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'