Merge from dogfood branch to master

Makefile

-BIN_PATH ?= /usr/bin
-NODE_PATH ?= /usr/share
-EXAMPLE_PATH ?= /usr/share/nni/examples
-
-SRC_DIR := ${PWD}
-
-.PHONY: build install uninstall
+BIN_PATH ?= ${HOME}/.local/bin
+INSTALL_PREFIX ?= ${HOME}/.local
+PIP_MODE ?= --user
+EXAMPLES_PATH ?= ${HOME}/nni/examples
+WHOAMI := $(shell whoami)
+.PHONY: build install uninstall dev-install
+YARN := $(INSTALL_PREFIX)/yarn/bin/yarn
 
 build:
 	### Building NNI Manager ###
@@ -21,50 +21,124 @@ build:
 
 
 install:
-	mkdir -p $(NODE_PATH)/nni
-	mkdir -p $(EXAMPLE_PATH)
+    ifneq ('$(HOME)', '/root')
+        ifeq (${WHOAMI}, root)
+			### Sorry, sudo make install is not supported ###
+			exit 1
+        endif
+    endif
+
+	mkdir -p $(BIN_PATH)
+	mkdir -p $(INSTALL_PREFIX)/nni
+	mkdir -p $(EXAMPLES_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
+	cp -rT src/nni_manager/dist $(INSTALL_PREFIX)/nni/nni_manager
+	cp -rT src/nni_manager/node_modules $(INSTALL_PREFIX)/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
+	cp -rT src/webui/build $(INSTALL_PREFIX)/nni/webui
+	ln -sf $(INSTALL_PREFIX)/nni/nni_manager/node_modules/serve/bin/serve.js $(BIN_PATH)/serve
 	
 	### Installing Python SDK dependencies ###
-	pip3 install -r src/sdk/pynni/requirements.txt
+	pip3 install $(PIP_MODE) -r src/sdk/pynni/requirements.txt
 	### Installing Python SDK ###
-	cd src/sdk/pynni && python3 setup.py install
+	cd src/sdk/pynni && python3 setup.py install $(PIP_MODE)
 	
 	### Installing nnictl ###
-	cd tools && python3 setup.py install
+	cd tools && python3 setup.py install $(PIP_MODE)
 	
 	echo '#!/bin/sh' > $(BIN_PATH)/nnimanager
-	echo 'cd $(NODE_PATH)/nni/nni_manager && node main.js $$@' >> $(BIN_PATH)/nnimanager
+	echo 'cd $(INSTALL_PREFIX)/nni/nni_manager && node main.js $$@' >> $(BIN_PATH)/nnimanager
 	chmod +x $(BIN_PATH)/nnimanager
 	
-	install -m 755 tools/nnictl $(BIN_PATH)/nnictl
+	echo '#!/bin/sh' > $(BIN_PATH)/nnictl
+	echo 'NNI_MANAGER=$(BIN_PATH)/nnimanager WEB_UI_FOLDER=$(INSTALL_PREFIX)/nni/webui python3 -m nnicmd.nnictl $$@' >> $(BIN_PATH)/nnictl
+	chmod +x $(BIN_PATH)/nnictl
 	
 	### Installing examples ###
-	cp -rT examples $(EXAMPLE_PATH)
+	cp -rT examples $(EXAMPLES_PATH)
+
+
+pip-install:
+    ifneq ('$(HOME)', '/root')
+        ifeq (${WHOAMI}, root)
+			### Sorry, sudo make install is not supported ###
+			exit 1
+        endif
+    endif
+
+	### Prepare Node.js ###
+	wget https://nodejs.org/dist/v10.9.0/node-v10.9.0-linux-x64.tar.xz
+	tar xf node-v10.9.0-linux-x64.tar.xz
+	cp -rT node-v10.9.0-linux-x64 $(INSTALL_PREFIX)/node
+	
+	### Prepare Yarn 1.9.4 ###
+	wget https://github.com/yarnpkg/yarn/releases/download/v1.9.4/yarn-v1.9.4.tar.gz
+	tar xf yarn-v1.9.4.tar.gz
+	cp -rT yarn-v1.9.4 $(INSTALL_PREFIX)/yarn
+
+	### Building NNI Manager ###
+	cd src/nni_manager && $(YARN) && $(YARN) build
+	
+	### Building Web UI ###
+	cd src/webui && $(YARN) && $(YARN) build
+	
+	mkdir -p $(BIN_PATH)
+	mkdir -p $(INSTALL_PREFIX)/nni
+	
+	### Installing NNI Manager ###
+	cp -rT src/nni_manager/dist $(INSTALL_PREFIX)/nni/nni_manager
+	cp -rT src/nni_manager/node_modules $(INSTALL_PREFIX)/nni/nni_manager/node_modules
+	echo '#!/bin/sh' > $(BIN_PATH)/nnimanager
+	echo 'cd $(INSTALL_PREFIX)/nni/nni_manager && node main.js $$@' >> $(BIN_PATH)/nnimanager
+	chmod +x $(BIN_PATH)/nnimanager
+
+	### Installing Web UI ###
+	cp -rT src/webui/build $(INSTALL_PREFIX)/nni/webui
+	ln -sf $(INSTALL_PREFIX)/nni/nni_manager/node_modules/serve/bin/serve.js $(BIN_PATH)/serve
+	
+	### Installing examples ###
+	cp -rT examples $(EXAMPLES_PATH)
 
 
 dev-install:
+	mkdir -p $(BIN_PATH)
+	mkdir -p $(INSTALL_PREFIX)/nni
+	
+	### Installing NNI Manager ###
+	ln -sf $(INSTALL_PREFIX)/nni/nni_manager $(PWD)/src/nni_manager/dist
+	ln -sf $(INSTALL_PREFIX)/nni/nni_manager/node_modules $(PWD)/src/nni_manager/node_modules
+	
+	### Installing Web UI ###
+	ln -sf $(INSTALL_PREFIX)/nni/webui $(PWD)/src/webui
+	ln -sf $(INSTALL_PREFIX)/nni/nni_manager/node_modules/serve/bin/serve.js $(BIN_PATH)/serve
+	
 	### Installing Python SDK dependencies ###
-	pip3 install --user -r src/sdk/pynni/requirements.txt
+	pip3 install $(PIP_MODE) -r src/sdk/pynni/requirements.txt
 	### Installing Python SDK ###
-	cd src/sdk/pynni && pip3 install --user -e .
+	cd src/sdk/pynni && pip3 install $(PIP_MODE) -e .
 	
 	### Installing nnictl ###
-	cd tools && pip3 install --user -e .
+	cd tools && pip3 install $(PIP_MODE) -e .
+	
+	echo '#!/bin/sh' > $(BIN_PATH)/nnimanager
+	echo 'cd $(INSTALL_PREFIX)/nni/nni_manager && node main.js $$@' >> $(BIN_PATH)/nnimanager
+	chmod +x $(BIN_PATH)/nnimanager
+	
+	echo '#!/bin/sh' > $(BIN_PATH)/nnictl
+	echo 'NNI_MANAGER=$(BIN_PATH)/nnimanager python3 -m nnicmd.nnictl $$@' >> $(BIN_PATH)/nnictl
+	chmod +x $(BIN_PATH)/nnictl
+	
+	### Installing examples ###
+	ln -sf $(EXAMPLES_PATH) $(PWD)/examples
 
 
 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
+	-pip3 uninstall -y nnictl
+	-rm -r $(INSTALL_PREFIX)/nni
+	-rm -r $(EXAMPLES_PATH)
 	-rm $(BIN_PATH)/serve
+	-rm $(BIN_PATH)/nnimanager
+	-rm $(BIN_PATH)/nnictl

README.md

 # 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. 
 
-# 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
+NNI (Neural Network Intelligence) is a toolkit to help users running automated machine learning experiments. 
+The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters at different environments (e.g. local, remote servers, Cloud).
+
+```
+            AutoML experiment                                 Training Services
+┌────────┐        ┌────────────────────────┐                  ┌────────────────┐
+│ nnictl │ ─────> │  nni_manager           │                  │ Local Machine  │
+└────────┘        │    sdk/tuner           │                  └────────────────┘
+                  │      hyperopt_tuner    │
+                  │      evolution_tuner   │    trail jobs    ┌────────────────┐
+                  │      ...               │     ────────>    │ Remote Servers │          
+                  ├────────────────────────┤                  └────────────────┘
+                  │  trial job source code │                  
+                  │    sdk/annotation      │                  ┌────────────────┐
+                  ├────────────────────────┤                  │ Yarn,K8s,      │
+                  │  nni_board             │                  │ ...            │
+                  └────────────────────────┘                  └────────────────┘
+```
+## **Who should consider using NNI**
+* You want to try different AutoML algorithms for your training code (model) at local
+* You want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers, Cloud)
+* As a researcher and data scientist, you want to implement your own AutoML algorithms and compare with other algorithms
+* As a ML platform owner, you want to support AutoML in your platform
+
+# Getting Started with NNI
+
+## **Installation**
+Install through python pip
+* requirements: python >= 3.5
+```
+pip3 install -v --user git+https://github.com/Microsoft/NeuralNetworkIntelligence.git
+source ~/.bashrc
+```
+
+
+## **Quick start: run an experiment at local**
+Requirements:
+* with NNI installed on your machine.
+
+Run the following command to create an experiment for [mnist]
+```bash
+    nnictl create --config ~/nni/examples/trials/mnist-annotation/config.yaml
+```
+This command will start the experiment and WebUI. The WebUI endpoint will be shown in the output of this command (for example, `http://localhost:8080`). Open this URL using your browsers. You can analyze your experiment through WebUI, or open trials' tensorboard. Please refer to [here](docs/GetStarted.md) for the GetStarted tutorial.
 
-# Build and Test
-TODO: Describe and show how to build your code and run the tests. 
 
 # Contribute
-TODO: Explain how other users and developers can contribute to make your code better. 
+NNI is designed as an automatic searching framework with high extensibility. NNI has a very clear modular design. Contributing more tuner/assessor algorithms, training services, SDKs are really welcome. Please refer to [here](docs/ToContribute.md) for how to contribute.
 
 # Privacy Statement
 The [Microsoft Enterprise and Developer Privacy Statement](https://privacy.microsoft.com/en-us/privacystatement) describes the privacy statement of this software.

docs/CustomizedTuner.md

+# Customized Tuner for Experts
+
+*Tuner receive result from Trial as a matric to evaluate the performance of a specific parameters/architecture configure. And tuner send next hyper-parameter or architecture configure to Trial.*
+
+So, if user want to implement a customized Tuner, she/he only need to:
+
+1) Inherit a tuner of a base Tuner class
+2) Implement receive_trial_result and generate_parameter function
+3) Write a script to run Tuner
+
+Here ia an example:
+
+**1) Inherit a tuner of a base Tuner class**
+```python
+from nni.tuner import Tuner
+
+class CustomizedTuner(Tuner):
+    def __init__(self, ...):
+        ...
+```
+
+**2) Implement receive_trial_result and generate_parameter function**
+```python
+from nni.tuner import Tuner
+
+class CustomizedTuner(Tuner):
+    def __init__(self, ...):
+        ...
+    
+    def receive_trial_result(self, parameter_id, parameters, reward):
+    '''
+    Record an observation of the objective function and Train
+    parameter_id: int
+    parameters: object created by 'generate_parameters()'
+    reward: object reported by trial
+    '''
+    # your code implements here.
+    ...
+    
+    def generate_parameters(self, parameter_id):
+    '''
+    Returns a set of trial (hyper-)parameters, as a serializable object
+    parameter_id: int
+    '''
+    # your code implements here.
+    return your_parameters
+    ...
+```
+```receive_trial_result``` will receive ```the parameter_id, parameters, reward``` as parameters input. Also, Tuner will receive the ```reward``` object are exactly same reward that Trial send.
+
+The ```your_parameters``` return from ```generate_parameters``` function, will be package as json object by NNI SDK. NNI SDK will unpack json object so the Trial will receive the exact same ```your_parameters``` from Tuner.
+
+For example:
+If the you implement the ```generate_parameters``` like this:
+```python
+    def generate_parameters(self, parameter_id):
+        '''
+        Returns a set of trial (hyper-)parameters, as a serializable object
+        parameter_id: int
+        '''
+        # your code implements here.
+        return {"dropout": 0.3, "learning_rate": 0.4}
+```
+It's means your Tuner will always generate parameters ```{"dropout": 0.3, "learning_rate": 0.4}```. Then Trial will receive ```{"dropout": 0.3, "learning_rate": 0.4}``` this object will using ```nni.get_parameters()``` API from NNI SDK. After training of Trial, it will send result to Tuner by calling ```nni.report_final_result(0.93)```. Then ```receive_trial_result``` will function will receied these parameters like：
+```
+parameter_id = 82347
+parameters = {"dropout": 0.3, "learning_rate": 0.4}
+reward = 0.93
+```
+
+**3) Configure your customized tuner in experiment yaml config file**
+
+NNI needs to locate your customized tuner class and instantiate the class, so you need to specify the location of the customized tuner class and pass literal values as parameters to the \_\_init__ constructor.
+```yaml
+tuner:
+  codeDir: /home/abc/mytuner
+  classFileName: my_customized_tuner.py
+  className: CustomizedTuner
+  # Any parameter need to pass to your tuner class __init__ constructor
+  # can be specified in this optional classArgs field, for example 
+  classArgs:
+    arg1: value1
+```
+
+More detail example you could see:
+> * [evolution-tuner](../src/sdk/pynni/nni/evolution_tuner)
+> * [hyperopt-tuner](../src/sdk/pynni/nni/hyperopt_tuner)
+> * [evolution-based-customized-tuner](../examples/tuners/ga_customer_tuner)

docs/EnableAssessor.md

@@ -18,17 +18,19 @@ trainingServicePlatform: local
 # choice: true, false  
 useAnnotation: true
 tuner:
-  tunerName: TPE
-  optimizationMode: Maximize
+  builtinTunerName: TPE
+  classArgs:
+    optimize_mode: maximize
 assessor:
-  assessorName: Medianstop
-  optimizationMode: Maximize
+  builtinAssessorName: Medianstop
+  classArgs:
+    optimize_mode: maximize
 trial:
-  trialCommand: python mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist-annotation
-  trialGpuNum: 0
+  command: python mnist.py
+  codeDir: /usr/share/nni/examples/trials/mnist-annotation
+  gpuNum: 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).
+For our built-in assessors, you need to fill two fields: `builtinAssessorName` which chooses NNI provided assessors (refer to [here]() for built-in assessors), `optimize_mode` 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:
@@ -46,15 +48,25 @@ trainingServicePlatform: local
 # choice: true, false  
 useAnnotation: true
 tuner:
-  tunerName: TPE
-  optimizationMode: Maximize
+  # Possible values: TPE, Random, Anneal, Evolution
+  builtinTunerName: TPE
+  classArgs:
+    optimize_mode: maximize
 assessor:
-  assessorCommand: your_command
-  assessorCodeDir: /path/of/your/asessor
-  assessorGpuNum: 0
+  # Your assessor code directory
+  codeDir: 
+  # Name of the file which contains your assessor class
+  classFileName: 
+  # Your assessor class name, must be a subclass of nni.Assessor
+  className: 
+  # Parameter names and literal values you want to pass to
+  # the __init__ constructor of your assessor class
+  classArgs:
+    arg1: value1
+  gpuNum: 0
 trial:
-  trialCommand: python mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist-annotation
-  trialGpuNum: 0
+  command: python mnist.py
+  codeDir: /usr/share/nni/examples/trials/mnist-annotation
+  gpuNum: 0
 ```
-You only need to fill three field: `assessorCommand`, `assessorCodeDir` and `assessorGpuNum`.
+You need to fill: `codeDir`, `classFileName`, `className`, and pass parameters to \_\_init__ constructor through `classArgs` field if the \_\_init__ constructor of your assessor class has required parameters.
\ No newline at end of file

docs/ExperimentConfig.md

@@ -19,14 +19,15 @@ searchSpacePath:
 useAnnotation: 
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: 
-  #choice: Maximize, Minimize
-  optimizationMode: 
-  tunerGpuNum: 
+  builtinTunerName:
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode:
+  gpuNum: 
 trial:
-  trialCommand: 
-  trialCodeDir: 
-  trialGpuNum: 
+  command: 
+  codeDir: 
+  gpuNum: 
 #machineList can be empty if the platform is local
 machineList:
   - ip: 
@@ -48,20 +49,22 @@ searchSpacePath:
 useAnnotation: 
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: 
-  #choice: Maximize, Minimize
-  optimizationMode: 
-  tunerGpuNum: 
+  builtinTunerName:
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode:
+  gpuNum: 
 assessor:
   #choice: Medianstop
-  assessorName: 
-  #choice: Maximize, Minimize
-  optimizationMode: 
-  assessorGpuNum: 
+  builtinAssessorName:
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode:
+  gpuNum: 
 trial:
-  trialCommand: 
-  trialCodeDir: 
-  trialGpuNum: 
+  command: 
+  codeDir: 
+  gpuNum: 
 #machineList can be empty if the platform is local
 machineList:
   - ip: 
@@ -82,20 +85,22 @@ trainingServicePlatform:
 useAnnotation: 
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: 
-  #choice: Maximize, Minimize
-  optimizationMode: 
-  tunerGpuNum: 
+  builtinTunerName:
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode:
+  gpuNum: 
 assessor:
   #choice: Medianstop
-  assessorName: 
-  #choice: Maximize, Minimize
-  optimizationMode: 
-  assessorGpuNum: 
+  builtinAssessorName:
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode:
+  gpuNum: 
 trial:
-  trialCommand: 
-  trialCodeDir: 
-  trialGpuNum: 
+  command: 
+  codeDir: 
+  gpuNum: 
 #machineList can be empty if the platform is local
 machineList:
   - ip: 
@@ -108,11 +113,13 @@ machineList:
   * Description  
             
 	 __authorName__ is the name of the author who create the experiment.
+   TBD: add default value
 	 
 * __experimentName__
   * Description
   
     __experimentName__ is the name of the experiment you created.  
+    TBD: add default value
 	
 * __trialConcurrency__
   * Description
@@ -155,61 +162,73 @@ machineList:
 * __tuner__
   * Description
   
-    __tuner__ specifies the tuner algorithm you use to run an experiment, there are two kinds of ways to set tuner. One way is to use tuner provided by nni sdk, you just need to set __tunerName__ and __optimizationMode__. Another way is to use your own tuner file, and you need to set __tunerCommand__, __tunerCwd__.
-  * __tunerName__ and __optimizationMode__
-    * __tunerName__
+    __tuner__ specifies the tuner algorithm you use to run an experiment, there are two kinds of ways to set tuner. One way is to use tuner provided by nni sdk, you just need to set __builtinTunerName__ and __classArgs__. Another way is to use your own tuner file, and you need to set __codeDirectory__, __classFileName__, __className__ and __classArgs__.
+  * __builtinTunerName__ and __classArgs__
+    * __builtinTunerName__
     
-	  __tunerName__ specifies the name of system tuner you want to use, nni sdk provides four kinds of tuner, including {__TPE__, __Random__, __Anneal__, __Evolution__}
-	 * __optimizationMode__
+	  __builtinTunerName__ specifies the name of system tuner you want to use, nni sdk provides four kinds of tuner, including {__TPE__, __Random__, __Anneal__, __Evolution__}
+	 * __classArgs__
 	
-	   __optimizationMode__ specifies the optimization mode of tuner algorithm, including {__Maximize__, __Minimize__}
-  * __tunerCommand__ and __tunerCwd__
-      * __tunerCommand__
+	   __classArgs__ specifies the arguments of tuner algorithm
+  * __codeDir__, __classFileName__, __className__ and __classArgs__
+      * __codeDir__
         
-		__tunerCommand__ specifies the command you want to use to run your own tuner file, for example {__python3 mytuner.py__}
-	   * __tunerCwd__
+		__codeDir__ specifies the directory of tuner code.
+	    * __classFileName__
 	   
-	     __tunerCwd__ specifies the working directory of your own tuner file, which is the path of your own tuner file.
-  * __tunerGpuNum__
+	  __classFileName__ specifies the name of tuner file.
+     * __className__
+	   
+	  __className__ specifies the name of tuner class.
+     * __classArgs__
+	   
+	  __classArgs__ specifies the arguments of tuner algorithm.
+  * __gpuNum__
     
-	  __tunerGPUNum__ specifies the gpu number you want to use to run the tuner process. The value of this field should be a positive number.
+	  __gpuNum__ specifies the gpu number you want to use to run the tuner process. The value of this field should be a positive number.
 	  
 	    Note: you could only specify one way to set tuner, for example, you could set {tunerName, optimizationMode} or {tunerCommand, tunerCwd}, and you could not set them both. 
 
 * __assessor__
  
   * Description
- 
-    __assessor__ specifies the assessor algorithm you use to run experiment, there are two kinds of ways to set assessor. One way is to use assessor provided by nni sdk, you just need to set __assessorName__ and __optimizationMode__. Another way is to use your own assessor file, and you need to set __assessorCommand__, __assessorCwd__.
-  * __assessorName__ and __optimizationMode__
-    * __assessorName__
+  
+    __assessor__ specifies the assessor algorithm you use to run an experiment, there are two kinds of ways to set assessor. One way is to use assessor provided by nni sdk, you just need to set __builtinAssessorName__ and __classArgs__. Another way is to use your own tuner file, and you need to set __codeDirectory__, __classFileName__, __className__ and __classArgs__.
+  * __builtinAssessorName__ and __classArgs__
+    * __builtinAssessorName__
     
-	  __assessorName__ specifies the name of system assessor you want to use, nni sdk provides one kind of assessor, which is {__Medianstop__}.
-	 * __optimizationMode__
-	 
-	   __optimizationMode__ specifies the optimization mode of tuner algorithm, including {__Maximize__, __Minimize__}
-  * __assessorCommand__ and __assessorCwd__
-      * __assessorCommand__
+	  __builtinAssessorName__ specifies the name of system assessor you want to use, nni sdk provides four kinds of tuner, including {__TPE__, __Random__, __Anneal__, __Evolution__}
+	 * __classArgs__
+	
+	   __classArgs__ specifies the arguments of tuner algorithm
+  * __codeDir__, __classFileName__, __className__ and __classArgs__
+      * __codeDir__
         
-		__assessorCommand__ specifies the command you want to use to run your own assessor file, for example {__python3 myassessor.py__}
-	 * __assessorCwd__
-	  
-	   __assessorCwd__ specifies the working directory of your own assessor file, which is the path of your own assessor file.
-  * __assessorGpuNum__
+		__codeDir__ specifies the directory of tuner code.
+	    * __classFileName__
+	   
+	  __classFileName__ specifies the name of tuner file.
+     * __className__
+	   
+	  __className__ specifies the name of tuner class.
+     * __classArgs__
+	   
+	  __classArgs__ specifies the arguments of tuner algorithm.
+  * __gpuNum__
     
-	__assessorGPUNum__ specifies the gpu number you want to use to run the assessor process. The value of this field should be a positive number.
+	__gpuNum__ specifies the gpu number you want to use to run the assessor process. The value of this field should be a positive number.
 
-        Note: you could only specify one way to set assessor, for example, you could set {assessorName, optimizationMode} or {assessorCommand, assessorCwd}, and you could not set them both.If you do not want to use assessor, you just need to leave assessor empty or remove assessor in your config file. 
+        Note: you could only specify one way to set assessor, for example, you could set {assessorName, optimizationMode} or {assessorCommand, assessorCwd}, and you could not set them both.If you do not want to use assessor, you just need to leave assessor empty or remove assessor in your config file. Default value is 0. 
 * __trial__
-  * __trialCommand__
+  * __command__
 
-      __trialCommand__  specifies the command to run trial process.
-  * __trialCodeDir__
+      __command__  specifies the command to run trial process.
+  * __codeDir__
     
-	  __trialCodeDir__ specifies the directory of your own trial file.
-  * __trialGpuNum__
+	  __codeDir__ specifies the directory of your own trial file.
+  * __gpuNum__
     
-	  __trialGpuNum__ specifies the num of gpu you want to use to run your trial process.
+	  __gpuNum__ specifies the num of gpu you want to use to run your trial process. Default value is 0. 
 * __machineList__
  
      __machineList__ should be set if you set __trainingServicePlatform__=remote, or it could be empty.
@@ -228,6 +247,17 @@ machineList:
     
 	__passwd__ specifies the password of your account.
 
+  * __sshKeyPath__
+
+    If you want to use ssh key to login remote machine, you could set __sshKeyPath__ in config file. __sshKeyPath__ is the path of ssh key file, which should be valid.
+	
+	    Note: if you set passwd and sshKeyPath simultaneously, nni will try passwd.
+		
+  * __passphrase__
+
+    __passphrase__ is used to protect ssh key, which could be empty if you don't have passphrase.
+
+
 ## Examples
 * __local mode__
 
@@ -244,14 +274,15 @@ trainingServicePlatform: local
 useAnnotation: true
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
-  tunerGpuNum: 0
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+  gpuNum: 0
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /nni/mnist
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: /nni/mnist
+  gpuNum: 0
 ```
 
   If you want to use assessor, you could add assessor configuration in your file.
@@ -268,20 +299,22 @@ searchSpacePath: /nni/search_space.json
 useAnnotation: false
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
-  tunerGpuNum: 0
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+  gpuNum: 0
 assessor:
   #choice: Medianstop
-  assessorName: Medianstop
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
-  assessorGpuNum: 0
+  builtinAssessorName: Medianstop
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+  gpuNum: 0
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /nni/mnist
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: /nni/mnist
+  gpuNum: 0
 ```
 
   Or you could specify your own tuner and assessor file as following:
@@ -297,17 +330,25 @@ searchSpacePath: /nni/search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  tunerCommand: python3 mytuner.py
-  tunerCwd: /nni/tuner
-  tunerGpuNum: 0
+  codeDir: /nni/tuner
+  classFileName: mytuner.py
+  className: MyTuner
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+  gpuNum: 0
 assessor:
-  assessorCommand: python3 myassessor.py
-  assessorCwd: /nni/assessor
-  assessorGpuNum: 0
+  codeDir: /nni/assessor
+  classFileName: myassessor.py
+  className: MyAssessor
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+  gpuNum: 0
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /nni/mnist
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: /nni/mnist
+  gpuNum: 0
 ```
 
 * __remote mode__
@@ -326,14 +367,15 @@ searchSpacePath: /nni/search_space.json
 useAnnotation: false
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
-  tunerGpuNum: 0
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+  gpuNum: 0
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /nni/mnist
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: /nni/mnist
+  gpuNum: 0
 #machineList can be empty if the platform is local
 machineList:
   - ip: 10.10.10.10
@@ -347,5 +389,6 @@ machineList:
   - ip: 10.10.10.12
     port: 22
     username: test
-    passwd: test
+    sshKeyPath: /nni/sshkey
+    passphrase: qwert
 ```
\ No newline at end of file

docs/GetStarted.md

 **Getting Started with NNI**
 ===
-NNI (Nerual Network Intelligance) is a toolkit to help users running automated machine learning experiment. 
-The tool dispatchs and runs trail jobs that generated by tunning algorithms to search the best neural architecture and/or hyper-parameters at different enviroments (e.g. local, remote servers, Cloud).
 
-```
-            AutoML experiment                                 Training Services
-┌────────┐        ┌────────────────────────┐                  ┌────────────────┐
-│ nnictl │ ─────> │  nni_manager           │                  │ Local Machine  │
-└────────┘        │    sdk/tuner           │                  └────────────────┘
-                  │      hyperopt_tuner    │
-                  │      evlution_tuner    │    trail jobs    ┌────────────────┐
-                  │      ...               │     ────────>    │ Remote Servers │          
-                  ├────────────────────────┤                  └────────────────┘
-                  │  trail job source code │                  
-                  │    sdk/annotation      │                  ┌────────────────┐
-                  ├────────────────────────┤                  │ Yarn,K8s,      │
-                  │  nni_board             │                  │ ...            │
-                  └────────────────────────┘                  └────────────────┘
-```
-## **Who should consider using NNI**
-* You want to try different AutoML algorithms for your training code (model) at local
-* You want to run AutoML trail jobs in different enviroments to speed up search (e.g. remote servers, Cloud)
-* As a reseacher and data scientist, you want to implement your own AutoML algorithms and compare with other algorithms
-* As a ML platform owner, you want to support AutoML in your platform
-
-## **Setup**
-* __Dependencies__  
-nni requires:
-```
-  python >= 3.5
-  node >= 10.9.0
-  yarn >= 1.9.4
-```
-Before install nni, please make sure you have installed python environment correctly.
-* __User installation__
+## **Installation**
+* __Dependencies__
 
-   * clone nni repository 
-   
-         git clone https://github.com/Microsoft/NeuralNetworkIntelligence
+      python >= 3.5
 
-   * run install.sh
-   
-         cd NeuralNetworkIntelligence
-         sh ./install.sh
+    python pip should also be correctly installed. You could use "which pip" or "pip -V" to check in Linux.
+    TBD: For now, we don's support virtual environment.
 
-For more details about installation, please refer to [Installation instructions](Installation.md).
+* __Install NNI through pip__
 
-## **Quick start: run an experiment at local**
-Requirements:
-* local enviroment setup [TODO]
+      pip3 install -v --user git+https://github.com/Microsoft/NeuralNetworkIntelligence.git
+      source ~/.bashrc
+
+* __Install NNI through source code__
+   
+      git clone https://github.com/Microsoft/NeuralNetworkIntelligence
+      cd NeuralNetworkIntelligence
+      chmod +x install.sh
+      source install.sh
 
-Run the following command to create an experiemnt for [mnist]
-```bash
-    nnictl create --config /usr/share/nni/examples/trials/mnist-annotation/config.yml
-```
-This command will start the experiment and WebUI. The WebUI endpoint will be shown in the output of this command (for example, `http://localhost:8080`). Open this URL using your browsers. You can analyze your experiment through WebUI, or open trials' tensorboard.
 
 ## **Quick start: run a customized experiment**
 An experiment is to run multiple trial jobs, each trial job tries a configuration which includes a specific neural architecture (or model) and hyper-parameter values. To run an experiment through NNI, you should:
@@ -64,48 +30,54 @@ An experiment is to run multiple trial jobs, each trial job tries a configuratio
 * Provide a yaml experiment configure file
 * (optional) Provide or choose an assessor
 
-**Prepare trial**: Let's use a simple trial example, e.g. mnist, provided by NNI. After you installed NNI, NNI examples have been put in /usr/share/nni/examples, run `ls /usr/share/nni/examples/trials` to see all the trial examples. You can simply execute the following command to run the NNI mnist example: 
+**Prepare trial**: Let's use a simple trial example, e.g. mnist, provided by NNI. After you installed NNI, NNI examples have been put in ~/nni/examples, run `ls ~/nni/examples/trials` to see all the trial examples. You can simply execute the following command to run the NNI mnist example: 
 
-      python /usr/share/nni/examples/trials/mnist-annotation/mnist.py
+      python ~/nni/examples/trials/mnist-annotation/mnist.py
 
 This command will be filled in the yaml configure file below. Please refer to [here]() for how to write your own trial.
 
-**Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Bayesian Optimization etc. Users can write their own tuner (refer to [here]()), but for simplicity, here we can choose a tuner provided by NNI as below:
+**Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to [here]()), but for simplicity, here we choose a tuner provided by NNI as below:
 
       tunerName: TPE
       optimizationMode: maximize
 
 *tunerName* is used to specify a tuner in NNI, *optimizationMode* is to indicate whether you want to maximize or minimize your trial's result.
 
-**Prepare configure file**: Since you have already known which trial code you are going to run and which tuner you are going to use, it is time to prepare the yaml configure file. NNI provides a demo configure file for each trial example, `cat /usr/share/nni/examples/trials/mnist-annotation/config.yml` to see it. Its content is basically shown below:
+**Prepare configure file**: Since you have already known which trial code you are going to run and which tuner you are going to use, it is time to prepare the yaml configure file. NNI provides a demo configure file for each trial example, `cat ~/nni/examples/trials/mnist-annotation/config.yml` to see it. Its content is basically 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
+  builtinTunerName: TPE
+  classArgs:
+    optimize_mode: maximize
 trial:
-  trialCommand: python mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist-annotation
-  trialGpuNum: 0
+  command: python mnist.py
+  codeDir: ~/nni/examples/trials/mnist-annotation
+  gpuNum: 0
 ``` 
 
 Here *useAnnotation* is true because this trial example uses our python annotation (refer to [here]() for details). For trial, we should provide *trialCommand* which is the command to run the trial, provide *trialCodeDir* where the trial code is. The command will be executed in this directory. We should also provide how many GPUs a trial requires.
 
 With all these steps done, we can run the experiment with the following command:
 
-      nnictl create --config /usr/share/nni/examples/trials/mnist-annotation/config.yml
+      nnictl create --config ~/nni/examples/trials/mnist-annotation/config.yml
 
 You can refer to [here](NNICTLDOC.md) for more usage guide of *nnictl* command line tool.
 
@@ -118,11 +90,8 @@ The experiment has been running now, NNI provides WebUI for you to view experime
 * [Tuners supported by NNI.](../src/sdk/pynni/nni/README.md)
 * [How to enable early stop (i.e. assessor) in an experiment?](EnableAssessor.md)
 * [How to run an experiment on multiple machines?](RemoteMachineMode.md)
-* [How to write a customized tuner?](../examples/tuners/README.md)
+* [How to write a customized tuner?](CustomizedTuner.md)
 * [How to write a customized assessor?](../examples/assessors/README.md)
-* [How to resume an experiment?]()
+* [How to resume an experiment?](NNICTLDOC.md)
 * [Tutorial of the command tool *nnictl*.](NNICTLDOC.md)
 * [How to use *nnictl* to control multiple experiments?]()
-
-## How to contribute
-TBD

docs/NNICTLDOC.md

@@ -8,12 +8,10 @@ nnictl support commands:
 ```
 nnictl create
 nnictl stop
-nnictl create
 nnictl update
 nnictl resume
 nnictl trial
 nnictl webui
-nnictl rest
 nnictl experiment
 nnictl config
 nnictl log
@@ -72,7 +70,7 @@ nnictl log
 	 * __nnictl update searchspace__
        * Description
           
-		     You can use this command to update an experiment's searchspace.
+		     You can use this command to update an experiment's search space.
        
        * Usage
  
@@ -200,15 +198,7 @@ nnictl log
     * Usage
     
 	      nnictl config show
-
-### Manage restful server
-* __nnictl rest check__  
-  * Description
-     
-	    Check the status of restful server
-   * Usage
-		
-		    nnictl rest check
+        
   
 ### Manage log
 * __nnictl log stdout__

docs/RELEASE.md

+# Release 0.1.0 - 9/15/2018 
+
+Initial release of Neural Network Intelligence (NNI).
+
+## Major Features
+   * Installation and Deployment
+      * Support pip install and source codes install
+      * Support training services on local mode(including Multi-GPU mode) as well as multi-machines mode
+   * Tuners, Accessors and Trial
+      * Support AutoML algorithms including:  hyperopt_tpe, hyperopt_annealing, hyperopt_random, and evolution_tuner
+      * Support assessor(early stop) algorithms including: medianstop algorithm
+      * Provide Python API for user defined tuners and accessors
+      * Provide Python API for user to wrap trial code as NNI deployable codes
+   * Experiments
+      * Provide a command line toolkit 'nnictl' for experiments management
+      * Provide a web UI for viewing experiments details and managing experiments
+   * Continuous Integration
+      * Support CI by providing out-of-box integration with [travis-ci](https://github.com/travis-ci) on ubuntu    
+   * Others
+      * Support simple GPU job scheduling 
+
+
+    
+      
+   
\ No newline at end of file

docs/RemoteMachineMode.md

@@ -10,6 +10,18 @@ NNI supports running an experiment on multiple machines, called remote machine m
 ## Setup environment
 Install NNI on each of your machines following the install guide [here](GetStarted.md).
 
+For remote machines that are used only to run trials but not the nnictl, you can just install python SDK:
+
+* __Install python SDK through pip__
+
+      pip3 install --user git+https://github.com/Microsoft/NeuralNetworkIntelligence.git#subdirectory=src/sdk/pynni
+
+* __Install python SDK through source code__
+
+      git clone https://github.com/Microsoft/NeuralNetworkIntelligence
+      cd src/sdk/pynni
+      python3 setup.py install
+
 ## Run an experiment
 Still using `examples/trials/mnist-annotation` as an example here. The yaml file you need is shown below: 
 ```
@@ -26,12 +38,13 @@ trainingServicePlatform: local
 # choice: true, false  
 useAnnotation: true
 tuner:
-  tunerName: TPE
-  optimizationMode: Maximize
+  builtinTunerName: TPE
+  classArgs:
+    optimize_mode: maximize
 trial:
-  trialCommand: python mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist-annotation
-  trialGpuNum: 0
+  command: python mnist.py
+  codeDir: /usr/share/nni/examples/trials/mnist-annotation
+  gpuNum: 0
 #machineList can be empty if the platform is local
 machineList:
   - ip: 10.1.1.1

docs/SearchSpaceSpec.md

+## How to define search space?
+
+### Hyper-parameter Search Space
+
+* A search space configure example as follow:
+
+```python
+{
+    "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
+    "conv_size":{"_type":"choice","_value":[2,3,5,7]},
+    "hidden_size":{"_type":"choice","_value":[124, 512, 1024]},
+    "batch_size":{"_type":"choice","_value":[50, 250, 500]},
+    "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
+}
+
+```
+
+The example define ```dropout_rate``` as variable which priori distribution is uniform distribution, and its value from ```0.1``` and ```0.5```.
+The tuner will sample parameters/architecture by understanding the search space first.
+
+User should define the name of variable, type and candidate value of variable.
+The candidate type and value for variable is here:
+
+* {"_type":"choice","_value":options}
+   * Which means the variable value is one of the options, which should be a list The elements of options can themselves be [nested] stochastic expressions. In this case, the stochastic choices that only appear in some of the options become conditional parameters.
+<br/>
+* {"_type":"randint","_value":[upper]}
+   * Which means the variable value is a random integer in the range [0, upper). The semantics of this distribution is that there is no more correlation in the loss function between nearby integer values, as compared with more distant integer values. This is an appropriate distribution for describing random seeds for example. If the loss function is probably more correlated for nearby integer values, then you should probably use one of the "quantized" continuous distributions, such as either quniform, qloguniform, qnormal or qlognormal.
+<br/>
+* {"_type":"uniform","_value":[low, high]}
+   * Which means the variable value is a value uniformly between low and high.
+   * When optimizing, this variable is constrained to a two-sided interval.
+<br/>
+* {"_type":"quniform","_value":[low, high, q]}
+   * Which means the variable value is a value like round(uniform(low, high) / q) * q
+   * Suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below.
+<br/>
+* {"_type":"loguniform","_value":[low, high]}
+   * Which means the variable value is a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.
+   * When optimizing, this variable is constrained to the interval [exp(low), exp(high)].
+<br/>
+* {"_type":"qloguniform","_value":[low, high, q]}
+   * Which means the variable value is a value like round(exp(uniform(low, high)) / q) * q
+   * Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
+<br/>
+* {"_type":"normal","_value":[label, mu, sigma]}
+   * Which means the variable value is a real value that's normally-distributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.
+<br/>
+* {"_type":"qnormal","_value":[label, mu, sigma, q]}
+   * Which means the variable value is a value like round(normal(mu, sigma) / q) * q
+   * Suitable for a discrete variable that probably takes a value around mu, but is fundamentally unbounded.
+<br/>
+* {"_type":"lognormal","_value":[label, mu, sigma]}
+   * Which means the variable value is a value drawn according to exp(normal(mu, sigma)) so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.
+<br/>
+* {"_type":"qlognormal","_value":[label, mu, sigma, q]}
+   * Which means the variable value is a value like round(exp(normal(mu, sigma)) / q) * q
+   * Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
+<br/>
\ No newline at end of file

docs/ToContribute.md

+## How to contribute
+
+TBD
\ No newline at end of file

docs/WriteYourTrial.md

@@ -2,39 +2,50 @@
 ===
 There would be only a few changes on your existing trial(model) code to make the code runnable on NNI. We provide two approaches for you to modify your code: `Python annotation` and `NNI APIs for trial`
 
-## Python annotation
-We designed a new syntax for users to annotation which variable they want to tune and in what range they want to tune the variable. Also, they can annotate which variable they want to report as intermediate result to `assessor`, and which variable to report as the final result (e.g. model accuracy) to `tuner`. A really appealing feature of our python annotation is that it exists as comments in your code, which means you can run your code as before without NNI. Let's look at an example, below is a piece of tensorflow code:
+## NNI APIs 
+We also support NNI APIs for trial code. By using this approach, you should first prepare a search space file. An example is shown below: 
 ```
-with tf.Session() as sess:
-    sess.run(tf.global_variables_initializer())
-    batch_size = 128
-    for i in range(10000):
-        batch = mnist.train.next_batch(batch_size)
-        dropout_rate = 0.5
-        mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
-                                                mnist_network.labels: batch[1],
-                                                mnist_network.keep_prob: dropout_rate})
-        if i % 100 == 0:
-            test_acc = mnist_network.accuracy.eval(
-                feed_dict={mnist_network.images: mnist.test.images,
-                            mnist_network.labels: mnist.test.labels,
-                            mnist_network.keep_prob: 1.0})
+{
+    "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
+    "conv_size":{"_type":"choice","_value":[2,3,5,7]},
+    "hidden_size":{"_type":"choice","_value":[124, 512, 1024]},
+    "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
+}
+```
+You can refer to [here](SearchSpaceSpec.md) for the tutorial of search space.
 
-    test_acc = mnist_network.accuracy.eval(
-        feed_dict={mnist_network.images: mnist.test.images,
-                    mnist_network.labels: mnist.test.labels,
-                    mnist_network.keep_prob: 1.0})
+Then, include `import nni` in your trial code to use NNI APIs. Using the line: 
+```
+RECEIVED_PARAMS = nni.get_parameters()
+```
+to get hyper-parameters' values assigned by tuner. `RECEIVED_PARAMS` is an object, for example: 
+```
+{"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}
 ```
 
-Let's say you want to tune batch\_size and dropout\_rate, and report test\_acc every 100 steps, at last report test\_acc as final result. With our python annotation, your code would look like below:
+On the other hand, you can use the API: `nni.report_intermediate_result(accuracy)` to send `accuracy` to assessor. And use `nni.report_final_result(accuracy)` to send `accuracy` to tuner. Here `accuracy` could be any python data type, but **NOTE that if you use built-in tuner/assessor, `accuracy` should be a numerical variable(e.g. float, int)**.
+
+The assessor will decide which trial should early stop based on the history performance of trial(intermediate result of one trial).
+The tuner will generate next parameters/architecture based on the explore history(final result of all trials).
+
+In the yaml configure file, you need two lines to enable NNI APIs:
+```
+useAnnotation: false
+searchSpacePath: /path/to/your/search_space.json
 ```
+
+You can refer to [here](../examples/trials/README.md) for more information about how to write trial code using NNI APIs.
+
+## NNI Annotation
+We designed a new syntax for users to annotate the variables they want to tune and in what range they want to tune the variables. Also, they can annotate which variable they want to report as intermediate result to `assessor`, and which variable to report as the final result (e.g. model accuracy) to `tuner`. A really appealing feature of our NNI annotation is that it exists as comments in your code, which means you can run your code as before without NNI. Let's look at an example, below is a piece of tensorflow code:
+```diff
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
-    """@nni.variable(nni.choice(50, 250, 500), name=batch_size)"""
++   """@nni.variable(nni.choice(50, 250, 500), name=batch_size)"""
     batch_size = 128
     for i in range(10000):
         batch = mnist.train.next_batch(batch_size)
-        """@nni.variable(nni.choice(1, 5), name=dropout_rate)"""
++       """@nni.variable(nni.choice(1, 5), name=dropout_rate)"""
         dropout_rate = 0.5
         mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
                                                 mnist_network.labels: batch[1],
@@ -44,47 +55,23 @@ with tf.Session() as sess:
                 feed_dict={mnist_network.images: mnist.test.images,
                             mnist_network.labels: mnist.test.labels,
                             mnist_network.keep_prob: 1.0})
-            """@nni.report_intermediate_result(test_acc)"""
++           """@nni.report_intermediate_result(test_acc)"""
 
     test_acc = mnist_network.accuracy.eval(
         feed_dict={mnist_network.images: mnist.test.images,
                     mnist_network.labels: mnist.test.labels,
                     mnist_network.keep_prob: 1.0})
-    """@nni.report_final_result(test_acc)"""
++   """@nni.report_final_result(test_acc)"""
 ```
 
-Simply adding four lines would make your code runnable on NNI. You can still run your code independently. `@nni.variable` works on its next line assignment, and `@nni.report_intermediate_result`/`@nni.report_final_result` would send the data to assessor/tuner at that line. Please refer to [here](../tools/annotation/README.md) for more annotation syntax and more powerful usage. In the yaml configure file, you need one line to enable Python annotation:
-```
-useAnnotation: true
-```
+Let's say you want to tune batch\_size and dropout\_rate, and report test\_acc every 100 steps, at last report test\_acc as final result. With our NNI annotation, your code would look like below:
 
-## NNI APIs for trial
-We also support NNI APIs for trial code. By using this approach, you should first prepare a search space file. An example is shown below: 
-```
-{
-    "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
-    "conv_size":{"_type":"choice","_value":[2,3,5,7]},
-    "hidden_size":{"_type":"choice","_value":[124, 512, 1024]},
-    "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
-}
-```
-You can refer to [here]() for the tutorial of search space.
 
-Then, include `import nni` in your trial code to use APIs. Using the line: 
-```
-RECEIVED_PARAMS = nni.get_parameters()
-```
-to get hyper-parameters' values assigned by tuner. `RECEIVED_PARAMS` is a json object, for example: 
+Simply adding four lines would make your code runnable on NNI. You can still run your code independently. `@nni.variable` works on its next line assignment, and `@nni.report_intermediate_result`/`@nni.report_final_result` would send the data to assessor/tuner at that line. Please refer to [here](../tools/annotation/README.md) for more annotation syntax and more powerful usage. In the yaml configure file, you need one line to enable NNI annotation:
 ```
-{'conv_size': 2, 'hidden_size': 124, 'learning_rate': 0.0307, 'dropout_rate': 0.2029}
+useAnnotation: true
 ```
 
-On the other hand, you can use the API: `nni.report_intermediate_result(accuracy)` to send `accuracy` to assessor. And use `nni.report_final_result(accuracy)` to send `accuracy` to tuner. Here `accuracy` could be any python data type, but **NOTE that if you use built-in tuner/assessor, `accuracy` should be a number (e.g. float, int)**.
-
-In the yaml configure file, you need two lines to enable NNI APIs:
-```
-useAnnotation: false
-searchSpacePath: /path/to/your/search_space.json
-```
+For users to correctly leverage NNI annotation, we briefly introduce how NNI annotation works here: NNI precompiles users' trial code to find all the annotations each of which is one line with `"""@nni` at the head of the line. Then NNI replaces each annotation with a corresponding NNI API at the location where the annotation is.
 
-You can refer to [here](../examples/trials/README.md) for more information about how to write trial code using NNI APIs.
+**Note that: in your trial code, you can use either one of NNI APIs and NNI annotation, but not both of them simultaneously.** 
\ No newline at end of file

examples/experiment_config.yml

@@ -9,17 +9,21 @@ searchSpacePath:
 #choice: true, false
 useAnnotation: 
 tuner:
-  tunerCommand: 
-  tunerCwd: 
-  tunerGpuNum: 
+  #choice: TPE, Random, Anneal, Evolution
+  builtinTunerName: 
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: 
 assessor:
-  assessorCommand: 
-  assessorCwd: 
-  assessorGpuNum: 
+  #choice: Medianstop
+  builtinAssessorName: 
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: 
 trial:
-  trialCommand: 
-  trialCodeDir: 
-  trialGpuNum: 
+  command: 
+  codeDir: 
+  gpuNum: 
 #machineList can be empty if the platform is local
 machineList:
   - ip: 

examples/trials/ga_squad/config.yml

@@ -8,9 +8,12 @@ trainingServicePlatform: local
 #choice: true, false
 useAnnotation: false
 tuner:
-  tunerCommand: python3 __main__.py
-  tunerCwd: /usr/share/nni/examples/tuners/ga_customer_tuner
+  codeDir: ~/nni/examples/tuners/ga_customer_tuner
+  classFileName: customer_tuner.py
+  className: CustomerTuner
+  classArgs:
+    optimize_mode: maximize
 trial:
-  trialCommand: python3 trial.py
-  trialCodeDir: /usr/share/nni/examples/trials/ga_squad
-  trialGpuNum: 0
+  command: python3 trial.py
+  codeDir: ~/nni/examples/trials/ga_squad
+  gpuNum: 0
\ No newline at end of file

examples/trials/mnist-annotation/config.yml

@@ -9,10 +9,11 @@ trainingServicePlatform: local
 useAnnotation: true
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist-annotation
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: ~/nni/examples/trials/mnist-annotation
+  gpuNum: 0
\ No newline at end of file

examples/trials/mnist-keras/config.yml

@@ -5,15 +5,16 @@ maxExecDuration: 1h
 maxTrialNum: 1
 #choice: local, remote
 trainingServicePlatform: local
-searchSpacePath: /usr/share/nni/examples/trials/mnist-keras/search_space.json
+searchSpacePath: ~/nni/examples/trials/mnist-keras/search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
 trial:
-  trialCommand: python3 mnist-keras.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist-keras
-  trialGpuNum: 0
+  command: python3 mnist-keras.py
+  codeDir: ~/nni/examples/trials/mnist-keras
+  gpuNum: 0
\ No newline at end of file

examples/trials/mnist-keras/mnist-keras.py

@@ -84,7 +84,7 @@ class SendMetrics(keras.callbacks.Callback):
         Run on end of each epoch
         '''
         LOG.debug(logs)
-        nni.report_intermediate_result(logs)
+        nni.report_intermediate_result(logs['acc'])
 
 def train(args, params):
     '''

examples/trials/mnist-smartparam/config.yml

@@ -9,10 +9,11 @@ trainingServicePlatform: local
 useAnnotation: true
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist-smartparam
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: ~/nni/examples/trials/mnist-smartparam
+  gpuNum: 0
\ No newline at end of file

examples/trials/mnist/config.yml

@@ -5,15 +5,16 @@ maxExecDuration: 1h
 maxTrialNum: 1
 #choice: local, remote
 trainingServicePlatform: local
-searchSpacePath: /usr/share/nni/examples/trials/mnist/search_space.json
+searchSpacePath: ~/nni/examples/trials/mnist/search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: ~/nni/examples/trials/mnist
+  gpuNum: 0
\ No newline at end of file

examples/trials/mnist/config_assessor.yml

@@ -5,20 +5,22 @@ maxExecDuration: 1h
 maxTrialNum: 1
 #choice: local, remote
 trainingServicePlatform: local
-searchSpacePath: /usr/share/nni/examples/trials/mnist/search_space.json
+searchSpacePath: ~/nni/examples/trials/mnist/search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
   #choice: TPE, Random, Anneal, Evolution
-  tunerName: TPE
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
 assessor:
   #choice: Medianstop
-  assessorName: Medianstop
-  #choice: Maximize, Minimize
-  optimizationMode: Maximize
+  builtinAssessorName: Medianstop
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
 trial:
-  trialCommand: python3 mnist.py
-  trialCodeDir: /usr/share/nni/examples/trials/mnist
-  trialGpuNum: 0
+  command: python3 mnist.py
+  codeDir: ~/nni/examples/trials/mnist
+  gpuNum: 0
\ No newline at end of file