Release 1.4 - 2/19/2020

Major Features

Neural Architecture Search

• Support C-DARTS algorithm and add the example using it
• Support a preliminary version of ProxylessNAS and the corresponding example
• Add unit tests for the NAS framework

Model Compression

• Support DataParallel for compressing models, and provide an example of using DataParallel
• Support model speedup for compressed models, in Alpha version

Training Service

• 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

• WebUI refactor: adopt fabric framework

Others

• Support running NNI experiment at foreground, i.e., --foreground argument in nnictl create/resume/view
• Support canceling the trials in UNKNOWN state
• Support large search space whose size could be up to 50mb (thanks external contributor @Sundrops)

Documentation

• Improve the index structure of NNI readthedocs
• Improve documentation for NAS
• Improve documentation for the new PAI mode
• Add QuickStart guidance for NAS and model compression
• Improve documentation for the supported EfficientNet

Bug Fixes

• Correctly support NaN in metric data, JSON compliant
• Fix the out-of-range bug of randint type 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

Major Features

Neural Architecture Search Algorithms Support

• Single Path One Shot algorithm and the example using it

Model Compression Algorithms Support

• Knowledge Distillation algorithm and the example using itExample
• Pruners
  • L2Filter Pruner
  • ActivationAPoZRankFilterPruner
  • ActivationMeanRankFilterPruner
• BNN Quantizer

Training Service

• 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.

Engineering (code and build automation)

• Enforced ESLint on static code analysis.

Small changes & Bug Fixes

• correctly recognize builtin tuner and customized tuner
• logging in dispatcher base
• fix the bug where tuner/assessor's failure sometimes kills the experiment.
• Fix local system as remote machine issue
• de-duplicate trial configuration in smac tuner ticket

Release 1.2 - 12/2/2019

Major Features

• Feature Engineering
  • New feature engineering interface
  • Feature selection algorithms: Gradient feature selector & GBDT selector
  • Examples for feature engineering
• Neural Architecture Search (NAS) on NNI
  • New NAS interface
  • NAS algorithms: ENAS, DARTS, P-DARTS (in PyTorch)
  • NAS in classic mode (each trial runs independently)
• Model compression
  • New model pruning algorithms: lottery ticket pruning approach, L1Filter pruner, Slim pruner, FPGM pruner
  • New model quantization algorithms: QAT quantizer, DoReFa quantizer
  • Support the API for exporting compressed model.
• Training Service
  • Support OpenPAI token authentication
• Examples:
  • An example to automatically tune rocksdb configuration with NNI.
  • A new MNIST trial example supports tensorflow 2.0.
• Engineering Improvements
  • 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.
• Documentation
  • Improved NNI API documentation with more API docstring.

Bug fix

• Fix the table sort issue when failed trials haven't metrics. -Issue #1764
• 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

Major Features

• New tuner: PPO Tuner
• View stopped experiments
• Tuners can now use dedicated GPU resource (see gpuIndices in 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
• More examples
  • EfficientNet PyTorch example
  • Cifar10 NAS example
• 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

Fixed Bugs

• Multiphase job hangs when search space exhuasted (issue #1204)
• nnictl fails when log not available (issue #1548)

Release 1.0 - 29/8/2019

Major Features

• Tuners and Assessors

  • Support Auto-Feature generator & selection -Issue#877 -PR #1387
    • Provide auto feature interface
    • Tuner based on beam search
    • Add Pakdd example
  • Add a parallel algorithm to improve the performance of TPE with large concurrency. -PR #1052
  • Support multiphase for hyperband -PR #1257

• Training Service

  • Support private docker registry -PR #755

• Engineering Improvements

  • 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

• Documentation

  • Update the docs structure -Issue #1231
  • Multi phase document improvement -Issue #1233 -PR #1242
    • Add configuration example
  • WebUI description improvement -PR #1419

Bug fix

• (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

Major Features

• General NAS programming interface

  • Add enas-mode and oneshot-mode for NAS interface: PR #1201

• Gaussian Process Tuner with Matern kernel

• 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.

  For details, please refer to Write a tuner that leverages multi-phase

• Web Portal

  • Enable trial comparation in Web Portal. For details, refer to View trials status
  • Allow users to adjust rendering interval of Web Portal. For details, refer to View Summary Page
  • show intermediate results more friendly. For details, refer to View trials status

• Commandline Interface

  • 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) Negative time number rendering in Web Portal - Issue #1182, Issue #1185
• (Bug Fix) Hyper-parameter not shown correctly in WebUI when there is only one hyper parameter - Issue #1192

Release 0.8 - 6/4/2019

Major Features

• Support NNI on Windows for PAI/Remote mode

  • NNI running on windows for remote mode

  • NNI running on windows for PAI 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

• General NAS programming interface

  • Provide NAS programming interface for users to easily express their neural architecture search space through NNI annotation

  • Provide a new command nnictl trial codegen for 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

• Web Portal

  • 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 update has 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

• Comparison of different hyper-parameter tuning algorithm

• Comparison of NAS algorithm

• NNI practice on Recommenders

Release 0.7 - 4/29/2018

Major Features

• Support NNI on Windows
  • NNI running on windows for local mode
• New advisor: BOHB
  • Support a new advisor BOHB, which is a robust and efficient hyperparameter tuning algorithm, combines the advantages of Bayesian optimization and Hyperband
• Support import and export experiment data through nnictl
  • Generate analysis results report after the experiment execution
  • Support import data to tuner and advisor for tuning
• Designated gpu devices for NNI trial jobs
  • 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

Bug fix

• Unable to kill all python threads after nnictl stop in async dispatcher mode
• nnictl --version does not work with make dev-instal
• All trail jobs status stays on 'waiting' for long time on PAI platform

Release 0.6 - 4/2/2019

Major Features

• Version checking
  • check whether the version is consistent between nniManager and trialKeeper
• Report final metrics for early stop job
  • 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.
• Separate Tuner/Assessor
  • Adds two pipes to separate message receiving channels for tuner and assessor.
• Make log collection feature configurable
• Add intermediate result graph for all trials

Bug fix

• Add shmMB config key for PAI
• 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 0.5.2.1 - 3/4/2019

• Add release note.
• Fix Metis tuner cold start issue.

Release 0.5.1 - 1/31/2018

Improvements

• Making log directory configurable
• Support different levels of logs, making it easier for debugging

Documentation

• Reorganized documentation & New Homepage Released: https://nni.readthedocs.io/en/latest/
• Chinese users are able to learn NNI with the translated Chinese doc: https://github.com/Microsoft/nni/blob/master/zh_CN/README.md.
  Dear Contributors: We'd love to provide more language translations, contribute to NNI with more languages =)

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 PAI mode after PAI is upgraded.
• Fix the bug that sometimes in-place flushed stdout makes experiment crash

Release 0.5.0 - 01/15/2019

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 PAI, 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.

Major Features

New tuner support

• Support network morphism

Training Service Improvements

• Migrate Kubeflow training service's dependency from kubectl CLI to Kubernetes API client
• Pytorch-operator support for Kubeflow training service.
• Improvement on local code files uploading to OpenPAI HDFS.

WebUI improvements

• 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

New example

• FashionMnist, work together with network morphism tuner.
• Distributed MNIST example written in PyTorch.

Others

• Show version information both in nnictl and WebUI. You can run nnictl -v to show your current installed NNI version.
• Bug Fix
  • fix the bug that WebUI doesn't show latest trial job status, which is caused by OpenPAI token expiration.
  • Update annotation to support displaying real choice in searchspace

Major Features

New Platform Support

• Support launch NNI experiment on MAC

New Tuner Support

• Grid search tuner
• Hyperband tuner

New Training Service Support

• Kubeflow Training service
  • Support tf-operator
  • Distributed trial example on Kubeflow

OpenPAI Training Service Improvements

*  Support NNI Manager IP configuration(nniManagerIp) in PAI 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 int PAI job’s stdout. Only print useful message once new metrics are recorded, to reduce confusion when user checks PAI trial’s output for debugging purpose
*  Add timestamp at the beginning of each log entry in trial keeper.
*  Show trial's hdfsLogPath

WebUI Improvements and updates

*  Download experiment parameters
*  UI support for hyperband tuner
*  Show experiment error message 
*  Show line numbers in search space and trial profile
*  Support search a specific trial by trial number
*  Remove tensorboard button 

Others

• Asynchronous dispatcher
• Docker file update: added pytorch library
• Refactor 'nnictl stop' process, send SIGTERM to nni manager process, rather than calling stop Rest API

Release 0.3.4 - 11/8/2018

• Updated several examples
• Fix the bug that Medianstop assessor does not work

Release 0.3.2 - 11/2/2018

Major Features

• Support running multiple experiments simultaneously. You can run multiple experiments by specifying a unique port for each experiment:

  nnictl create --port 8081 --config <config file path>

  You can still run the first experiment without '--port' parameter:

  nnictl create --config <config file path>

• A builtin Batch Tuner which iterates all parameter combination, can be used to submit batch trial jobs.

• nni.report_final_result(result) API supports more data types for result parameter, it can be of following types:

  • int
  • float
  • 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.

• Continuous Integration

  • Switched to Azure pipelines

• Others

  • New nni.get_sequence_id() API. Each trial job is allocated a unique sequence number, which can be retrieved by nni.get_sequence_id() API.

  • Download experiment result from WebUI

  • Add trial examples using sklearn and NNI together

  • Support updating max trial number

  • Kaggle competition TGS Salt code as an example

  • NNI Docker image:

    docker pull msranni/nni:latest

Breaking changes

• API nn.get_parameters() is renamed to nni.get_next_parameter(), this is a broken change, all examples of prior releases can not run on v0.3.2, please clone nni repo to get new examples.

  git clone -b v0.3.2 https://github.com/Microsoft/nni.git

Know issues

Known Issues in release 0.3.2.