@@ -5,6 +5,7 @@ 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:
...
...
@@ -34,7 +35,9 @@ machineList:
username:
passwd:
```
* __Use Assessor__
```
authorName:
experimentName:
...
...
@@ -71,7 +74,9 @@ machineList:
username:
passwd:
```
* __Use Annotation__
```
authorName:
experimentName:
...
...
@@ -107,6 +112,7 @@ machineList:
username:
passwd:
```
## Configuration
* __authorName__
* Description
...
...
@@ -207,27 +213,27 @@ machineList:
* 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__.
__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 assessor 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__}
__builtinAssessorName__ specifies the name of system assessor, nni sdk provides one kind of assessor {__Medianstop__}
* __classArgs__
__classArgs__ specifies the arguments of tuner algorithm
__classArgs__ specifies the arguments of assessor algorithm
* __codeDir__, __classFileName__, __className__ and __classArgs__
* __codeDir__
__codeDir__ specifies the directory of tuner code.
__codeDir__ specifies the directory of assessor code.
* __classFileName__
__classFileName__ specifies the name of tuner file.
__classFileName__ specifies the name of assessor file.
* __className__
__className__ specifies the name of tuner class.
__className__ specifies the name of assessor class.
* __classArgs__
__classArgs__ specifies the arguments of tuner algorithm.
__classArgs__ specifies the arguments of assessor algorithm.
* __gpuNum__
__gpuNum__ specifies the gpu number to run the assessor process. The value of this field should be a positive number.
...
...
@@ -306,7 +312,7 @@ machineList:
* __image__
__iamge__ set the image to be used in __ps__.
__image__ set the image to be used in __ps__.
* __worker__
...
...
@@ -333,7 +339,7 @@ machineList:
* __image__
__iamge__ set the image to be used in __worker__.
__image__ set the image to be used in __worker__.
...
...
@@ -426,6 +432,7 @@ machineList:
* __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
...
...
@@ -450,6 +457,7 @@ trial:
```
Could add assessor configuration in config file if set assessor.
```
authorName: test
experimentName: test_experiment
...
...
@@ -482,6 +490,7 @@ trial:
```
Or you could specify your own tuner and assessor file as following:
```
authorName: test
experimentName: test_experiment
...
...
@@ -518,6 +527,7 @@ trial:
* __remote mode__
If run trial jobs in remote machine, users could specify the remote mahcine information as fllowing format:
