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.