def action_dir():
'''
Returns a JSON with the list of spaces and versions
'''
# get de space repo path
spaces_path = pathlib.Path(utils.space_repository_path())
# get directories in space repo path
dirs = [x for x in spaces_path.iterdir() if x.is_dir()]
# if dir contains dev/ -> is space (NAIVE APPROACH)
# get last dir name [-1]: space name
space_dirs = [d.parts[-1] for d in dirs if list(d.glob('dev'))]
results = []
for ispace in space_dirs:
idict = {}
idict["spacename"] = ispace
versions = [0]
for iversion in os.listdir(utils.space_tree_path(ispace)):
if iversion.startswith('ver'):
versions.append(utils.modeldir2ver(iversion))
idict["versions"] = versions
results.append(idict)
# print (json.dumps(results))
return True, json.dumps(results)
def action_dir():
'''
Returns a list of models and versions
'''
# get de model repo path
models_path = pathlib.Path(utils.model_repository_path())
if models_path.is_dir() is False:
return False, 'the model repository path does not exist. Please run "flame -c config".'
# get directories in model repo path
dirs = [x for x in models_path.iterdir() if x.is_dir()]
# if dir contains dev/ -> is model (NAIVE APPROACH)
# get last dir name [-1]: model name
model_dirs = [d.parts[-1] for d in dirs if list(d.glob('dev'))]
label_defaults = {
'maturity': 'dev',
'type': 'unk',
'subtype': 'unk',
'endpoint': 'unk',
'species': 'unk'
}
results = []
for imodel in model_dirs:
idict = {}
idict["modelname"] = imodel
idict["version"] = 0
idict["info"] = action_info(imodel, 0, output=None)[1]
success, label = action_label(imodel, 0, oformat='object')
if success:
idict["label"] = label
else:
idict["label"] = label_defaults
results.append(idict)
for iversion in os.listdir(utils.model_tree_path(imodel)):
if iversion.startswith('ver'):
idict = {}
idict["modelname"] = imodel
idict["version"] = utils.modeldir2ver(iversion)
idict["info"] = action_info(imodel,
idict["version"],
output=None)[1]
success, label = action_label(imodel,
idict["version"],
oformat='object')
if success:
idict["label"] = label
else:
idict["label"] = label_defaults
results.append(idict)
# print (results)
return True, results
def action_dir():
'''
Returns a the list of spaces and versions
'''
# get de space repo path
spaces_path = pathlib.Path(utils.space_repository_path())
if spaces_path.is_dir() is False:
return False, 'the spaces repository path does not exist. Please run "flame -c config".'
# get directories in space repo path
dirs = [x for x in spaces_path.iterdir() if x.is_dir()]
# if dir contains dev/ -> is space (NAIVE APPROACH)
# get last dir name [-1]: space name
space_dirs = [d.parts[-1] for d in dirs if list(d.glob('dev'))]
# results = []
# for ispace in space_dirs:
# idict = {}
# idict ["spacename"] = ispace
# versions = [0]
# for iversion in os.listdir(utils.space_tree_path(ispace)):
# if iversion.startswith('ver'):
# versions.append(utils.modeldir2ver(iversion))
# idict ["versions"] = versions
# results.append(idict)
results = []
for ispace in space_dirs:
idict = {}
idict["spacename"] = ispace
idict["version"] = 0
idict["info"] = action_info(ispace, 0, output=None)[1]
results.append(idict)
for iversion in os.listdir(utils.space_tree_path(ispace)):
if iversion.startswith('ver'):
idict = {}
idict["spacename"] = ispace
idict["version"] = utils.modeldir2ver(iversion)
idict["info"] = action_info(ispace,
idict["version"],
output=None)[1]
results.append(idict)
# print (json.dumps(results))
return True, results
def action_report():
'''
Returns a list of models and the results of each one
'''
# get de model repo path
models_path = pathlib.Path(utils.model_repository_path())
# get directories in model repo path
dirs = [x for x in models_path.iterdir() if x.is_dir()]
# # if dir contains dev/ -> is model (NAIVE APPROACH)
# # get last dir name [-1]: model name
# model_dirs = [d.parts[-1] for d in dirs if list(d.glob('dev'))]
results = []
# iterate models
for d in dirs:
imodel_name = d.parts[-1]
imodel_vers = [x.parts[-1] for x in d.iterdir() if x.is_dir()]
# make sure the model contains 'dev' to recognize models
if 'dev' not in imodel_vers:
continue
imodel_vers_info = []
for ivtag in imodel_vers:
iver = utils.modeldir2ver(ivtag)
# now we have the model name and version, try to get the info
try:
isuccess, iresult = action_info(imodel_name,
iver,
output='bin')
except:
continue
if not isuccess:
continue
# build a tuple (version, object) for each version and append
imodel_vers_info.append((iver, iresult))
# build a tuple (model_name, [version_info]) for each model and append
results.append((imodel_name, imodel_vers_info))
return True, results
def action_refresh(model=None, version=None, GUI=False):
'''
Rebuild one or many models making use of existing parameter files and
locally stored training series.
'''
import flame.context as context
from flame.parameters import Parameters
# from flame.documentation import Documentation
import logging
if GUI:
token_file = os.path.join(tempfile.gettempdir(), 'refreshing_' + model)
# update token file with content 'working'
with open(token_file, 'w') as f:
f.write('Analyzing and sorting models...')
# list endpoints relevant for the arguments
if model is not None:
model_list = [model]
else:
model_root = pathlib.Path(utils.model_repository_path())
model_list = [x.stem for x in model_root.iterdir() if x.is_dir()]
# list versions relevant for the arguments
task_list = []
for imodel in model_list:
if version is not None:
task_list.append((imodel, version))
else:
model_root = pathlib.Path(utils.model_tree_path(imodel))
itask_list = [(imodel, utils.modeldir2ver(x.stem))
for x in model_root.iterdir() if x.is_dir()]
task_list += itask_list # use "+=" and not "append" to merge the new list with the old one
# analize task_list and add at the end ensemble models
# this is needed to have low models refreshed BEFORE refreshing the high models
# eliminating the need to refresh them recursively
LOG.info("Analyzing and sorting models...")
# make sure the lower models are in task_list and, if not, force the inclussion
for itask in task_list:
param = Parameters()
success, results = param.loadYaml(itask[0], itask[1])
if not success:
continue
if param.getVal('input_type') == 'model_ensemble':
ens_nams = param.getVal('ensemble_names')
ens_vers = param.getVal('ensemble_versions')
for i in range(len(ens_nams)):
iver = 0
inam = ens_nams[i]
if (i < len(ens_vers)):
iver = ens_vers[i]
if ((inam, iver)) not in task_list:
task_list.append((inam, iver))
# create separate lists for regular and ensemble models
# and add ensemble models at the end
# this needs to be carried out after the previos step because
# some of the lower level models could be an ensemble model
# itself
mol_list = []
ens_list = []
for itask in task_list:
param = Parameters()
success, results = param.loadYaml(itask[0], itask[1])
if not success:
mol_list.append(itask)
continue
if param.getVal('input_type') == 'model_ensemble':
ens_list.append(itask)
else:
mol_list.append(itask)
task_list = mol_list + ens_list
# show all models before stating
LOG.info(
"Starting model refreshing task for the following models and versions")
for itask in task_list:
LOG.info(f' model: {itask[0]} version: {itask[1]}')
LOG.info("This can take some time, please be patient...")
source_dir = os.path.dirname(os.path.abspath(__file__))
children_dir = os.path.join(source_dir, 'children')
master_parameters = os.path.join(children_dir, 'parameters.yaml')
master_documentation = os.path.join(children_dir, 'documentation.yaml')
# now send the build command for each task
for itask in task_list:
destinat_path = utils.model_path(itask[0], 0) # dev
if itask[1] != 0:
# move version to /dev for building
original_path = utils.model_path(itask[0], itask[1]) # veri
security_path = destinat_path + '_security' # dev_sec
shutil.move(destinat_path, security_path) # dev --> dev_sec
shutil.move(original_path, destinat_path) # veri --> dev
LOG.info(
f' refreshing model: {itask[0]} version: {itask[1]} ({task_list.index(itask)+1} of {len(task_list)})...'
)
if GUI:
with open(token_file, 'w') as f:
f.write(
f'model: {itask[0]} version: {itask[1]} ({task_list.index(itask)+1} of {len(task_list)})'
)
# dissable LOG output
logging.disable(logging.ERROR)
# update parameters
dump_parameters = os.path.join(destinat_path, 'parameters_dump.yaml')
success, param = action_parameters(itask[0], 0, oformat='bin')
if success:
param_yaml = param.dumpYAML()
with open(dump_parameters, 'w') as f:
for line in param_yaml:
f.write(line + '\n')
else:
LOG.info(
' ERROR: unable to merge parameters for model: {itask[0]} version: {itask[1]}'
)
dump_parameters = None
original_parameters = os.path.join(destinat_path, 'parameters.yaml')
shutil.copy(master_parameters, original_parameters)
#update documentation
dump_documentation = os.path.join(destinat_path,
'documentation_dump.yaml')
success, documentation = action_documentation(itask[0],
0,
doc_file=None,
oformat='bin')
original_documentation = os.path.join(destinat_path,
'documentation.yaml')
shutil.copy(master_documentation, original_documentation)
if success:
documentation_yaml = documentation.dumpYAML()
with open(dump_documentation, 'w') as f:
for line in documentation_yaml:
line = line.encode("ascii", "ignore")
line = line.decode("ascii", "ignore")
f.write(line + '\n')
s2, documentation = action_documentation(itask[0],
0,
doc_file=None,
oformat='bin')
s3, r3 = documentation.delta(itask[0], 0, dump_documentation)
else:
LOG.info(
' ERROR: unable to merge documentation for model: {itask[0]} version: {itask[1]}'
)
# rebuild the model
command_build = {
'endpoint': itask[0],
'infile': None,
'param_file': dump_parameters,
'incremental': False
}
success, results = context.build_cmd(command_build)
# enable LOG output
logging.disable(logging.NOTSET)
if itask[1] != 0:
shutil.move(destinat_path, original_path) # dev --> veri
shutil.move(security_path, destinat_path) # dev_sec --> dev
if not success:
LOG.error(results)
LOG.info("Model refreshing task finished")
if GUI:
# update token file with status 'ready'
with open(token_file, 'w') as f:
f.write('ready')
return True, 'OK'
def action_refresh(model=None, version=None):
'''
Rebuild one or many models making use of existing parameter files and
locally stored training series.
'''
import flame.context as context
from flame.parameters import Parameters
import logging
# list endpoints relevant for the arguments
if model is not None:
model_list = [model]
else:
model_root = pathlib.Path(utils.model_repository_path())
model_list = [x.stem for x in model_root.iterdir() if x.is_dir()]
# list versions relevant for the arguments
task_list = []
for imodel in model_list:
if version is not None:
task_list.append((imodel, version))
else:
model_root = pathlib.Path(utils.model_tree_path(imodel))
itask_list = [(imodel, utils.modeldir2ver(x.stem))
for x in model_root.iterdir() if x.is_dir()]
task_list += itask_list # use "+=" and not "append" to merge the new list with the old one
# analize task_list and add at the end ensemble models
# this is needed to have low models refreshed BEFORE refreshing the high models
# eliminating the need to refresh them recursively
LOG.info("Analyzing and sorting models...")
# make sure the lower models are in task_list and, if not, force the inclussion
for itask in task_list:
param = Parameters()
success, results = param.loadYaml(itask[0], itask[1])
if not success:
continue
if param.getVal('input_type') == 'model_ensemble':
ens_nams = param.getVal('ensemble_names')
ens_vers = param.getVal('ensemble_versions')
for i in range(len(ens_nams)):
iver = 0
inam = ens_nams[i]
if (i < len(ens_vers)):
iver = ens_vers[i]
if ((inam, iver)) not in task_list:
task_list.append((inam, iver))
# create separate lists for regular and ensemble models
# and add ensemble models at the end
# this needs to be carried out after the previos step because
# some of the lower level models could be an ensemble model
# itself
mol_list = []
ens_list = []
for itask in task_list:
param = Parameters()
success, results = param.loadYaml(itask[0], itask[1])
if not success:
mol_list.append(itask)
continue
if param.getVal('input_type') == 'model_ensemble':
ens_list.append(itask)
else:
mol_list.append(itask)
task_list = mol_list + ens_list
# show all models before stating
LOG.info(
"Starting model refreshing task for the following models and versions")
for itask in task_list:
LOG.info(f' model: {itask[0]} version: {itask[1]}')
LOG.info("This can take some time, please be patient...")
# now send the build command for each task
for itask in task_list:
if itask[1] != 0:
# move version to /dev for building
original_path = utils.model_path(itask[0], itask[1]) # veri
destinat_path = utils.model_path(itask[0], 0) # dev
security_path = destinat_path + '_security' # dev_sec
shutil.move(destinat_path, security_path) # dev --> dev_sec
shutil.move(original_path, destinat_path) # veri --> dev
LOG.info(
f' refreshing model: {itask[0]} version: {itask[1]} ({task_list.index(itask)+1} of {len(task_list)})...'
)
# dissable LOG output
logging.disable(logging.ERROR)
command_build = {
'endpoint': itask[0],
'infile': None,
'param_file': None,
'incremental': False
}
success, results = context.build_cmd(command_build)
# enable LOG output
logging.disable(logging.NOTSET)
if itask[1] != 0:
shutil.move(destinat_path, original_path) # dev --> veri
shutil.move(security_path, destinat_path) # dev_sec --> dev
if not success:
LOG.error(results)
LOG.info("Model refreshing task finished")
return True, 'OK'
