Nni

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

* 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 ...`

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
* <span style="color:red">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.</span>

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

Know issues
[Known Issues in release 0.3.2](https://github.com/Microsoft/nni/labels/nni030knownissues).

Major Features
* Support for [OpenPAI](https://github.com/Microsoft/pai) (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](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) tuner
* [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) 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](https://github.com/automl/SMAC3)
* Support NNI installation on [conda](https://conda.io/docs/index.html) 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](https://github.com/Microsoft/nni/labels/nni020knownissues).