**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:
  builtinTunerName: TPE
  classArgs:
    optimize_mode: maximize
assessor:
  builtinAssessorName: Medianstop
  classArgs:
    optimize_mode: maximize
trial:
  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: `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:
```
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:
  # Possible values: TPE, Random, Anneal, Evolution
  builtinTunerName: TPE
  classArgs:
    optimize_mode: maximize
assessor:
  # 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:
  command: python mnist.py
  codeDir: /usr/share/nni/examples/trials/mnist-annotation
  gpuNum: 0
```
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.