Release 0.5.2 - 3/4/2019

Improvements

• Curve fitting assessor performance improvement.

Documentation

• 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#tuner

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, PAI and kubeflow training services.
• Support Pylon in PAI webhdfs client.

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/README_zh_CN.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.

Release 0.3.3 - 11/5/2018

• Fix tuner path in ga_squad example
• Fix the bug induced by changed nni APIs in mnist_smarparam example
• Fix bug in command nnictl update ...

Release 0.2.0 - 9/29/2018

Major Features

• Support for OpenPAI (aka pai) Training Service
  • 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 SMAC tuner
  • SMAC is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO to handle categorical parameters. The SMAC supported by NNI is a wrapper on SMAC3
• Support NNI installation on conda and python virtual environment
• Others
  • Update ga squad example and related documentation
  • WebUI UX small enhancement and bug fix

Known Issues

Known Issues in release 0.2.0.

Release 0.1.0 - 9/10/2018 (initial release)

Initial release of Neural Network Intelligence (NNI).

Major Features

• 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, Accessors 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 accessors
  • Provide Python API for user to wrap trial code as NNI deployable codes
• Experiments
  • Provide a command line toolkit 'nnictl' for experiments management
  • Provide a web UI for viewing experiments details and managing experiments
• Continuous Integration
  • Support CI by providing out-of-box integration with travis-ci on ubuntu
• Others
  • Support simple GPU job scheduling

Known Issues

Known Issues in release 0.1.0.