GitLab

Fix pylint errors

* Refactor multiphase interface

* Implement multiphase on PAI

* update multiphase doc

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

There is one kind of version string like 'v0.5.2-gews11f', it is generated by installing from source code.
In current trialKeeper, use exact version match, and this version string will cause code break in msranni/nni image, because our offical image use clean number version.
Change the logic to fuzzy match, only match the main number of nni.

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

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.

* Fix a race condidtion issue in trial_keeper for reading log from pipe

[Logging architecture refactor] Remove unused metrics related code in nni trial_tools, support kubeflow mode for logging architecture refactor (#551)

* Remove unused metrics related code in nni trial_tools, support kubeflow mode for logging architecture refactor

* Removed unused log code, refactor to rename some class name in nni sdk and trial_tools

* Fix the regression bug that loca/remote mode doesnt work

* Update nnictl.py

Fix the issue that nnictl --version via pip installation doesn't work

* Update kubeflow training service document (#494)

* Remove kubectl related document, add messages for kubeconfig
* Add design section for kubeflow training service
* Move the image files for PAI training service doc into img folder.

* Update KubeflowMode.md (#498)

Update KubeflowMode.md, small terms change

* [V0.4.1 bug fix] Cannot run kubeflow training service due to trial_keeper change (#503)

* Update kubeflow training service document

* fix bug a that kubeflow trial job cannot run

* upgrade version number (#499)

* [V0.4.1 bug fix] Support read K8S config from KUBECONFIG environment variable (#507)

* Add KUBCONFIG env variable support

* In main.ts, throw cached error to make sure nnictl can show the error in stderr

* [PAI training service] codeDir files upload improvement

* Create full local temp folder

* Organize the folder structure for experiment and trial files

* [Trial keeper refactor] refactor trial keeper stdout output

* 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

* add exception handling in trial_keeper.py

* Fix trial keeper bug, use actual exitcode to exit rather than 1

* webui logpath and document (#135)

* Add webui document and logpath as a href

* fix tslint

* fix comments by Chengmin

* Pai training service bug fix and enhancement (#136)

* Add NNI installation scripts

* Update pai script, update NNI_out_dir

* Update NNI dir in nni sdk local.py

* Create .nni folder in nni sdk local.py

* Add check before creating .nni folder

* Fix typo for PAI_INSTALL_NNI_SHELL_FORMAT

* Improve annotation (#138)

* Improve annotation

* Minor bugfix

* Selectively install through pip (#139)

Selectively install through pip 
* update setup.py

* fix paiTrainingService bugs (#137)

* fix nnictl bug

* add hdfs host validation

* fix bugs

* fix dockerfile

* fix install.sh

* update install.sh

* fix dockerfile

* Set timeout for HDFSUtility exists function

* remove unused TODO

* fix sdk

* add optional for outputDir and dataDir

* refactor dockerfile.base

* Remove unused import in hdfsclientUtility

* Add documentation for NNI PAI mode experiment (#141)

* Add documentation for NNI PAI mode

* Fix typo based on PR comments

* Exit with subprocess return code of trial keeper

* Remove additional exit code

* Fix typo based on PR comments

* update doc for smac tuner (#140)

* Revert "Selectively install through pip (#139)" due to potential pip install issue (#142)

* Revert "Selectively install through pip (#139)"

This reverts commit 1d174836.

* Add exit code of subprocess for trial_keeper

* Update README, add link to PAImode doc

* Revert "Selectively install through pip (#139)"

This reverts commit 1d174836.

* Add exit code of subprocess for trial_keeper

* Update README, add link to PAImode doc

* 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.