#2753)
Dropped support for Python 3.5 and scikit-learn 0.20 (#2778) (#2777) (2783) (#2787) (#2788) (#2790)
Upgrade TensorFlow version in Docker image (#2732) (#2735) (#2720)
Remove gpuNum in assessor examples (#2641)
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)
Fix mkdir error in training service (#2673)
Fix bug when using chmod in remote training service (#2689)
Fix dependency issue by making
_graph_utilsimported inline (#2675)
Fix mask issue in
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 - 7/8/2020¶
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.
Neural Architecture Search (NAS)¶
Improve Model Speedup: track more dependencies among layers and automatically resolve mask conflict, support the speedup of pruned resnet.
Added model sensitivity analysis tool to help users find the sensitivity of each layer to the pruning.
Update lottery ticket pruner to export winning ticket.
Support visualizing nested search space more friendly.
Show trial’s dict keys in hyper-parameter graph.
Enhancements to trial duration display.
Provide utility function to merge parameters received from NNI
Support setting paiStorageConfigName in pai mode
Fix bug for model graph with shared nn.Module
Fix nodejs OOM when
Fix NASUI bugs
Fix duration and intermediate results pictures update issue.
Fix minor WebUI table style issues.
Release 1.6 - 5/26/2020¶
New Features and improvement¶
Improve IPC limitation to 100W
improve code storage upload logic among trials in non-local platform
__version__for SDK version
support windows dev intall
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
Improve PBT on failure handling and support experiment resume for PBT
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
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)
Release 1.5 - 4/13/2020¶
New Features and Documentation¶
New tuner: Population Based Training (PBT)
Trials can now report infinity and NaN as result
Neural Architecture Search¶
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
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)
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
Release 1.4 - 2/19/2020¶
Neural Architecture Search¶
Support complete PAI configurations by allowing users to specify PAI config file path
Add example config yaml files for the new PAI mode (i.e., paiK8S)
Support deleting experiments using sshkey in remote mode (thanks external contributor @tyusr)
WebUI refactor: adopt fabric framework
Support running NNI experiment at foreground, i.e.,
Support canceling the trials in UNKNOWN state
Support large search space whose size could be up to 50mb (thanks external contributor @Sundrops)
Correctly support NaN in metric data, JSON compliant
Fix the out-of-range bug of
randinttype in search space
Fix the bug of wrong tensor device when exporting onnx model in model compression
Fix incorrect handling of nnimanagerIP in the new PAI mode (i.e., paiK8S)
Release 1.3 - 12/30/2019¶
Neural Architecture Search Algorithms Support¶
Single Path One Shot algorithm and the example using it
Model Compression Algorithms Support¶
NFS Support for PAI
Instead of using HDFS as default storage, since OpenPAI v0.11, OpenPAI can have NFS or AzureBlob or other storage as default storage. In this release, NNI extended the support for this recent change made by OpenPAI, and could integrate with OpenPAI v0.11 or later version with various default storage.
Kubeflow update adoption
Adopted the Kubeflow 0.7’s new supports for tf-operator.
Release 1.2 - 12/02/2019¶
Neural Architecture Search (NAS) on NNI
Support OpenPAI token authentication
For remote training service, trial jobs require no GPU are now scheduled with round-robin policy instead of random.
Pylint rules added to check pull requests, new pull requests need to comply with these pylint rules.
Web Portal & User Experience
Support user to add customized trial.
User can zoom out/in in detail graphs, except Hyper-parameter.
Improved NNI API documentation with more API docstring.
Fix the table sort issue when failed trials haven’t metrics. -Issue #1773
Maintain selected status(Maximal/Minimal) when the page switched. -PR#1710
Make hyper-parameters graph’s default metric yAxis more accurate. -PR#1736
Fix GPU script permission issue. -Issue #1665
Release 1.1 - 10/23/2019¶
New tuner: PPO Tuner
Tuners can now use dedicated GPU resource (see
gpuIndicesin tutorial for details)
Web UI improvements
Trials detail page can now list hyperparameters of each trial, as well as their start and end time (via “add column”)
Viewing huge experiment is now less laggy
Model compression toolkit - Alpha release: We are glad to announce the alpha release for model compression toolkit on top of NNI, it’s still in the experiment phase which might evolve based on usage feedback. We’d like to invite you to use, feedback and even contribute
Multiphase job hangs when search space exhuasted (issue #1204)
nnictlfails when log not available (issue #1548)
Release 1.0 - 9/2/2019¶
Tuners and Assessors
Support Auto-Feature generator & selection -Issue#877 -PR #1387
Provide auto feature interface
Tuner based on beam search
Add a parallel algorithm to improve the performance of TPE with large concurrency. -PR #1052
Support multiphase for hyperband -PR #1257
Support private docker registry -PR #755
Python wrapper for rest api, support retrieve the values of the metrics in a programmatic way PR #1318
New python API : get_experiment_id(), get_trial_id() -PR #1353 -Issue #1331 & -Issue#1368
Optimized NAS Searchspace -PR #1393
Unify NAS search space with _type – “mutable_type”e
Update random search tuner
Set gpuNum as optional -Issue #1365
Remove outputDir and dataDir configuration in PAI mode -Issue #1342
When creating a trial in Kubeflow mode, codeDir will no longer be copied to logDir -Issue #1224
Web Portal & User Experience
Show the best metric curve during search progress in WebUI -Issue #1218
Show the current number of parameters list in multiphase experiment -Issue1210 -PR #1348
Add “Intermediate count” option in AddColumn. -Issue #1210
Support search parameters value in WebUI -Issue #1208
Enable automatic scaling of axes for metric value in default metric graph -Issue #1360
Add a detailed documentation link to the nnictl command in the command prompt -Issue #1260
UX improvement for showing Error log -Issue #1173
Update the docs structure -Issue #1231
(deprecated) Multi phase document improvement -Issue #1233 -PR #1242
Add configuration example
WebUI description improvement -PR #1419
(Bug fix)Fix the broken links in 0.9 release -Issue #1236
(Bug fix)Script for auto-complete
(Bug fix)Fix pipeline issue that it only check exit code of last command in a script. -PR #1417
(Bug fix)quniform fors tuners -Issue #1377
(Bug fix)’quniform’ has different meaning beween GridSearch and other tuner. -Issue #1335
(Bug fix)”nnictl experiment list” give the status of a “RUNNING” experiment as “INITIALIZED” -PR #1388
(Bug fix)SMAC cannot be installed if nni is installed in dev mode -Issue #1376
(Bug fix)The filter button of the intermediate result cannot be clicked -Issue #1263
(Bug fix)API “/api/v1/nni/trial-jobs/xxx” doesn’t show a trial’s all parameters in multiphase experiment -Issue #1258
(Bug fix)Succeeded trial doesn’t have final result but webui show ×××(FINAL) -Issue #1207
(Bug fix)IT for nnictl stop -Issue #1298
(Bug fix)fix security warning
(Bug fix)Hyper-parameter page broken -Issue #1332
(Bug fix)Run flake8 tests to find Python syntax errors and undefined names -PR #1217
Release 0.9 - 7/1/2019¶
General NAS programming interface
oneshot-modefor NAS interface: PR #1201
(deprecated) Multiphase experiment supports
Added new training service support for multiphase experiment: PAI mode supports multiphase experiment since v0.9.
Added multiphase capability for the following builtin tuners:
TPE, Random Search, Anneal, Naïve Evolution, SMAC, Network Morphism, Metis Tuner.
nnictl experiment delete: delete one or all experiments, it includes log, result, environment information and cache. It uses to delete useless experiment result, or save disk space.
nnictl platform clean: It uses to clean up disk on a target platform. The provided YAML file includes the information of target platform, and it follows the same schema as the NNI configuration file.
Bug fix and other changes¶
Tuner Installation Improvements: add sklearn to nni dependencies.
(Bug Fix) Failed to connect to PAI http code - Issue #1076
(Bug Fix) Validate file name for PAI platform - Issue #1164
(Bug Fix) Update GMM evaluation in Metis Tuner
(Bug Fix) Hyper-parameter not shown correctly in WebUI when there is only one hyper parameter - Issue #1192
Release 0.8 - 6/4/2019¶
Support NNI on Windows for OpenPAI/Remote mode
NNI running on windows for remote mode
NNI running on windows for OpenPAI mode
Advanced features for using GPU
Run multiple trial jobs on the same GPU for local and remote mode
Run trial jobs on the GPU running non-NNI jobs
Kubeflow v1beta2 operator
Support Kubeflow TFJob/PyTorchJob v1beta2
Provide NAS programming interface for users to easily express their neural architecture search space through NNI annotation
Provide a new command
nnictl trial codegenfor debugging the NAS code
Tutorial of NAS programming interface, example of NAS on MNIST, customized random tuner for NAS
Support resume tuner/advisor’s state for experiment resume
For experiment resume, tuner/advisor will be resumed by replaying finished trial data
Improve the design of copying trial’s parameters
Support ‘randint’ type in hyper-parameter graph
Use should ComponentUpdate to avoid unnecessary render
Bug fix and other changes¶
Bug fix that
nnictl updatehas inconsistent command styles
Support import data for SMAC tuner
Bug fix that experiment state transition from ERROR back to RUNNING
Fix bug of table entries
Nested search space refinement
Refine ‘randint’ type and support lower bound
Release 0.7 - 4/29/2018¶
NNI running on windows for local mode
Support a new advisor BOHB, which is a robust and efficient hyperparameter tuning algorithm, combines the advantages of Bayesian optimization and Hyperband
Generate analysis results report after the experiment execution
Support import data to tuner and advisor for tuning
Specify GPU devices for NNI trial jobs by gpuIndices configuration, if gpuIndices is set in experiment configuration file, only the specified GPU devices are used for NNI trial jobs.
Web Portal enhancement
Decimal format of metrics other than default on the Web UI
Hints in WebUI about Multi-phase
Enable copy/paste for hyperparameters as python dict
Enable early stopped trials data for tuners.
NNICTL provide better error message
nnictl provide more meaningful error message for YAML file format error
Unable to kill all python threads after nnictl stop in async dispatcher mode
nnictl –version does not work with make dev-install
All trail jobs status stays on ‘waiting’ for long time on OpenPAI platform
Release 0.6 - 4/2/2019¶
check whether the version is consistent between nniManager and trialKeeper
If includeIntermediateResults is true, the last intermediate result of the trial that is early stopped by assessor is sent to tuner as final result. The default value of includeIntermediateResults is false.
Adds two pipes to separate message receiving channels for tuner and assessor.
Make log collection feature configurable
Add intermediate result graph for all trials
Fix the bug that doesn’t show any result if metrics is dict
Fix the number calculation issue for float types in hyperband
Fix a bug in the search space conversion in SMAC tuner
Fix the WebUI issue when parsing experiment.json with illegal format
Fix cold start issue in Metis Tuner
Release 0.5.2 - 3/4/2019¶
Curve fitting assessor performance improvement.
Chinese version document: https://nni.readthedocs.io/zh/latest/
Debuggability/serviceability document: https://nni.readthedocs.io/en/latest/Tutorial/HowToDebug.html
Tuner assessor reference: https://nni.readthedocs.io/en/latest/sdk_reference.html
Bug Fixes and Other Changes¶
Fix a race condition bug that does not store trial job cancel status correctly.
Fix search space parsing error when using SMAC tuner.
Fix cifar10 example broken pipe issue.
Add unit test cases for nnimanager and local training service.
Add integration test azure pipelines for remote machine, OpenPAI and kubeflow training services.
Support Pylon in OpenPAI webhdfs client.
Release 0.5.1 - 1/31/2018¶
Reorganized documentation & New Homepage Released: https://nni.readthedocs.io/en/latest/
Bug Fixes and Other Changes¶
Fix the bug of installation in python virtualenv, and refactor the installation logic
Fix the bug of HDFS access failure on OpenPAI mode after OpenPAI is upgraded.
Fix the bug that sometimes in-place flushed stdout makes experiment crash
Release 0.5.0 - 01/14/2019¶
New tuner and assessor supports¶
Support Metis tuner as a new NNI tuner. Metis algorithm has been proofed to be well performed for online hyper-parameter tuning.
Support ENAS customized tuner, a tuner contributed by github community user, is an algorithm for neural network search, it could learn neural network architecture via reinforcement learning and serve a better performance than NAS.
Support Curve fitting assessor for early stop policy using learning curve extrapolation.
Advanced Support of Weight Sharing: Enable weight sharing for NAS tuners, currently through NFS.
Training Service Enhancement¶
FrameworkController Training service: Support run experiments using frameworkcontroller on kubernetes
FrameworkController is a Controller on kubernetes that is general enough to run (distributed) jobs with various machine learning frameworks, such as tensorflow, pytorch, MXNet.
NNI provides unified and simple specification for job definition.
MNIST example for how to use FrameworkController.
User Experience improvements¶
A better trial logging support for NNI experiments in OpenPAI, Kubeflow and FrameworkController mode:
An improved logging architecture to send stdout/stderr of trials to NNI manager via Http post. NNI manager will store trial’s stdout/stderr messages in local log file.
Show the link for trial log file on WebUI.
Support to show final result’s all key-value pairs.
Release 0.4.1 - 12/14/2018¶
Training Service improvements¶
Pytorch-operator support for Kubeflow training service
Improvement on local code files uploading to OpenPAI HDFS
Fixed OpenPAI integration WebUI bug: WebUI doesn’t show latest trial job status, which is caused by OpenPAI token expiration
Show version information both in nnictl and WebUI. You can run nnictl -v to show your current installed NNI version
Enable modify concurrency number during experiment
Add feedback link to NNI github ‘create issue’ page
Enable customize top 10 trials regarding to metric numbers (largest or smallest)
Enable download logs for dispatcher & nnimanager
Enable automatic scaling of axes for metric number
Update annotation to support displaying real choice in searchspace
Release 0.4 - 12/6/2018¶
Distributed trial example on Kubeflow
Support launch NNI experiment on MAC
UI support for hyperband tuner
Remove tensorboard button
Show experiment error message
Show line numbers in search space and trial profile
Support search a specific trial by trial number
Show trial’s hdfsLogPath
Download experiment parameters
Docker file update, add pytorch library
Refactor ‘nnictl stop’ process, send SIGTERM to nni manager process, rather than calling stop Rest API.
OpenPAI training service bug fix
Support NNI Manager IP configuration(nniManagerIp) in OpenPAI cluster config file, to fix the issue that user’s machine has no eth0 device
File number in codeDir is capped to 1000 now, to avoid user mistakenly fill root dir for codeDir
Don’t print useless ‘metrics is empty’ log in OpenPAI job’s stdout. Only print useful message once new metrics are recorded, to reduce confusion when user checks OpenPAI trial’s output for debugging purpose
Add timestamp at the beginning of each log entry in trial keeper.
Release 0.3.0 - 11/2/2018¶
NNICTL new features and updates¶
Support running multiple experiments simultaneously.
Before v0.3, NNI only supports running single experiment once a time. After this release, users are able to run multiple experiments simultaneously. Each experiment will require a unique port, the 1st experiment will be set to the default port as previous versions. You can specify a unique port for the rest experiments as below:
nnictl create --port 8081 --config <config file path>
Support updating max trial number. use
nnictl update --helpto learn more. Or refer to NNICTL Spec for the fully usage of NNICTL.
API new features and updates¶
**breaking change**: nn.get_parameters() is refactored to nni.get_next_parameter. All examples of prior releases can not run on v0.3, please clone nni repo to get new examples. If you had applied NNI to your own codes, please update the API accordingly.
New API nni.get_sequence_id(). Each trial job is allocated a unique sequence number, which can be retrieved by nni.get_sequence_id() API.
git clone -b v0.3 https://github.com/microsoft/nni.git
nni.report_final_result(result) API supports more data types for result parameter.
It can be of following types:
A python dict containing ‘default’ key, the value of ‘default’ key should be of type int or float. The dict can contain any other key value pairs.
New tuner support¶
Batch Tuner which iterates all parameter combination, can be used to submit batch trial jobs.
Release 0.2.0 - 9/29/2018¶
Support training services on pai mode. NNI trials will be scheduled to run on OpenPAI cluster
NNI trial’s output (including logs and model file) will be copied to OpenPAI HDFS for further debugging and checking
Support NNI installation on conda and python virtual environment
Update ga squad example and related documentation
WebUI UX small enhancement and bug fix
Release 0.1.0 - 9/10/2018 (initial release)¶
Initial release of Neural Network Intelligence (NNI).
Installation and Deployment
Support pip install and source codes install
Support training services on local mode(including Multi-GPU mode) as well as multi-machines mode
Tuners, Assessors and Trial
Support AutoML algorithms including: hyperopt_tpe, hyperopt_annealing, hyperopt_random, and evolution_tuner
Support assessor(early stop) algorithms including: medianstop algorithm
Provide Python API for user defined tuners and assessors
Provide Python API for user to wrap trial code as NNI deployable codes
Provide a command line toolkit ‘nnictl’ for experiments management
Provide a WebUI for viewing experiments details and managing experiments
Support CI by providing out-of-box integration with travis-ci on ubuntu
Support simple GPU job scheduling