GitLab

[Kubeflow training service] Update kubeflow exp job config schema to support distributed training (#387)

* Support distributed training on tf-operator, for worker and ps

* Update validation rule for kubeflow config

* small code refactor adjustment for private methods

* Use different output folder for ps and worker

* Asynchronous dispatcher

* updates

* updates

* updates

* updates

* 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

Fix "nnictl stop"

* add gridsearch tuner

* add gridsearchtuner

* add gridsearchtuner

* add gridsearchtuner

* update gridsearch tuner

* update gridsearch tuner

* update gridsearch tuner

* update gridsearch tuner

* update gridsearch tuner

* update gridsearch tuner

* update gridsearch tuner

* update gridsearch and pylint

1.Set scikit-learn==0.20.0 in Dockerfile
2.Update readme.md of dockerile
3.Add PyTorch 0.4.1
4.Add description for 'nnictl stop all'

* update makefile

* update launcher.py to fix the problem of finding main.js

* remove duplicated lib

* Update nnictl.py

* modify help message for nnictl stop

Fix nnictl stop logic
Fix localTrainingService cancelJob logic
Show port information in "nnictl experiment list" cmd.
Show more information when config file validate failed.
Add nnictl detect adjacent port logic if the platform is pai