def get(self, request, graph_id):
"""
Manage AutoML define information include (id, desc and etc ) \n
Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch) \n
(1) Define AutoML Graph definition (<- for this step) \n
(2) Select Type of Data (<- for this step)\n
(3) Select Type of Anal algorithm (<- for this step)\n
(4) Select range of hyper parameters (<- for this step)\n
(5) Run - AutoML \n
(6) Check result of each generation with UI/UX \n
(7) Select Best model you want use and activate it \n
---
# Class Name : RunManagerAutoRule
# Description:
Get AutoML description include hyperparameters for deep learning algorithms
"""
try:
if graph_id.isdigit() == True :
return_data = AutoMlRule().get_graph_type_list(graph_id)
elif graph_id is not None :
return_data = AutoMlRule().get_graph_info(graph_id)
else :
raise Exception("no vailed graph_id")
return Response(json.dumps(return_data))
except Exception as e:
return_data = {"status": "404", "result": str(e)}
return Response(json.dumps(return_data))
def get(self, request, graph_id):
"""
- desc : get cnn configuration data
"""
try:
if (graph_id == 'all'):
return_data = AutoMlRule().get_graph_type_list()
elif (graph_id is not None):
return_data = AutoMlRule().get_graph_info(graph_id)
else:
raise Exception("no vailed graph_id")
return Response(json.dumps(return_data))
except Exception as e:
return_data = {"status": "404", "result": str(e)}
return Response(json.dumps(return_data))
def put(self, request, graph_id):
"""
- desc ; update cnn configuration data
"""
try:
return_data = AutoMlRule().update_graph_type_list(
graph_id, request.data)
return Response(json.dumps(return_data))
except Exception as e:
return_data = {"status": "404", "result": str(e)}
return Response(json.dumps(return_data))
def create_workflow(self, nn_id, wf_ver, type):
"""
create workflow base node info
:param nn_id:
:return:
"""
# crate state info with nn_id & workflow version
input_data = {}
input_data['nn_id'] = str(nn_id)
input_data['nn_wf_ver_id'] = str(wf_ver)
input_data['wf_state_id'] = str(nn_id) + "_" + str(wf_ver)
state_id = self._create_workflow_state(input_data)
#todo 나중에 꼭 지울것!!!!!!!! 임시방편 it is temporary solution
if (type == 'frame'):
type = 'wdnn'
# create nodes fit to requested type (img, text, frame)
net_node = AutoMlRule().get_graph_info(type, "all")
self.set_node_name(net_node)
if (type == 'cnn'):
self._create_predefined_nodes_cnn(state_id)
elif (type == 'resnet'):
self._create_predefined_nodes_renet(state_id)
elif (type == 'frame' or type == "wdnn" or type == "dnn"):
self._create_predefined_nodes_frame(state_id)
elif (type == 'keras_frame' or type == "wdnn_keras"):
self._create_predefined_nodes_keras_frame(state_id)
elif (type == 'word2vec'):
self._create_predefined_nodes_word2vec(state_id)
elif (type == 'word2vec_frame'):
self._create_predefined_nodes_word2vec_frame(state_id)
elif (type == 'doc2vec'):
self._create_predefined_nodes_doc2vec(state_id)
elif (type == 'wcnn'):
self._create_predefined_nodes_wcnn_frame(state_id)
elif (type == 'seq2seq'):
self._create_predefined_nodes_seq2seq(state_id)
elif (type == 'seq2seq_csv'):
self._create_predefined_nodes_seq2seq_csv(state_id)
elif (type == 'autoencoder_img'):
self._create_predefined_nodes_autoencoder_img(state_id)
elif (type == 'autoencoder_csv'):
self._create_predefined_nodes_autoencoder_csv(state_id)
elif (type == 'bilstmcrf_iob'):
self._create_predefined_nodes_bilstmcrf_iob(state_id)
elif (type == 'fasttext_txt'):
self._create_predefined_nodes_fasttext_txt(state_id)
else:
raise Exception("Error : Not defined type (" + type + ")")
return type
def get(self, request, graph_id):
"""
Your docs
---
# Class Name (must be separated by `---`)
# Description:
- name: name
description: Foobar long description goes here
"""
try:
if (graph_id == 'all'):
return_data = AutoMlRule().get_graph_type_list()
elif (graph_id is not None):
return_data = AutoMlRule().get_graph_info(graph_id)
else:
raise Exception("no vailed graph_id")
return Response(json.dumps(return_data))
except Exception as e:
return_data = {"status": "404", "result": str(e)}
return Response(json.dumps(return_data))
def get_nn_node_name(self, nn_id):
"""
search all node name info
:param nn_id: neural network definition id
:return: sql
"""
try :
condition = {}
condition['nn_id'] = nn_id
nninfo = NNCommonManager().get_nn_info(condition)
type = nninfo[0]['dir']
net_node = AutoMlRule().get_graph_info(type, "all")
graph_id = net_node[0]['fields']["graph_flow_info_id"]
graph = WorkFlowStateMenu().get_graph_info(graph_id)
return graph
except Exception as e :
return []
def put(self, request, graph_id):
"""
Your docs
---
# Class Name (must be separated by `---`)
# Description:
- name: name
description: Foobar long description goes here
"""
try:
return_data = AutoMlRule().update_graph_type_list(
graph_id, request.data)
return Response(json.dumps(return_data))
except Exception as e:
return_data = {"status": "404", "result": str(e)}
return Response(json.dumps(return_data))
def post(self, request, graph_id):
"""
Manage AutoML define information include (id, desc and etc ) \n
Structure : AutoML - NetID - NetVer(Auto Generated by GA) - NetBatch (auto generated on every batch) \n
(1) Define AutoML Graph definition (<- for this step) \n
(2) Select Type of Data (<- for this step)\n
(3) Select Type of Anal algorithm (<- for this step)\n
(4) Select range of hyper parameters (<- for this step)\n
(5) Run - AutoML \n
(6) Check result of each generation with UI/UX \n
(7) Select Best model you want use and activate it \n
---
# Class Name : RunManagerAutoRule
# Description:
Set AutoML description, This is necessary process if you want to use automatically tune
hyperparameters for deep learning algorithms
"""
try:
return_data = AutoMlRule().set_graph_type_list(graph_id, request.data)
return Response(json.dumps(return_data))
except Exception as e:
return_data = {"status": "404", "result": str(e)}
return Response(json.dumps(return_data))
def get(self, 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
"""
if (type != None):
# tmp 임시 저장소의 값을 만들어서 전달해줌.
if type.find("tmp") >= 0:
tmpfilepath = get_source_path(nnid, ver, "")
if type != None and type.find("store") >= 0:
tmpfilepath = get_store_path(nnid, ver, "")
fileName = time.strftime('%Y%m%d')
stand = "1".zfill(10)
stcnt = int(fileName + stand)
mxcnt = stcnt
for i in os.listdir(tmpfilepath):
try:
if int(i) >= mxcnt:
mxcnt = int(i) + 1
if int(i) < stcnt:
shutil.rmtree(tmpfilepath + i)
except:
None
mxcnt = str(mxcnt)
if not os.path.isdir(tmpfilepath + mxcnt):
os.mkdir(tmpfilepath + mxcnt)
return_data = {"path": mxcnt}
elif type.find("runcheck") >= 0:
return_data = []
graph = NNCommonManager().get_nn_node_name(nnid)
for net in graph:
if net['fields']['graph_node'] == 'netconf_data':
netconf_data = net['fields']['graph_node_name']
if net['fields']['graph_node'] == 'eval_data':
eval_data = net['fields']['graph_node_name']
source_path_n = get_source_path(nnid, ver, netconf_data)
source_path_e = get_source_path(nnid, ver, eval_data)
store_path_n = get_store_path(nnid, ver, netconf_data)
store_path_e = get_store_path(nnid, ver, eval_data)
source_n_cnt = len(os.listdir(source_path_n))
source_e_cnt = len(os.listdir(source_path_e))
store_n_cnt = len(os.listdir(store_path_n))
store_e_cnt = len(os.listdir(store_path_e))
condition = {}
condition['nn_id'] = nnid
dir = NNCommonManager().get_nn_info(condition)[0]['dir']
auto = AutoMlRule().get_graph_info(dir)
if auto[0]['fields'][
'train_file_path'] != '' and source_n_cnt == 0 and store_n_cnt == 0:
resub = {"filecnt": 0, "type": "Source"}
elif auto[0]['fields'][
'eval_file_path'] != '' and source_e_cnt == 0 and store_e_cnt == 0:
resub = {"filecnt": 0, "type": "Eval"}
else:
resub = {"filecnt": 1}
return_data.append(resub)
else:
return_data = []
tmpfilepath = get_source_path(nnid, ver, dir)
if type != None and type.find("store") >= 0:
tmpfilepath = get_store_path(nnid, ver, dir)
for i in os.listdir(tmpfilepath):
resub = {"filename": i}
return_data.append(resub)
else:
return_data = WorkFlowDataImage().get_data_node_info(
nnid, ver, dir)
return Response(json.dumps(return_data))