GitLab

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.

Add shell support for ssh connection, so that remote script can be started with user environment.

Minor fixes,

1. Fix gpu_metrics_collector to support pyenv. As pyenv will create one more process, so that original pgrep code always got extra processes, and cannot start gpu_metrics_collector.
2. Fix NASUI failure on dev-install-node-modules, to create subfolder every time.
3. Fix MakeFile to reduce mis-created links, and other minor issues.
4. Add node --watch for nni_manager for better dev experience.

* enable eslint

* remove tslint

* swith to eslint

* upgrade version of nni dependencies

* fix local and remote training services tslint

* fs's API changed

* fix version

* roll back utils

Unit test for nnimanager

* 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 base version for remote-log

* change launcher.py

* test

* basic version

* debug

* debug

* basic work version

* fix code

* update disable_log

* remove unused line

* add diable log in kubernetesTrainingService

* add detect frameworkcontroller

* fix comment

* update

* update

* fix kubernetesData

* debug

* debug

* debug

* fix comment

* fix conflict

* remove local temp files

* revert launcher.py

* update code by comments

* remove disableLog

* remove disable Log

* set timeout for cleanup

* fix code by comments

* update variable names

* add comments

* add delay function

* update

* update

* update by comments

* add  in remote script path

* rename variables

* update variable name

* add mkdir -p for subfolder

* Add UT code coverage report

* updates

* updates

* updates

* updates

* updates

* updates

* integration test python code coverage report

* fix bug

* add docs

* add ut

* add ut

* add to ci

* update doc

* update doc

* update ut

* add ut to ci

* add ut to ci

* add ut to ci

* add ut to ci

* add ut to ci

* add ut to ci

* add ut to ci

* add ut to ci

* test

* test

* test

* test

* test

* test

* test

* test

* test

* test

* revert

* refactor

* refactor

* s

* merge

* Dockerfile improvement

* Fix UT broken for kubeflow training service

rest api of kubernetes does not use base64 to encode chars, now use base64 to encode username and then create secret.

[Kubeflow training service] Use Kubernete API server to replace kubectl dependency

Support aks of kuberflow training service
Support nnictl set nniManagerIp

* update Makefile for mac support, wait for aka.ms support

* refix Makefile for colorful echo

* update Makefile with shorturl

* fix false fail on mac webui

* fix cross os remote tmpdir issue

* add readonly to RemoteMachineTrainingService.remoteOS

* fix var name for PR 386

* 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

* 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

* Improve Makefile

* Bugfix

* Add make target for remote machine workers

* Update travis config

* Debug travis

* Debug travis

* Debug travis

* Debug travis

* Debug travis

* Debug travis

* Debug travis

* Install bash completion script