GitLab

update version number

* Add build_version input for build pipeline
* Change time stamp to UTC time

add package requirement data in setup.py

Update nni docker image build process in build pipeline

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.

Add azure pipeline to build and upload nni release

* add nni_gpu_tool in setup.py
* add exclude=['test'] in setup.py

release related doc/note modification for v0.5.1

fix indents of doc,
change nni to NNI
yaml to yml(file) and YAML(doc)

* fix some bugs in doc and log

* The learning rate focus more on validation sets accuracy than training sets accuracy.

* update Dockerfile and README

* Update README.md

Merge to branch v0.5

* Dockerfile improvement

* Fix UT broken for kubeflow training service

* update pypi/makefile for multiple platform support

* update linux os spec

* udpate doc for installation & pypi

* update readme

* Change version number from v0.3.4 to v0.4, for NNI v0.4 release

Remove "RUN python3 -m pip --no-cache-dir install torch torchvision"

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 local demo doc and configuration

* change folder name

* Update tutorial_1_CR_exp_local_api.md

no need to have a new training file

* Delete mnist_gpu.py

no need to have a new training file

* Update config_gpu.yml

no need to have a new training file

* add PyTorch to Dockerfile

* update doc for docker image

* update

* Fix pypi package missing python module

* Fix pypi package missing python module

* fix bug in smartparam example (#322)

* Fix nnictl update trialnum and document (#326)

1.Fix restful server of update
2.Update nnictl document of update
3.Add tensorboard in docement

* Update the version numbers from 0.3.2 to 0.3.3

1.Fix old version of config file
2.fix sklearn requirements
3.Fix resume log logic

* refactor Dockerfile

1.fix some bugs
2.support nnictl stop id, and add some regulars