* 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
* 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
Yuge Zhang
liuzhe-lz
lin bin
Yan Ni
Lee
QuanluZhang
Zejun Lin
Deshui Yu