GitLab

* strengthen UT and fix hyperopt randint

* fix a ridiculous mistake

* add pruner unit test
* modify pruners compatible with torch0.4.1

* unit test for fpgm pruner

* fpgm pruner pytorch and tensorflow 2.0 implementation

api refactor for compression, especially, quantization APIs

* Add comprehensive tests for tuners (#1570)

* refactor

* fix compressor op_types

* fix builtin pruners bug

* use type_as

* fix pruner bugs and add model compression README

* fix example bugs

* add AutoCompression.md and remove sensitive pruner

* fix tf pruner bugs

* update overview

* Pruner.md

* Resolve comments in PR 1571

* try to pass ut

* fix typo

* format doc-string

* use tensorflow.compat.v1

* Revert "use tensorflow.compat.v1"

This reverts commit 97a4ed923677c6dfd545fd654c55c424cf490a19.

* [Proposal] demo compressor (#1402)

model compression

* update doc for model compression (#1509)

* Update Overview.md

* Change Doc (#1510)

* refactor compression sdk (#1562)

* refactor compression sdk

* bugfix

* bugfix

* update ut

* Sync model compression doc and implementation (#1575)

* update doc

* formatting

* bugfix

* add import to examples

Add example for customized advisor and some refactoring

* Refactor `receive_customized_trial_result` API

* add doc

* update

* dev-tf-master

* fix bugs

* fix bugs

* remove unnecessary lines

* dev oneshot and darts

* dev nas

* dev enas and oneshot

* dev enas and oneshot

* dev enas and oneshot

* dev oneshot and enas

* dev oneshot

* add ut

* add docstring

* add docstring

* fix

* resolve comments by changing docstring

* resolve comments

* Refactor multiphase interface

* Implement multiphase on PAI

* update multiphase doc

* add different tuner config files for config_test

* change MetisTuner config test due to no lightgbm python module in integration test

* install smac package in azure-pipelines

* SMAC need swig to be installed

* Try to install swig from source code

* remove SMAC test because the dependency can not be installed

* use sudo to install the swig

* sleep 10s to make sure the port has been released

* remove tuner test for networkmorphism because it uses more than 30s to release the tcp port

* word "down" to "done"

* add config test for Curvefitting assessor

* change file name

* Fix data type not match bug

* Optimize MetisTunner

* pretty the code

* Follow the review comment

* add exploration probability

* Avoid None type object generating

* fix nnictl log trial bug

* rollback chinese doc

* add argument 'experiment' to parser_log_trial and parser_trial_kill

* update doc

* add NASComparison for ResearchBlog

* Fix format of table

* update doc and add index to toctree

* Update NASComparison.md

Slightly change.

* Update NASComparison.md

Add http links in toctree

* Move ResearchBlog to bottom

move ResearchBlog to bottom

* Follow the review comments

* change the file structure

* add utils

* slight change

* Remove unrelated files

* add doc in SearchSpaceSpec.md and add config test for nested search space

* add unittest

* add unittest for hyperopt_tuner

* update as comment

* Update SearchSpaceSepc doc

* Delete unnecessary space change and correct a mistake

create utils.py, put extract_scalar_reward to it and remove others

1. Route tuner and assessor commands to 2 seperate queues  issue #841
2. Allow tuner to leverage intermediate result when trial is early stopped.  issue #843

* fix annotation, resolve annotation's key err bug, refactor the design

* 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

* fix-annotation

* fix annotation for error printing

* update annotation

* update annotation

* update annotation

* update annotation

* update unittest for annotation

* update unit test

* update annotation

* update annotation

* update annotation

* update ut

* update ut

* Quick fix nnictl config logic (#289)

* fix nnictl bug

* fix install.sh

* add desc for Dockerfile.build.base

* update document for Dockerfile

* update

* refactor port detect

* update

* refactor NNICTLDOC.md

* add document for pai and nnictl

* add default value for port

* add exception handling in trial_keeper.py

* fix port bug

* fix resume

* fix nnictl resume and fix nnictl stop

* fix document

* update

* refactor nnictl

* update

* update doc

* update

* update nnictl

* fix comment

* revert dockerfile

* update

* update

* update

* fix nnictl error hit

* fix comments

* fix bash-completion

* fix paramiko install

* quick fix resume logic

* update

* quick fix nnictl

* PR merge to 0.3 (#297)

* refactor doc

* update with Mao's suggestions

* Set theme jekyll-theme-dinky

* update doc

* fix links

* fix links

* fix links

* merge

* fix links and doc errors

* merge

* merge

* merge

* merge

* Update README.md (#288)

added License badge

* merge

* updated the "Contribute" part (merged Gems' wiki in, updated ReadMe)

* fix link

* fix doc mistakes and broken links. (#271)

* refactor doc

* update with Mao's suggestions

* Set theme jekyll-theme-dinky

* updated the "Contribute" part (merged Gems' wiki in, updated ReadMe)

* fix link

* Update README.md

* Fix misspelling in examples/trials/ga_squad/README.md

* revise the installation cmd to v0.2

* revise to install v0.2

* remove enas readme (#292)

* Fix datastore performance issue (#301)

* Fix nnictl in v0.3 (#299)

Fix old version of config file
fix sklearn requirements
Fix resume log logic

* add basic tuner and trial for network morphism

* Complete basic receive_trial_result() and generate_parameters(). Use onnx  as the intermediate representation ( But it cannot convert to pytorch model )

* add tensorflow cifar10 for network morphism

* add unit test for tuner and its function

* use temporary torch_model

* fix request bug and program can communicate nni

* add basic pickle support for graph and train successful in pytorch

* Update unittest for networkmorphism_tuner

* Network Morphism add multi-gpu trial training support

* Format code with black tool

* change intermediate representation from pickle file to json we defined

* successfully pass the unittest for test_graph_json_transform

* add README for network morphism and it works fine in both Pytorch and Keras.

* separate the original Readme.md in network-morphism into two parts (tuner and trial)

* change the openpai image path

* beautify the file structure of network_morphism and add a fashion_mnist keras example

* pretty the source and add some docstring for funtion in order to pass the pylint.

* remove unused module import and add some docstring

* add some details for the application scenario Network Morphism Tuner

* follow the advice and modify the doc file

* add the config file for each task in the examples trial of network morphism

* change default python interpreter from python to python3

* fix sdk's unittest and add medianstop, batchtuner to ci

* fix sdk's unittest and add medianstop, batchtuner to ci

* remove debug info

* update azure-pipelines

* remove useless code

* add some checks

* fix pylint

* update ci test

* update ci

* add pycharm project files to .gitignore list

* update pylintrc to conform vscode settings

* fix RemoteMachineMode for wrong trainingServicePlatform

* add python cache files to gitignore list

* move extract scalar reward logic from dispatcher to tuner

* update tuner code corresponding to last commit

* update doc for receive_trial_result api change

* add numpy to package whitelist of pylint

* distinguish param value from return reward for tuner.extract_scalar_reward

* update pylintrc

* add comments to dispatcher.handle_report_metric_data

* refactor extract reward from dict by tuner