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* skeleton of dlts training service (#1844)

* Hello, DLTS!

* Revert version

* Remove fs-extra

* Add some default cluster config

* schema

* fix

* Optional cluster (default to `.default`)

Depends on DLWorkspace#837

* fix

* fix

* optimize gpu type

* No more copy

* Format

* Code clean up

* Issue fix

* Add optional fields in config

* Issue fix

* Lint

* Lint

* Validate email, password and team

* Doc

* Doc fix

* Set TMPDIR

* Use metadata instead of gpu_capacity

* Cancel paused DLTS job

* workaround lint rules

* pylint

* doc
Co-authored-by: QuanluZhang <z.quanluzhang@gmail.com>

* enable eslint

* remove tslint

* update eslint rules
* auto fix eslint
* manually fix eslint (#1833)

* Refactor multiphase interface

* Implement multiphase on PAI

* update multiphase doc

* fix local and remote training services tslint

* Kubeflow TrainingService support, v1 (#373)

1. Create new Training Service: kubeflow trainning service, use 'kubectl' and kubeflow tfjobs CRD to submit and manage jobs
2. Update nni python SDK to support new kubeflow platform
3. Update nni python SDK's get_sequende_id() implementation, read NNI_TRIAL_SEQ_ID env variable, instead of reading .nni/sequence_id file
4. This version only supports Tensorflow operator. Will add more operators' support in future versions

* Change base image from devel to runtime, to reduce docker image size

* Support running multiple experiment for PAI

* Fix a bug regarding to recuisively reference between paiRestServer and
paiTrainingService

* PAI Training service implementation
**1. Implement PAITrainingService
**2. Add trial-keeper python module, and modify setup.py to install the module
**3. Add PAItrainingService rest server to collect metrics from PAI container.

* Merge latest code changes into Github Master

* temporary modification for travis

* temporary modification for travis