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Designed new interface to support reusable training service, currently only applies to OpenPAI, and default disabled.

Replace trial_keeper.py to trial_runner.py, trial_runner holds an environment, and receives commands from nni manager to run or stop an trial, and return events to nni manager.
Add trial dispatcher, which inherits from original trianing service interface. It uses to share as many as possible code of all training service, and isolate with training services.
Add EnvironmentService interface to manage environment, including start/stop an environment, refresh status of environments.
Add command channel on both nni manager and trial runner parts, it supports different ways to pass messages between them. Current supported channels are file, web sockets. and supported commands from nni manager are start, kill trial, send new parameters; from runner are initialized(support some channel doesn't know which runner connected), trial end, stdout ((new type), including metric like before), version check (new type), gpu info (new type).
Add storage service to wrapper a storage to standard file operations, like NFS, azure storage and so on.
Partial support run multiple trials in parallel on runner side, but not supported by trial dispatcher side.
Other minor changes,

Add log_level to TS UT, so that UT can show debug level log.
Expose platform to start info.
Add RouterTrainingService to keep origianl OpenPAI training service, and support dynamic IOC binding.
Add more GPU info for future usage, including GPU mem total/free/used, gpu type.
Make some license information consistence.
Fix async/await problems on Array.forEach, this method doesn't support async actually.
Fix IT errors on download data, which causes by my #2484 .
Accelerate some run loop pattern by reducing sleep seconds.

* check pylint for nni_cmd

* fix id error

* init

* remove comments
Co-authored-by: SparkSnail <Administrator@MININT-27KBA7M.fareast.corp.microsoft.com>

remove `--user` to fix DLTS testing.

* update sklearn version

* updates

* update sklean version

* updates

* updates

* updates

* Refine gitignore

* add newline at the end

* add more environments

* Add example for EfficientNet and upgrade PyTorch in Dockerfile

* squash commits in v1.0 first round bug bash

* fix remote bug

* add document

* add document

* update

* update

* update

* update

* fix remote issue

* fix forEach

* update doc according to comments

* update

* update

* update

* remove 'any more'

* add sklearn in setup.py

* remove sklearn in doc for MetisTuner

* test python

* test python36

* debug python

* debug python

* debug

* python version

* test python

* debug

* install nni

* install nni

* test powershell

* debug python

* test

* test python

* use python

* test python

* test python

* test

* update

* test powershell

* debug python

* debug python

* debug python

* debug powershell

* debug

* debug

* debug install.ps1

* add continueOnError: true

* debug

* debug

* update

* update

* add unittest

* test node

* update

* update joi

* debug joi

* add joi

* debug joi

* Update install

* update

* update

* add unittest

* add convert command

* add example

* fix windows commands

* debug

* fix tensorflow version

* fix pipeline

* update

* add gpu logic in windows

* update

* update

* debug

* fix commands

* fix commands

* update

* update

* Fix comments

* update...

* Update readme

Fix windows pypi pipeline and add win32 version

update version number

* Add build_version input for build pipeline
* Change time stamp to UTC time

add package requirement data in setup.py

Update nni docker image build process in build pipeline

trial_keeper use 50070 port to connect to webhdfs server, and PAI use a mapping method to map 50070 port to 5070 port to visit restful server, this method has some risk for PAI may not support this kind of mapping in later release.Now use Pylon path(/webhdfs/api/v1) instead of 50070 port in webhdfs client of trial_keeper, the path is transmitted in trainingService.
In this pr, we have these changes:

1. Change to use webhdfs path instead of 50070 port in hdfs client.
2. Change to use new hdfs package "PythonWebHDFS", which is build to support pylon by myself. You could test the new function from "sparksnail/nni:dev-pai" image to test pai trainingService.
3. Update some variables' name according to comments.