ExperimentConfig.md

# Experiment config reference

A config file is needed when create an experiment, the path of the config file is provide to nnictl.
The config file is written in yaml format, and need to be written correctly.
This document describes the rule to write config file, and will provide some examples and templates. 
## Template
* __light weight(without Annotation and Assessor)__ 
```
authorName: 
experimentName: 
trialConcurrency: 
maxExecDuration: 
maxTrialNum: 
#choice: local, remote, pai, kubeflow
trainingServicePlatform: 
searchSpacePath: 
#choice: true, false
useAnnotation: 
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName:
  classArgs:
    #choice: maximize, minimize
    optimize_mode:
  gpuNum: 
trial:
  command: 
  codeDir: 
  gpuNum: 
#machineList can be empty if the platform is local
machineList:
  - ip: 
    port: 
    username: 
    passwd: 
```
* __Use Assessor__
```
authorName: 
experimentName: 
trialConcurrency: 
maxExecDuration: 
maxTrialNum: 
#choice: local, remote, pai, kubeflow
trainingServicePlatform: 
searchSpacePath: 
#choice: true, false
useAnnotation: 
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName:
  classArgs:
    #choice: maximize, minimize
    optimize_mode:
  gpuNum: 
assessor:
  #choice: Medianstop
  builtinAssessorName:
  classArgs:
    #choice: maximize, minimize
    optimize_mode:
  gpuNum: 
trial:
  command: 
  codeDir: 
  gpuNum: 
#machineList can be empty if the platform is local
machineList:
  - ip: 
    port: 
    username: 
    passwd: 
```
* __Use Annotation__
```
authorName: 
experimentName: 
trialConcurrency: 
maxExecDuration: 
maxTrialNum: 
#choice: local, remote, pai, kubeflow
trainingServicePlatform: 
#choice: true, false
useAnnotation: 
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName:
  classArgs:
    #choice: maximize, minimize
    optimize_mode:
  gpuNum: 
assessor:
  #choice: Medianstop
  builtinAssessorName:
  classArgs:
    #choice: maximize, minimize
    optimize_mode:
  gpuNum: 
trial:
  command: 
  codeDir: 
  gpuNum: 
#machineList can be empty if the platform is local
machineList:
  - ip: 
    port: 
    username: 
    passwd: 
```
## Configuration
* __authorName__
  * 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 created.  
    TBD: add default value
	
* __trialConcurrency__
  * Description
    
	 __trialConcurrency__ specifies the max num of trial jobs run simultaneously.  
	 
	    Note: if trialGpuNum is bigger than the free gpu numbers, and the trial jobs running simultaneously can not reach trialConcurrency number, some trial jobs will be put into a queue to wait for gpu allocation.
	 
* __maxExecDuration__
  * Description
    
	__maxExecDuration__ specifies the max duration time of an experiment.The unit of the time is {__s__, __m__, __h__, __d__}, which means {_seconds_, _minutes_, _hours_, _days_}.  
	
* __maxTrialNum__
  *  Description
    
	 __maxTrialNum__ specifies the max number of trial jobs created by nni, including succeeded and failed jobs.  
	 
* __trainingServicePlatform__
  * Description
      
	  __trainingServicePlatform__ specifies the platform to run the experiment, including {__local__, __remote__, __pai__, __kubeflow__}.  
	
    * __local__ run an experiment on local ubuntu machine.  
	
	
    * __remote__ submit trial jobs to remote ubuntu machines, and __machineList__ field should be filed in order to set up SSH connection to remote machine.  

	
    * __pai__  submit trial jobs to [OpenPai](https://github.com/Microsoft/pai) of Microsoft. For more details of pai configuration, please reference [PAIMOdeDoc](./PAIMode.md)
   
    * __kubeflow__ submit trial jobs to [kubeflow](https://www.kubeflow.org/docs/about/kubeflow/), nni support kubeflow based on normal kubernetes and [azure kubernetes](https://azure.microsoft.com/en-us/services/kubernetes-service/).
	
* __searchSpacePath__
  * Description
    
	 __searchSpacePath__ specifies the path of search space file, which should be a valid path in the local linux machine.
	        
	    Note: if set useAnnotation=True, the searchSpacePath field should be removed.
* __useAnnotation__
  * Description
   
    __useAnnotation__ use annotation to analysis trial code and generate search space. 
	   
	    Note: if set useAnnotation=True, the searchSpacePath field should be removed.

* __nniManagerIp__
  * Description
   
    __nniManagerIp__ set the IP address of the machine on which nni manager process runs. This field is optional, and if it's not set, eth0 device IP will be used instead.

        Note: run ifconfig on NNI manager's machine to check if eth0 device exists. If not, we recommend to set nnimanagerIp explicitly.
	   
		
* __tuner__
  * Description
  
    __tuner__ specifies the tuner algorithm in the experiment, there are two kinds of ways to set tuner. One way is to use tuner provided by nni sdk, need to set __builtinTunerName__ and __classArgs__. Another way is to use users' own tuner file, and need to set __codeDirectory__, __classFileName__, __className__ and __classArgs__.
  * __builtinTunerName__ and __classArgs__
    * __builtinTunerName__
    
	  __builtinTunerName__ specifies the name of system tuner, nni sdk provides four kinds of tuner, including {__TPE__, __Random__, __Anneal__, __Evolution__, __BatchTuner__, __GridSearch__}
	 * __classArgs__
	
	   __classArgs__ specifies the arguments of tuner algorithm. If the __builtinTunerName__ is in {__TPE__, __Random__, __Anneal__, __Evolution__}, user should set __optimize_mode__.
  * __codeDir__, __classFileName__, __className__ and __classArgs__
      * __codeDir__
        
		__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__
    
	  __gpuNum__ specifies the gpu number to run the tuner process. The value of this field should be a positive number.
	  
	    Note: users could only specify one way to set tuner, for example, set {tunerName, optimizationMode} or {tunerCommand, tunerCwd}, and could not set them both. 

* __assessor__
 
  * Description
  
    __assessor__ specifies the assessor algorithm to run an experiment, there are two kinds of ways to set assessor. One way is to use assessor provided by nni sdk, users need to set __builtinAssessorName__ and __classArgs__. Another way is to use users' own tuner file, and need to set __codeDirectory__, __classFileName__, __className__ and __classArgs__.
  * __builtinAssessorName__ and __classArgs__
    * __builtinAssessorName__
    
	  __builtinAssessorName__ specifies the name of system assessor, 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__
        
		__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__
    
	__gpuNum__ specifies the gpu number to run the assessor process. The value of this field should be a positive number.

        Note: users' could only specify one way to set assessor, for example,set {assessorName, optimizationMode} or {assessorCommand, assessorCwd}, and users could not set them both.If users do not want to use assessor, assessor fileld should leave to empty. 
* __trial(local, remote)__
  * __command__

      __command__  specifies the command to run trial process.
  * __codeDir__
    
	  __codeDir__ specifies the directory of your own trial file.
  * __gpuNum__
    
	  __gpuNum__ specifies the num of gpu to run the trial process. Default value is 0. 

* __trial(pai)__
  * __command__

      __command__  specifies the command to run trial process.
  * __codeDir__
    
	  __codeDir__ specifies the directory of the own trial file.
  * __gpuNum__
    
	  __gpuNum__ specifies the num of gpu to run the trial process. Default value is 0.
  * __cpuNum__

    __cpuNum__ is the cpu number of cpu to be used in pai container.
  * __memoryMB__

    __memoryMB__ set the momory size to be used in pai's container.
  
  * __image__

    __image__ set the image to be used in pai.

  * __dataDir__

    __dataDir__ is the data directory in hdfs to be used.
  
  * __outputDir__
   
    __outputDir__ is the output directory in hdfs to be used in pai, the stdout and stderr files are stored in the directory after job finished.
  


* __trial(kubeflow)__
  
  * __codeDir__
    
    __codeDir__ is the local directory where the code files in.
  
  * __ps(optional)__
    
    __ps__ is the configuration for kubeflow's tensorflow-operator. 
    * __replicas__
      
      __replicas__ is the replica number of __ps__ role.
    
    * __command__
      
      __command__ is the run script in __ps__'s container.
    
    * __gpuNum__
     
      __gpuNum__ set the gpu number to be used in __ps__ container.
    
    * __cpuNum__
    
      __cpuNum__ set the cpu number to be used in __ps__ container.
    
    * __memoryMB__
      
      __memoryMB__ set the memory size of the container.
    
    * __image__
      
      __iamge__ set the image to be used in __ps__.

  * __worker__
    
    __worker__ is the configuration for kubeflow's tensorflow-operator. 
    * __replicas__
      
      __replicas__ is the replica number of __worker__ role.
    
    * __command__
      
      __command__ is the run script in __worker__'s container.
    
    * __gpuNum__
     
      __gpuNum__ set the gpu number to be used in __worker__ container.
    
    * __cpuNum__
    
      __cpuNum__ set the cpu number to be used in __worker__ container.
    
    * __memoryMB__
      
      __memoryMB__ set the memory size of the container.
    
    * __image__
      
      __iamge__ set the image to be used in __worker__.



* __machineList__
 
     __machineList__ should be set if users set __trainingServicePlatform__=remote, or it could be empty.
  * __ip__
    
	__ip__ is the ip address of remote machine.
  * __port__
    
	__port__ is the ssh port to be used to connect machine.
	
	    Note: if users set port empty, the default value will be 22.
  * __username__
    
	__username__ is the account of remote machine.
  * __passwd__
    
	__passwd__ specifies the password of the account.

  * __sshKeyPath__

    If users use ssh key to login remote machine, could set __sshKeyPath__ in config file. __sshKeyPath__ is the path of ssh key file, which should be valid.
	
	    Note: if users set passwd and sshKeyPath simultaneously, nni will try passwd.
		
  * __passphrase__

    __passphrase__ is used to protect ssh key, which could be empty if users don't have passphrase.

* __kubeflowConfig__:
  
  * __operator__
    
    __operator__ specify the kubeflow's operator to be used, nni support __tf-operator__ in current version.
  
  * __storage__
   
    __storage__ specify the storage type of kubeflow, including {__nfs__, __azureStorage__}. This field is optional, and the default value is __nfs__. If the config use azureStorage, this field must be completed.
  
  * __nfs__
    
    __server__ is the host of nfs server

    __path__ is the mounted path of nfs
  
  * __keyVault__
    
    If users want to use azure kubernetes service, they should set keyVault to storage the private key of your azure storage account. Refer: https://docs.microsoft.com/en-us/azure/key-vault/key-vault-manage-with-cli2

    * __vaultName__

      __vaultName__ is the value of ```--vault-name``` used in az command.

    * __name__

      __name__ is the value of ```--name``` used in az command.

  * __azureStorage__
    
    If users use azure kubernetes service, they should set azure storage account to store code files.

    * __accountName__
     
      __accountName__ is the name of azure storage account.

    * __azureShare__
      
      __azureShare__ is the share of the azure file storage.

* __paiConfig__

  * __userName__
    
    __userName__ is the user name of your pai account.

  * __password__
    
    __password__ is the password of the pai account.
  
  * __host__
    
    __host__ is the host of pai.
    
    

        
## Examples
* __local mode__

  If users want to run trial jobs in local machine, and use annotation to generate search space, could use the following config:
```
authorName: test
experimentName: test_experiment
trialConcurrency: 3
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai, kubeflow
trainingServicePlatform: local
#choice: true, false
useAnnotation: true
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
  gpuNum: 0
trial:
  command: python3 mnist.py
  codeDir: /nni/mnist
  gpuNum: 0
```

  Could add assessor configuration in config file if set assessor.
```
authorName: test
experimentName: test_experiment
trialConcurrency: 3
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai, kubeflow
trainingServicePlatform: local
searchSpacePath: /nni/search_space.json
#choice: true, false
useAnnotation: false
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
  gpuNum: 0
assessor:
  #choice: Medianstop
  builtinAssessorName: Medianstop
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
  gpuNum: 0
trial:
  command: python3 mnist.py
  codeDir: /nni/mnist
  gpuNum: 0
```

  Or you could specify your own tuner and assessor file as following:
```
authorName: test
experimentName: test_experiment
trialConcurrency: 3
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai, kubeflow
trainingServicePlatform: local
searchSpacePath: /nni/search_space.json
#choice: true, false
useAnnotation: false
tuner:
  codeDir: /nni/tuner
  classFileName: mytuner.py
  className: MyTuner
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
  gpuNum: 0
assessor:
  codeDir: /nni/assessor
  classFileName: myassessor.py
  className: MyAssessor
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
  gpuNum: 0
trial:
  command: python3 mnist.py
  codeDir: /nni/mnist
  gpuNum: 0
```

* __remote mode__

If run trial jobs in remote machine, users could specify the remote mahcine information as fllowing format:
```
authorName: test
experimentName: test_experiment
trialConcurrency: 3
maxExecDuration: 1h
maxTrialNum: 10
#choice: local, remote, pai, kubeflow
trainingServicePlatform: remote
searchSpacePath: /nni/search_space.json
#choice: true, false
useAnnotation: false
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
  gpuNum: 0
trial:
  command: python3 mnist.py
  codeDir: /nni/mnist
  gpuNum: 0
#machineList can be empty if the platform is local
machineList:
  - ip: 10.10.10.10
    port: 22
    username: test
    passwd: test
  - ip: 10.10.10.11
    port: 22
    username: test
    passwd: test
  - ip: 10.10.10.12
    port: 22
    username: test
    sshKeyPath: /nni/sshkey
    passphrase: qwert
```

* __pai mode__

```
authorName: test
experimentName: nni_test1
trialConcurrency: 1
maxExecDuration:500h
maxTrialNum: 1
#choice: local, remote, pai, kubeflow
trainingServicePlatform: pai
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: false
tuner:
  #choice: TPE, Random, Anneal, Evolution, BatchTuner
  #SMAC (SMAC should be installed through nnictl)
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
trial:
  command: python3 main.py 
  codeDir: .
  gpuNum: 4
  cpuNum: 2
  memoryMB: 10000
  #The docker image to run nni job on pai
  image: msranni/nni:latest
  #The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
  dataDir: hdfs://10.11.12.13:9000/test
  #The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
  outputDir: hdfs://10.11.12.13:9000/test
paiConfig:
  #The username to login pai
  userName: test
  #The password to login pai
  passWord: test
  #The host of restful server of pai
  host: 10.10.10.10

```

* __kubeflow mode__

kubeflow use nfs as storage.

```
authorName: default
experimentName: example_mni
trialConcurrency: 1
maxExecDuration: 1h
maxTrialNum: 1
#choice: local, remote, pai, kubeflow
trainingServicePlatform: kubeflow
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: false
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
trial:
  codeDir: .
  worker:
    replicas: 1
    command: python3 mnist.py
    gpuNum: 0
    cpuNum: 1
    memoryMB: 8192
    image: msranni/nni:latest
kubeflowConfig:
  operator: tf-operator
  nfs:
    server: 10.10.10.10
    path: /var/nfs/general
```
kubeflow use azure storage
```
authorName: default
experimentName: example_mni
trialConcurrency: 1
maxExecDuration: 1h
maxTrialNum: 1
#choice: local, remote, pai, kubeflow
trainingServicePlatform: kubeflow
searchSpacePath: search_space.json
#choice: true, false
useAnnotation: false
#nniManagerIp: 10.10.10.10
tuner:
  #choice: TPE, Random, Anneal, Evolution
  builtinTunerName: TPE
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize
assessor:
  builtinAssessorName: Medianstop
  classArgs:
    optimize_mode: maximize
  gpuNum: 0
trial:
  codeDir: .
  worker:
    replicas: 1
    command: python3 mnist.py
    gpuNum: 0
    cpuNum: 1
    memoryMB: 4096
    image: msranni/nni:latest
kubeflowConfig:
  operator: tf-operator
  keyVault:
    vaultName: Contoso-Vault
    name: AzureStorageAccountKey
  azureStorage:
    accountName: storage
    azureShare: share01
```