api.views package

Submodules

api.views.aug_nlp_conf module

class api.views.aug_nlp_conf.AugNlpConf(**kwargs)

Bases: rest_framework.views.APIView

post(request, nnid, ver)

Augment text data with pattern and dict — # Class Name : AugNlpConf

# Description:

This Rest API support to augment train data with pattern and dict, this will generate real type of data for sequence labeling, wordembedding and supervised learning

api.views.chatbot_build_manager module

class api.views.chatbot_build_manager.ChatbotBuildManager(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=Field(name='parm3', required=True, location='', schema=None, description='haha', type=<class 'str'>, example=None), description=None, type=None, example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

post(request, type)

ChatBot Build API — # Class Name : ChatbotBuildManager

# Description:

Build chatbot process include create chatbotId, StoryBoard, NeuralNet IDS (used on chatbot process) This is a necessary step to use chatbot, you have to define all parms for chatbot before use it

put(request)

Your docs — # Class Name (must be separated by —)

# Description:

• name: name description: Foobar long description goes here

api.views.chatbot_service_manager module

class api.views.chatbot_service_manager.ChatbotServiceManager(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm3', required=True, location='', schema=Field(name='parm3', required=True, location='', schema=None, description='haha', type='float', example=None), description=None, type=None, example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

post(request, cbid)

Your docs — # Class Name (must be separated by —)

# Description:

• name: name description: Foobar long description goes here

put(request, cbid)

Your docs — # Class Name (must be separated by —)

# Description:

• name: name description: Foobar long description goes here

api.views.common_nninfo_batch module

class api.views.common_nninfo_batch.CommonNNInfoBatch(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

get(request, nnid, ver)

Your docs — # Class Name (must be separated by —)

# Description:

• name: name description: Foobar long description goes here

put(request, nnid, ver)

Your docs — # Class Name (must be separated by —)

# Description:

• name: name description: Foobar long description goes here

api.views.common_nninfo_list module

class api.views.common_nninfo_list.CommonNNInfoList(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='nn_id', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='biz_cate', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='biz_sub_cate', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='nn_title', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='nn_desc', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='use_flag', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='dir', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='config', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

Common Network Info Delete Method — # Class Name : CommonNNInfoList

# Description:

Structure : <nninfo> - version - batch version Delete selected neuralnetwork from list (careful cannot be undo) Delete all related info list version, batch, model and etc

get(request, nnid)

Common Network Info Get Method — # Class Name : CommonNNInfoList

# Description:

Structure : <nninfo> - version - batch version Search deinfed list of neural networks

post(request, nnid)

Common Network Info Post Method — # Class Name : CommonNNInfoList

# Description:

Structure : <nninfo> - version - batch version Define new neural network process

put(request, nnid)

Common Network Info Put Method — # Class Name : CommonNNInfoList

# Description:

Structure : <nninfo> - version - batch version Modifiy already defined neuralnetwork info

api.views.common_nninfo_version module

class api.views.common_nninfo_version.CommonNNInfoVersion(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='nn_def_list_info_nn_id', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='nn_wf_ver_info', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='condition', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='active_flag', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

Common Network Version Info Delete Method — # Class Name : CommonNNInfoVersion

# Description:

Structure : nninfo - <version> - batch version delete selected network info and related data

get(request, nnid)

Common Network Version Info Get Method — # Class Name : CommonNNInfoVersion

# Description:

Structure : nninfo - <version> - batch version Get version information of selected nnid

post(request, nnid)

Common Network Version Info Post Method — # Class Name : CommonNNInfoVersion

# Description:

Structure : nninfo - <version> - batch version need to define version info under network definition

put(request, nnid)

Common Network Version Info Delete Method — # Class Name : CommonNNInfoVersion

# Description:

Structure : nninfo - <version> - batch version Modify seleted nnid’s information

api.views.common_server_restart module

class api.views.common_server_restart.CommonServerRestart(**kwargs)

Bases: rest_framework.views.APIView

post(request)

Request to restart all nginx threads — # Class Name : CommonServerRestart

# Description:

restart nginx threads via rest api

api.views.conf_server_data module

class api.views.conf_server_data.ConfServerData(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

Manage Server info (master, slave, service and data servers) — # Class Name : ConfServerData

# Description:

create, remove, update, delete server info via rest api

get(request, nnid)

Manage Server info (master, slave, service and data servers) — # Class Name : ConfServerData

# Description:

create, remove, update, delete server info via rest api

post(request, nnid)

Manage Server info (master, slave, service and data servers) — # Class Name : ConfServerData

# Description:

create, remove, update, delete server info via rest api

put(request, nnid)

Manage Server info (master, slave, service and data servers) — # Class Name : ConfServerData

# Description:

create, remove, update, delete server info via rest api

api.views.file_upload_view module

class api.views.file_upload_view.FileUploadView(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='file', required=True, location='', schema=None, description=None, type='string', example=None),)

delete(request, nnid, ver, dir)

File Management Rest Service — # Class Name : FileUploadView

# Description:

delete selected upload file

get(request, nnid, ver, dir, type=None)

File Management Rest Service — # Class Name : FileUploadView

# Description:

Get file counts or file name of given network id and version

post(request, nnid, ver, dir, type=None)

File Management Rest Service — # Class Name : FileUploadView

# Description:

upload actual file via rest api

put(request, nnid, ver, dir)

File Management Rest Service — # Class Name : FileUploadView

# Description:

Get file counts or file name of given network id and version

api.views.resultmanager_default module

class api.views.resultmanager_default.ResultManagerDefault(**kwargs)

Bases: rest_framework.views.APIView

get(request, nnid, ver)

Trained model test result — # Class Name : ResultManagerDefault

# Description:

train result management (accuracy, loss, test result and etc)

api.views.rule_cate_list module

class api.views.rule_cate_list.RuleCateList(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

Manage simple rules for framework — # Class Name : RuleCateList

# Description:

Manage simple rules in a dict format Provide CRDU method for rules

get(request, nnid)

Manage simple rules for framework — # Class Name : RuleCateList

# Description:

Manage simple rules in a dict format Provide CRDU method for rules

post(request, nnid)

Manage simple rules for framework — # Class Name : RuleCateList

# Description:

Manage simple rules in a dict format Provide CRDU method for rules

put(request, nnid)

Manage simple rules for framework — # Class Name : RuleCateList

# Description:

Manage simple rules in a dict format Provide CRDU method for rules

api.views.runmanager_auto_conf module

class api.views.runmanager_auto_conf.RunManagerAutoConf(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='netconf_node', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='datasrc', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='evaldata', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='evalnode', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoConf

# Description:

Delete hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

get(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoConf

# Description:

Get hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

post(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoConf

# Description:

Set hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

put(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoConf

# Description:

Modifiy hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

api.views.runmanager_auto_parm module

class api.views.runmanager_auto_parm.RunManagerAutoParm(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='generation', required=True, location='', schema=None, description=None, type='int', example=None), Field(name='population', required=True, location='', schema=None, description=None, type='int', example=None), Field(name='survive', required=True, location='', schema=None, description=None, type='float', example=None))

delete(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML (<- for this step)
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoParm

# Description:

Delete hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

get(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML (<- for this step)
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoParm

# Description:

Get hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

post(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML (<- for this step)
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoParm

# Description:

Set hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

put(request, nnid)

Manage hyperparameter for GA algorithm like eval, population, survive etc

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML (<- for this step)
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoParm

# Description:

Modifiy hyperparameters for genetic algorithm itself, if it is not set Genetic Algorithm will run with default parmas

api.views.runmanager_auto_rule module

class api.views.runmanager_auto_rule.RunManagerAutoRule(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='netconf_node', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='datasrc', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='evaldata', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='evalnode', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, graph_id)

Manage AutoML define information include (id, desc and etc )

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition (<- for this step)
2. Select Type of Data (<- for this step)
3. Select Type of Anal algorithm (<- for this step)
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoRule

# Description:

Delete AutoML description include hyperparameters for deep learning algorithms This will remove all realted information (nn_id, ver, batch and model)

get(request, graph_id)

Manage AutoML define information include (id, desc and etc )

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition (<- for this step)
2. Select Type of Data (<- for this step)
3. Select Type of Anal algorithm (<- for this step)
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoRule

# Description:

Get AutoML description include hyperparameters for deep learning algorithms

post(request, graph_id)

Manage AutoML define information include (id, desc and etc )

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition (<- for this step)
2. Select Type of Data (<- for this step)
3. Select Type of Anal algorithm (<- for this step)
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoRule

# Description:

Set AutoML description, This is necessary process if you want to use automatically tune hyperparameters for deep learning algorithms

put(request, graph_id)

Manage AutoML define information include (id, desc and etc )

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition (<- for this step)
2. Select Type of Data (<- for this step)
3. Select Type of Anal algorithm (<- for this step)
4. Select range of hyper parameters (<- for this step)
5. Run - AutoML
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoRule

# Description:

Modify AutoML description include hyperparameters for deep learning algorithms

api.views.runmanager_auto_stat module

class api.views.runmanager_auto_stat.RunManagerAutoStat(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='version', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='batch', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

Manage AutoML define information include (id, desc and etc )

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML
6. Check result of each generation with UI/UX (<- for this step)
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoStat

# Description:

Delete selected set of populations

get(request, nnid)

Manage AutoML define information include (id, desc and etc )

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML
6. Check result of each generation with UI/UX (<- for this step)
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoStat

# Description:

Get Result of Train for each population

put(request, nnid)

Manage AutoML define information include (id, desc and etc )

Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch)

1. Define AutoML Graph definition
2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML
6. Check result of each generation with UI/UX (<- for this step)
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoStat

# Description:

Set flags on each population

api.views.runmanager_auto_train module

class api.views.runmanager_auto_train.RunManagerAutoTrain(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='flag', required=True, location='', schema=None, description=None, type='string', example=None),)

delete(request, nnid)

Your docs — # Class Name (must be separated by —)

# Description:

• name: name description: Foobar long description goes here

get(request, nnid)

Bellow is the process of running automl on our framework (1) Define AutoML Graph definition

2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML (<- for this step)
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoTrain

# Description:

get status of selected autol ml id

post(request, nnid)

Bellow is the process of running automl on our framework (1) Define AutoML Graph definition

2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML (<- for this step)
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoTrain

# Description:

request train on selected automl id

put(request, nnid)

Bellow is the process of running automl on our framework (1) Define AutoML Graph definition

2. Select Type of Data
3. Select Type of Anal algorithm
4. Select range of hyper parameters
5. Run - AutoML (<- for this step)
6. Check result of each generation with UI/UX
7. Select Best model you want use and activate it

— # Class Name : RunManagerAutoTrain

# Description:

change status of automl id (like.. stop process while working..)

api.views.runmanager_history module

class api.views.runmanager_history.RunManagerHistory(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

Check History of models — # Class Name : RunManagerHistory

# Description:

manage history of training results

get(request, nnid)

Check History of models — # Class Name : RunManagerHistory

# Description:

manage history of training results

post(request, nnid)

Check History of models — # Class Name : RunManagerHistory

# Description:

manage history of training results

put(request, nnid)

Check History of models — # Class Name : RunManagerHistory

# Description:

manage history of training results

api.views.runmanager_schedule module

class api.views.runmanager_schedule.RunManagerSchedule(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='interval', required=True, location='', schema=None, description=None, type='time', example=None), Field(name='start', required=True, location='', schema=None, description=None, type='date', example=None), Field(name='end', required=True, location='', schema=None, description=None, type='date', example=None), Field(name='repeat', required=True, location='', schema=None, description=None, type='int', example=None))

delete(request, nnid)

Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update model automatically — # Class Name : RunManagerSchedule

# Description:

delete schedule params for selected nn_id

get(request, nnid)

Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update model automatically — # Class Name : RunManagerSchedule

# Description:

get schedule params for selected nn_id

post(request, nnid)

Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update model automatically — # Class Name : RunManagerSchedule

# Description:

create schedule params sets

put(request, nnid)

Training Job schedule management We have predefined set of graph flow.. on Scheduler you can set a schedule this graph flow run on exact time. (everyday 1pm, every 5hours ... somting like this) So that we can feed new data and update model automatically — # Class Name : RunManagerSchedule

# Description:

modify schedule params for selected nn_id

api.views.runmanager_single_request module

class api.views.runmanager_single_request.RunManagerSingleRequest(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='flag', required=True, location='', schema=None, description=None, type='string', example=None),)

get(request, nnid, ver, node)

We can execute single node with this api you must specify nnid, ver and node name for that purpose — # Class Name : RunManagerSingleRequest

# Description:

get status of single node (run, wait, done.. etc)

post(request, nnid, ver, node)

We can execute single node with this api you must specify nnid, ver and node name for that purpose — # Class Name : RunManagerSingleRequest

# Description:

request single node to be executed

put(request, nnid, ver, node)

We can execute single node with this api you must specify nnid, ver and node name for that purpose — # Class Name : RunManagerSingleRequest

# Description:

edit status of single node

api.views.runmanager_train_request module

class api.views.runmanager_train_request.RunManagerTrainRequest(**kwargs)

Bases: rest_framework.views.APIView

get(request, nnid, ver)

We can execute whole process from data extraction > data preprocessing > train model > eval Process of execute single graph flow is like bellow (1) Set Network Id

2. Set Version Id
3. Set set graph flow
4. Set each nodes params on graph
5. Run graph flow of certain version defined on (2) <– here .. this step
6. Service output model

— # Class Name : RunManagerTrainRequest

# Description:

get status of process (scheduled, active, reserved, done.. )

post(request, nnid, ver)

We can execute whole process from data extraction > data preprocessing > train model > eval Process of execute single graph flow is like bellow (1) Set Network Id

2. Set Version Id
3. Set set graph flow
4. Set each nodes params on graph
5. Run graph flow of certain version defined on (2) <– here .. this step
6. Service output model

— # Class Name : RunManagerTrainRequest

# Description:

execute all process at once

put(request, nnid, ver)

We can execute whole process from data extraction > data preprocessing > train model > eval Process of execute single graph flow is like bellow (1) Set Network Id

2. Set Version Id
3. Set set graph flow
4. Set each nodes params on graph
5. Run graph flow of certain version defined on (2) <– here .. this step
6. Service output model

— # Class Name : RunManagerTrainRequest

# Description:

Change status.. like stop processing task..

api.views.runmanager_workflow module

class api.views.runmanager_workflow.RunManagerWorkFlow(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

CRUD of workflow information — # Class Name : RunManagerWorkFlow

# Description:

CRUD of workflow information (not implemented yet)

get(request, nnid)

CRUD of workflow information — # Class Name : RunManagerWorkFlow

# Description:

CRUD of workflow information (not implemented yet)

post(request, nnid)

CRUD of workflow information — # Class Name : RunManagerWorkFlow

# Description:

CRUD of workflow information (not implemented yet)

put(request, nnid)

CRUD of workflow information — # Class Name : RunManagerWorkFlow

# Description:

CRUD of workflow information (not implemented yet)

api.views.service_manager module

class api.views.service_manager.ServiceManager(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

Set configurations for predict service — # Class Name : ServiceManager

# Description:

Set configurations for predict service (not implemented yet)

get(request, nnid)

Set configurations for predict service — # Class Name : ServiceManager

# Description:

Set configurations for predict service (not implemented yet)

post(request, nnid)

Set configurations for predict service — # Class Name : ServiceManager

# Description:

Set configurations for predict service (not implemented yet)

put(request, nnid)

Set configurations for predict service — # Class Name : ServiceManager

# Description:

Set configurations for predict service (not implemented yet)

api.views.service_manager_predict module

class api.views.service_manager_predict.ServiceManagerPredict(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='input_data', required=True, location='', schema=None, description=None, type='string', example=None),)

post(request, type, nnid, ver)

Request Deep Neural Network to predict result with given data

input formats can be varies on type of networks

but usually you can use it with parm input_data

— # Class Name : ServiceManagerPredict

# Description:

request predict service via rest service It caches the model and vectors on first request It may can take some time at first for caching, after than we can response the request within 1.0 sec

api.views.workflow_data_frame module

class api.views.workflow_data_frame.WorkFlowDataFrame(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='type', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_server', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_sql', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='preprocess', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles frame type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataFrame

# Description:

1. Delete part of data (seleted by user ) conditions or rows (use datamanager instead)
2. Reset Node parameters

get(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles frame type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataFrame

# Description:

1. Search real data from the source in range (use datamanager instead)
2. See Data Node Parameters for selected nnid

post(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles frame type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataFrame

# Description:

Set params for data source, preprocess method and etc

put(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles frame type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataFrame

# Description:

1. Modify selected data (use datamanager instead)
2. Modify params for data source, preprocess method and etc

api.views.workflow_data_image module

class api.views.workflow_data_image.WorkFlowDataImage(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='type', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_server', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_sql', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='preprocess', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataImage

# Description:

1. Delete part of data (seleted by user ) conditions or rows (use datamanager instead)
2. Reset Node parameters

get(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataImage

# Description:

1. Search real data from the source in range (use datamanager instead)
2. See Data Node Parameters for selected nnid

post(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataImage

# Description:

Set params for data source, preprocess method and etc

put(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataImage

# Description:

1. Modify selected data (use datamanager instead)
2. Modify params for data source, preprocess method and etc

api.views.workflow_data_iob module

class api.views.workflow_data_iob.WorkFlowDataIob(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='type', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_server', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_sql', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='preprocess', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataIob

# Description:

1. Delete part of data (seleted by user ) conditions or rows (use datamanager instead)
2. Reset Node parameters

get(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataIob

# Description:

1. Search real data from the source in range (use datamanager instead)
2. See Data Node Parameters for selected nnid

post(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataIob

# Description:

Set params for data source, preprocess method and etc

put(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles image type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataIob

# Description:

1. Modify selected data (use datamanager instead)
2. Modify params for data source, preprocess method and etc

api.views.workflow_data_reuse module

class api.views.workflow_data_reuse.WorkFlowDataReuse(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='node_id', required=True, location='', schema=None, description=None, type='string', example=None),)

get(request, nnid)

This API is for set node parameters

This node is for data extraction

This node especially handles reuse type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataReuse

# Description:

You can see how the dataset looks like (use datamanager instead) this will show youj reused data node id only

post(request, nnid)

This API is for set node parameters

This node is for data extraction

This node especially handles reuse type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataReuse

# Description:

Set params which data node to reuse

api.views.workflow_data_text module

class api.views.workflow_data_text.WorkFlowDataText(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='type', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_server', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='source_sql', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='preprocess', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles text type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataText

# Description:

1. Delete part of data (seleted by user ) conditions or rows (use datamanager instead)
2. Reset Node parameters

get(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles text type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataText

# Description:

1. Search real data from the source in range (use datamanager instead)
2. See Data Node Parameters for selected nnid

post(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles text type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataText

# Description:

Set params for data source, preprocess method and etc

put(request, src, form, prg, nnid, ver, node)

This API is for set node parameters

This node is for data extraction

This node especially handles text type data

You can set source server by set up parameters

— # Class Name : WorkFlowDataText

# Description:

1. Modify selected data (use datamanager instead)
2. Modify params for data source, preprocess method and etc

api.views.workflow_dataconf_frame module

class api.views.workflow_dataconf_frame.WorkFlowDataConfFrame(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete cnn configuration data

get(request, nnid, ver, node)

• desc : get cnn configuration data

post(request, nnid, ver, node)

• desc : insert cnn configuration data completed

put(request, nnid, ver, node)

• desc ; update cnn configuration data

api.views.workflow_dataconf_image module

class api.views.workflow_dataconf_image.WorkFlowDataConfImage(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

• desc : delete cnn configuration data

get(request, nnid)

• desc : get cnn configuration data

post(request, nnid)

• desc : insert cnn configuration data

put(request, nnid)

• desc ; update cnn configuration data

api.views.workflow_eval_conf module

class api.views.workflow_eval_conf.WorkFlowEvalConf(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='type', required=True, location='', schema=None, description=None, type='string', example=None),)

delete(request, nnid)

This API is for set node parameters

This node is for evaluation of train result

You can choose 3 diffrent kind of test method (n fold, random, extra test set)

— # Class Name : WorkFlowEvalConf

# Description:

reset evaluation node configurations

get(request, nnid)

This API is for set node parameters

This node is for evaluation of train result

You can choose 3 diffrent kind of test method (n fold, random, extra test set)

— # Class Name : WorkFlowEvalConf

# Description:

Get evaluation node configurations

post(request, nnid, ver)

This API is for set node parameters

This node is for evaluation of train result

You can choose 3 diffrent kind of test method (n fold, random, extra test set)

— # Class Name : WorkFlowEvalConf

# Description:

Set Test method and test data source

put(request, nnid, ver, node)

This API is for set node parameters

This node is for evaluation of train result

You can choose 3 diffrent kind of test method (n fold, random, extra test set)

— # Class Name : WorkFlowEvalConf

# Description:

modify evaluation node configurations

api.views.workflow_init_custom module

class api.views.workflow_init_custom.WorkFlowInitCustom(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitCustom

# Description:

still on development

get(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitCustom

# Description:

still on development

post(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitCustom

# Description:

still on development

put(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitCustom

# Description:

still on development

api.views.workflow_init_easy module

class api.views.workflow_init_easy.WorkFlowInitEasy(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitEasy

# Description:

still on development

get(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitEasy

# Description:

still on development

post(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitEasy

# Description:

still on development

put(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitEasy

# Description:

still on development

api.views.workflow_init_history module

class api.views.workflow_init_history.WorkFlowInitHistory(**kwargs)

Bases: rest_framework.views.APIView

delete(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitHistory

# Description:

still on development

get(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitHistory

# Description:

still on development

post(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitHistory

# Description:

still on development

put(request, nnid)

[Still on development] This will allow you to design your own graph path — # Class Name : WorkFlowInitHistory

# Description:

still on development

api.views.workflow_init_simple module

class api.views.workflow_init_simple.WorkFlowInitSimple(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='type', required=True, location='', schema=None, description=None, type='string', example=None),)

delete(request, nnid, wfver)

Simply initialize fixed graph flow which is preefined

You can choose process with network id and data type

There are several processes already designed

— # Class Name : WorkFlowInitSimple

# Description:

Delete all graph flow and relate information

get(request, nnid, wfver, desc)

Simply initialize fixed graph flow which is preefined

You can choose process with network id and data type

There are several processes already designed

— # Class Name : WorkFlowInitSimple

# Description:

Get graph flow information with given network id

post(request, nnid, wfver)

Simply initialize fixed graph flow which is preefined

You can choose process with network id and data type

There are several processes already designed

— # Class Name : WorkFlowInitSimple

# Description:

Set graph flow with given name and data type

api.views.workflow_menu_manager module

class api.views.workflow_menu_manager.WorkFlowMenuManager(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='wf_task_menu_id', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='wf_task_menu_name', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='wf_task_menu_desc', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='visible_flag', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request)

This API manages type of nodes

We have several type of node like bellow

1. Data Extraction node
2. Data View node
3. Data Preprocess node
4. Model configuration and train node
5. Model evaluation node
6. Model inference node

— # Class Name : WorkFlowMenuManager

# Description:

Delete node type information

get(request)

This API manages type of nodes

We have several type of node like bellow

1. Data Extraction node
2. Data View node
3. Data Preprocess node
4. Model configuration and train node
5. Model evaluation node
6. Model inference node

— # Class Name : WorkFlowMenuManager

# Description:

Search node type and status already registered

post(request)

This API manages type of nodes

We have several type of node like bellow

1. Data Extraction node
2. Data View node
3. Data Preprocess node
4. Model configuration and train node
5. Model evaluation node
6. Model inference node

— # Class Name : WorkFlowMenuManager

# Description:

Create new type of node if you want but add new category doesn’y mean you can use it (need to implement real logic)

put(request)

This API manages type of nodes

We have several type of node like bellow

1. Data Extraction node
2. Data View node
3. Data Preprocess node
4. Model configuration and train node
5. Model evaluation node
6. Model inference node

— # Class Name : WorkFlowMenuManager

# Description:

Modify node type information

api.views.workflow_netconf_autoencoder module

class api.views.workflow_netconf_autoencoder.WorkFlowNetConfAutoEncoder(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='learning_rate', required=True, location='', schema=None, description=None, type='float', example=None), Field(name='iter', required=True, location='', schema=None, description=None, type='int', example=None), Field(name='batch_size', required=True, location='', schema=None, description=None, type='int', example=None), Field(name='examples_to_show', required=True, location='', schema=None, description=None, type='10', example=None), Field(name='n_hidden', required=True, location='', schema=None, description=None, type='list', example=None))

delete(request, nnid)

This API handles network node information

This is for Stacked AutoEncoder

We designed general form of Autoencoder

You can modify hyperparameters with rest api

— # Class Name : WorkFlowNetConfAutoEncoder

# Description:

Reset Network configuration for AutoEncoder

get(request, nnid)

This API handles network node information

This is for Stacked AutoEncoder

We designed general form of Autoencoder

You can modify hyperparameters with rest api

— # Class Name : WorkFlowNetConfAutoEncoder

# Description:

Get Network configuration for AutoEncoder

post(request, nnid)

This API handles network node information

This is for Stacked AutoEncoder

We designed general form of Autoencoder

You can modify hyperparameters with rest api

— # Class Name : WorkFlowNetConfAutoEncoder

# Description:

Set Network configuration for AutoEncoder

put(request, nnid, ver, node)

This API handles network node information

This is for Stacked AutoEncoder

We designed general form of Autoencoder

You can modify hyperparameters with rest api

— # Class Name : WorkFlowNetConfAutoEncoder

# Description:

Modify Network configuration for AutoEncoder

api.views.workflow_netconf_bilstmcrf module

class api.views.workflow_netconf_bilstmcrf.WorkFlowNetConfBiLstmCrf(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

• desc : delete data

get(request, nnid)

• desc : get data

post(request, nnid)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_netconf_cnn module

class api.views.workflow_netconf_cnn.WorkFlowNetConfCnn(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete data

get(request, nnid, ver, node)

• desc : get data

post(request, nnid, ver, node)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_netconf_d2v module

class api.views.workflow_netconf_d2v.WorkFlowNetConfD2V(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete data

get(request, nnid, ver, node)

• desc : get data

post(request, nnid, ver, node)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_netconf_fasttext module

class api.views.workflow_netconf_fasttext.WorkFlowNetConfFastText(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete cnn configuration data

get(request, nnid, ver, node)

• desc : get configuration data

post(request, nnid, ver, node)

• desc : insert configuration data

put(request, nnid, ver, node)

• desc ; update config data

api.views.workflow_netconf_predefined module

class api.views.workflow_netconf_predefined.WorkFlowNetConfPredefined(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

• desc : delete data

get(request, nnid)

• desc : get data

post(request, nnid)

• desc : insert data

put(request, nnid)

• desc ; update data

api.views.workflow_netconf_renet module

class api.views.workflow_netconf_renet.WorkFlowNetConfRenet(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete data

get(request, nnid, ver, node)

• desc : get data

post(request, nnid, ver, node)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_netconf_rnn module

class api.views.workflow_netconf_rnn.WorkFlowNetConfRnn(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

• desc : delete data

get(request, nnid)

• desc : get data

post(request, nnid)

• desc : insert data

put(request, nnid)

• desc ; update data

api.views.workflow_netconf_seq2seq module

class api.views.workflow_netconf_seq2seq.WorkFlowNetConfSeq2Seq(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete data

get(request, nnid, ver, node)

• desc : get data

post(request, nnid, ver, node)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_netconf_w2v module

class api.views.workflow_netconf_w2v.WorkFlowNetConfW2V(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete data

get(request, nnid, ver, node)

• desc : get data

post(request, nnid, ver, node)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_netconf_wcnn module

class api.views.workflow_netconf_wcnn.WorkFlowNetConfWcnn(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete data

get(request, nnid, ver, node)

• desc : get data

post(request, nnid, ver, node)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_netconf_wdnn module

class api.views.workflow_netconf_wdnn.WorkFlowNetConfWdnn(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node)

• desc : delete data

get(request, nnid, ver, node)

• desc : get data

post(request, nnid, ver, node)

• desc : insert data

put(request, nnid, ver, node)

• desc ; update data

api.views.workflow_node_manager module

class api.views.workflow_node_manager.WorkFlowNodeManager(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

• desc : delete data

get(request, nnid)

• desc : get data

post(request, nnid)

• desc : insert data

put(request, nnid)

• desc ; update data

api.views.workflow_pre_feeder module

class api.views.workflow_pre_feeder.WorkFlowPreFeeder(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, src, net, nnid, ver, node)

• desc : delete data

get(request, src, net, nnid, ver, node)

• desc : get data

post(request, src, net, nnid, ver, node)

• desc : insert data

put(request, src, net, nnid, ver, node)

• desc ; update data

api.views.workflow_pre_merge module

class api.views.workflow_pre_merge.WorkFlowPreMerge(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid, ver, node, type)

• desc : delete data

get(request, nnid, ver, node, type)

• desc : get data

post(request, nnid, ver, node, type)

• desc : insert data

put(request, nnid, ver, node, type)

• desc ; update data

api.views.workflow_pre_predict module

class api.views.workflow_pre_predict.WorkFlowPrePredict(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

• desc : delete data

get(request, nnid)

• desc : get data

post(request, nnid)

• desc : insert data

put(request, nnid)

• desc ; update data

api.views.workflow_state_manager module

class api.views.workflow_state_manager.WorkFlowStateManager(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, nnid)

• desc : delete data

get(request, nnid)

• desc : get data

post(request, nnid)

• desc : insert data

put(request, nnid)

• desc ; update data

api.views.workflow_submenu_manager module

class api.views.workflow_submenu_manager.WorkFlowSubMenuManager(**kwargs)

Bases: rest_framework.views.APIView

coreapi_fields = (Field(name='parm1', required=True, location='', schema=None, description=None, type='string', example=None), Field(name='parm2', required=True, location='', schema=None, description=None, type='string', example=None))

delete(request, menu)

• desc : delete data

get(request, menu)

• desc : get data

post(request, menu)

• desc : insert data

put(request, menu)

• desc ; update data

Module contents