NNI dogfood version 1

.gitattributes

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+*.ts text eol=lf
+*.py text eol=lf
+*.sh text eol=lf

Makefile

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+BIN_PATH ?= /usr/bin
+NODE_PATH ?= /usr/share
+EXAMPLE_PATH ?= /usr/share/nni/examples
+
+SRC_DIR := ${PWD}
+
+.PHONY: build install uninstall
+
+build:
+	### Building NNI Manager ###
+	cd src/nni_manager && yarn && yarn build
+
+	### Building Web UI ###
+	cd src/webui && yarn && yarn build
+
+	### Building Python SDK ###
+	cd src/sdk/pynni && python3 setup.py build
+
+	### Building nnictl ###
+	cd tools && python3 setup.py build
+
+
+install:
+	mkdir -p $(NODE_PATH)/nni
+	mkdir -p $(EXAMPLE_PATH)
+
+	### Installing NNI Manager ###
+	cp -rT src/nni_manager/dist $(NODE_PATH)/nni/nni_manager
+	cp -rT src/nni_manager/node_modules $(NODE_PATH)/nni/nni_manager/node_modules
+
+	### Installing Web UI ###
+	cp -rT src/webui/build $(NODE_PATH)/nni/webui
+	ln -sf $(NODE_PATH)/nni/nni_manager/node_modules/serve/bin/serve.js $(BIN_PATH)/serve
+
+	### Installing Python SDK dependencies ###
+	pip3 install -r src/sdk/pynni/requirements.txt
+	### Installing Python SDK ###
+	cd src/sdk/pynni && python3 setup.py install
+
+	### Installing nnictl ###
+	cd tools && python3 setup.py install
+
+	echo '#!/bin/sh' > $(BIN_PATH)/nnimanager
+	echo 'cd $(NODE_PATH)/nni/nni_manager && node main.js $$@' >> $(BIN_PATH)/nnimanager
+	chmod +x $(BIN_PATH)/nnimanager
+
+	install -m 755 tools/nnictl $(BIN_PATH)/nnictl
+
+	### Installing examples ###
+	cp -rT examples $(EXAMPLE_PATH)
+
+
+dev-install:
+	### Installing Python SDK dependencies ###
+	pip3 install --user -r src/sdk/pynni/requirements.txt
+	### Installing Python SDK ###
+	cd src/sdk/pynni && pip3 install --user -e .
+
+	### Installing nnictl ###
+	cd tools && pip3 install --user -e .
+
+
+uninstall:
+	-rm -r $(EXAMPLE_PATH)
+	-rm -r $(NODE_PATH)/nni
+	-pip3 uninstall -y nnictl
+	-pip3 uninstall -y nni
+	-rm $(BIN_PATH)/nnictl
+	-rm $(BIN_PATH)/nnimanager
+	-rm $(BIN_PATH)/serve

README.md

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+# Introduction 
+Neural Network Intelligence(NNI) is a light package for supporting hyper-parameter tuning or neural architecture search. 
+It could easily run in different environments, such as: local/remote machine/cloud.
+And it offers a new annotation language for user to conveniently design search space.
+Also user could write code using any language or any machine learning framework. 
 
-# Contributing
+# Getting Started
+TODO: Guide users through getting your code up and running on their own system. In this section you can talk about:
+1.	Installation process
+2.	Software dependencies
+3.	Latest releases
+4.	API references
 
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.microsoft.com.
+# Build and Test
+TODO: Describe and show how to build your code and run the tests. 
 
-When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
+# Contribute
+TODO: Explain how other users and developers can contribute to make your code better. 
 
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
+# Privacy Statement
+The [Microsoft Enterprise and Developer Privacy Statement](https://privacy.microsoft.com/en-us/privacystatement) describes the privacy statement of this software.

docs/EnableAssessor.md

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+**Enable Assessor in your expeirment**
+===
+Assessor module is for assessing running trials. One common use case is early stopping, which terminates unpromising trial jobs based on their intermediate results.
+
+## Using NNI built-in Assessor
+Here we use the same example `examples/trials/mnist-annotation`. We use `Medianstop` assessor for this experiment. The yaml configure file is shown below:
+```
+authorName: your_name
+experimentName: auto_mnist
+# how many trials could be concurrently running
+trialConcurrency: 2
+# maximum experiment running duration
+maxExecDuration: 3h
+# empty means never stop
+maxTrialNum: 100
+# choice: local, remote  
+trainingServicePlatform: local
+# choice: true, false  
+useAnnotation: true
+tuner:
+  tunerName: TPE
+  optimizationMode: Maximize
+assessor:
+  assessorName: Medianstop
+  optimizationMode: Maximize
+trial:
+  trialCommand: python mnist.py
+  trialCodeDir: /usr/share/nni/examples/trials/mnist-annotation
+  trialGpuNum: 0
+```
+For our built-in assessors, you need to fill two fields: `assessorName` which chooses NNI provided assessors (refer to [here]() for built-in assessors), `optimizationMode` which includes Maximize and Minimize (you want to maximize or minimize your trial result).
+
+## Using user customized Assessor
+You can also write your own assessor following the guidance [here](). For example, you wrote an assessor for `examples/trials/mnist-annotation`. You should prepare the yaml configure below:
+```
+authorName: your_name
+experimentName: auto_mnist
+# how many trials could be concurrently running
+trialConcurrency: 2
+# maximum experiment running duration
+maxExecDuration: 3h
+# empty means never stop
+maxTrialNum: 100
+# choice: local, remote  
+trainingServicePlatform: local
+# choice: true, false  
+useAnnotation: true
+tuner:
+  tunerName: TPE
+  optimizationMode: Maximize
+assessor:
+  assessorCommand: your_command
+  assessorCodeDir: /path/of/your/asessor
+  assessorGpuNum: 0
+trial:
+  trialCommand: python mnist.py
+  trialCodeDir: /usr/share/nni/examples/trials/mnist-annotation
+  trialGpuNum: 0
+```
+You only need to fill three field: `assessorCommand`, `assessorCodeDir` and `assessorGpuNum`.