GitLab

* Refactoring local training service
* Designated GPU for local training service
* RemoteMachine designated GPU configuration

If user set remoteloggingType in config file, log content will not be transmitted from trialkeeper

check nni version in trialkeeper, to make sure the version of trialkeeper is consistent with trainingService
add a debug mode in config file

Add frameworkcontroller training service based on kubeflow training service.
Refactor code structure, add kubernetes training service as father class, and set kubeflow training service and frameworkcontroller training service as child class.

Add nniManager Ip in nnictl, pai TrainingService and kubeflow TrainingService.
If users set nniManagerIp, pai and kubeflow will use this ip instead of using getIPV4() function.
Web UI will also use this nniManagerIp.

* 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

* Updated based on comments

* Fix bug, make get_parameters() idompotent

* Add idompotent support for get_parameters() in LocalTrainingService

* 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.