Nni

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.

Improvements
* Making [log directory](https://github.com/Microsoft/nni/blob/v0.5.1/docs/ExperimentConfig.md) configurable
* Support [different levels of logs](https://github.com/Microsoft/nni/blob/v0.5.1/docs/ExperimentConfig.md), 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

New tuner and assessor supports
* Support [Metis tuner](docs/HowToChooseTuner.mdMetisTuner) as a new NNI tuner. Metis algorithm has been proofed to be well performed for **online** hyper-parameter tuning.
* Support [ENAS customized tuner](https://github.com/countif/enas_nni), 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](docs/HowToChooseTuner.mdCurvefitting) for early stop policy using learning curve extrapolation.
* Advanced Support of [Weight Sharing](docs/AdvancedNAS.md): Enable weight sharing for NAS tuners, currently through NFS.

Training Service Enhancement
* [FrameworkController Training service](docs/FrameworkControllerMode.md): 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](https://github.com/Microsoft/nni/tree/master/src/sdk/pynni/nni/networkmorphism_tuner)

Training Service Improvements
* Migrate [Kubeflow training service](https://github.com/microsoft/nni/blob/master/docs/en_US/TrainingService/KubeflowMode.md)'s dependency from kubectl CLI to [Kubernetes API](https://kubernetes.io/docs/concepts/overview/kubernetes-api/) client
* [Pytorch-operator](https://github.com/kubeflow/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](https://github.com/Microsoft/nni/tree/master/examples/trials/network_morphism), work together with network morphism tuner.
* [Distributed MNIST example](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-distributed-pytorch) 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](https://github.com/Microsoft/nni/blob/master/src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py)
* [Hyperband tuner](https://github.com/Microsoft/nni/tree/master/src/sdk/pynni/nni/hyperband_advisor)

New Training Service Support
* [Kubeflow Training service](https://github.com/microsoft/nni/blob/master/docs/en_US/TrainingService/KubeflowMode.md)
* Support tf-operator
* [Distributed trial example](https://github.com/Microsoft/nni/blob/master/examples/trials/mnist-distributed/dist_mnist.py) 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