def run(self, **Xy):
from functools import partial
from multiprocessing import Pool
from epac.map_reduce.mappers import MapperSubtrees
from epac.map_reduce.mappers import map_process
## Split input into several parts and create mapper
## ================================================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root, num_processes=self.num_processes)
input_list = split_node_input.split(node_input)
if len(input_list) == 1:
self.tree_root.run(**Xy)
return self.tree_root
mapper = MapperSubtrees(Xy=Xy, tree_root=self.tree_root, function=self.function_name)
## Run map processes in parallel
## =============================
partial_map_process = partial(map_process, mapper=mapper)
# res_tree_root_list = []
# for linput in input_list:
# res_tree_root_list.append(partial_map_process(linput))
# pool = Pool(processes=len(input_list))
# res_tree_root_list = pool.map(partial_map_process, input_list)
from joblib import Parallel, delayed
res_tree_root_list = Parallel(n_jobs=len(input_list))(delayed(partial_map_process)(i) for i in input_list)
for each_tree_root in res_tree_root_list:
self.tree_root.merge_tree_store(each_tree_root)
return self.tree_root
def run(self, **Xy):
from functools import partial
from multiprocessing import Pool
from epac.map_reduce.mappers import MapperSubtrees
from epac.map_reduce.mappers import map_process
## Split input into several parts and create mapper
## ================================================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
input_list = split_node_input.split(node_input)
mapper = MapperSubtrees(Xy=Xy,
tree_root=self.tree_root,
function=self.function_name)
## Run map processes in parallel
## =============================
partial_map_process = partial(map_process, mapper=mapper)
pool = Pool(processes=len(input_list))
res_tree_root_list = pool.map(partial_map_process, input_list)
for each_tree_root in res_tree_root_list:
self.tree_root.merge_tree_store(each_tree_root)
return self.tree_root
def run(self, **Xy):
from functools import partial
from multiprocessing import Pool
from epac.map_reduce.mappers import MapperSubtrees
from epac.map_reduce.mappers import map_process
## Split input into several parts and create mapper
## ================================================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
input_list = split_node_input.split(node_input)
if len(input_list) == 1:
self.tree_root.run(**Xy)
return self.tree_root
mapper = MapperSubtrees(Xy=Xy,
tree_root=self.tree_root,
function=self.function_name)
## Run map processes in parallel
## =============================
partial_map_process = partial(map_process, mapper=mapper)
# res_tree_root_list = []
# for linput in input_list:
# res_tree_root_list.append(partial_map_process(linput))
# pool = Pool(processes=len(input_list))
# res_tree_root_list = pool.map(partial_map_process, input_list)
from joblib import Parallel, delayed
res_tree_root_list = \
Parallel(n_jobs=len(input_list))(delayed(partial_map_process)(i)
for i in input_list)
for each_tree_root in res_tree_root_list:
self.tree_root.merge_tree_store(each_tree_root)
return self.tree_root
def run(self, **Xy):
from functools import partial
from multiprocessing import Pool
from epac.map_reduce.mappers import MapperSubtrees
from epac.map_reduce.mappers import map_process
## Split input into several parts and create mapper
## ================================================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
input_list = split_node_input.split(node_input)
mapper = MapperSubtrees(Xy=Xy,
tree_root=self.tree_root,
function=self.function_name)
## Run map processes in parallel
## =============================
partial_map_process = partial(map_process, mapper=mapper)
pool = Pool(processes=len(input_list))
res_tree_root_list = pool.map(partial_map_process, input_list)
for each_tree_root in res_tree_root_list:
self.tree_root.merge_tree_store(each_tree_root)
return self.tree_root
def export_jobs(tree_root, num_processes, dir_path):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
node_input = NodesInput(tree_root.get_key())
split_node_input = SplitNodesInput(tree_root, num_processes=num_processes)
nodesinput_list = split_node_input.split(node_input)
return save_job_list(dir_path, nodesinput_list)
def export_to_gui(self, soma_workflow_dirpath, **Xy):
'''
Example
-------
see the directory of "examples/run_somaworkflow_gui.py" in epac
'''
try:
from soma.workflow.client import Job, Workflow
from soma.workflow.client import Helper, FileTransfer
except ImportError:
errmsg = "No soma-workflow is found. "\
"Please verify your soma-worklow"\
"on your computer (e.g. PYTHONPATH) \n"
sys.stderr.write(errmsg)
sys.stdout.write(errmsg)
raise NoSomaWFError
if not os.path.exists(soma_workflow_dirpath):
os.makedirs(soma_workflow_dirpath)
tmp_work_dir_path = soma_workflow_dirpath
cur_work_dir = os.getcwd()
os.chdir(tmp_work_dir_path)
ft_working_directory = FileTransfer(is_input=True,
client_path=tmp_work_dir_path,
name="working directory")
## Save the database and tree to working directory
## ===============================================
np.savez(os.path.join(tmp_work_dir_path,
SomaWorkflowEngine.dataset_relative_path), **Xy)
store = StoreFs(dirpath=os.path.join(
tmp_work_dir_path,
SomaWorkflowEngine.tree_root_relative_path))
self.tree_root.save_tree(store=store)
## Subtree job allocation on disk
## ==============================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
nodesinput_list = split_node_input.split(node_input)
keysfile_list = self._save_job_list(tmp_work_dir_path,
nodesinput_list)
## Build soma-workflow
## ===================
jobs = [Job(command=[u"epac_mapper",
u'--datasets', '"%s"' %
(SomaWorkflowEngine.dataset_relative_path),
u'--keysfile', '"%s"' %
(nodesfile)],
referenced_input_files=[ft_working_directory],
referenced_output_files=[ft_working_directory],
name="epac_job_key=%s" % (nodesfile),
working_directory=ft_working_directory)
for nodesfile in keysfile_list]
soma_workflow = Workflow(jobs=jobs)
if soma_workflow_dirpath and soma_workflow_dirpath != "":
out_soma_workflow_file = os.path.join(soma_workflow_dirpath,
SomaWorkflowEngine.open_me_by_soma_workflow_gui)
Helper.serialize(out_soma_workflow_file, soma_workflow)
os.chdir(cur_work_dir)
def export_to_gui(self, soma_workflow_dirpath, **Xy):
'''
Example
-------
see the directory of "examples/run_somaworkflow_gui.py" in epac
'''
try:
from soma_workflow.client import Job, Workflow
from soma_workflow.client import Helper, FileTransfer
except ImportError:
errmsg = "No soma-workflow is found. "\
"Please verify your soma-worklow"\
"on your computer (e.g. PYTHONPATH) \n"
sys.stderr.write(errmsg)
sys.stdout.write(errmsg)
raise NoSomaWFError
if not os.path.exists(soma_workflow_dirpath):
os.makedirs(soma_workflow_dirpath)
tmp_work_dir_path = soma_workflow_dirpath
cur_work_dir = os.getcwd()
os.chdir(tmp_work_dir_path)
ft_working_directory = FileTransfer(is_input=True,
client_path=tmp_work_dir_path,
name="working directory")
## Save the database and tree to working directory
## ===============================================
# np.savez(os.path.join(tmp_work_dir_path,
# SomaWorkflowEngine.dataset_relative_path), **Xy)
db_size = estimate_dataset_size(**Xy)
db_size = int(db_size / (1024 * 1024)) # convert it into mega byte
save_dataset(SomaWorkflowEngine.dataset_relative_path, **Xy)
store = StoreFs(dirpath=os.path.join(
tmp_work_dir_path, SomaWorkflowEngine.tree_root_relative_path))
self.tree_root.save_tree(store=store)
## Subtree job allocation on disk
## ==============================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
nodesinput_list = split_node_input.split(node_input)
keysfile_list = self._save_job_list(tmp_work_dir_path, nodesinput_list)
## Build soma-workflow
## ===================
jobs = self._create_jobs(keysfile_list,
is_run_local=False,
ft_working_directory=ft_working_directory)
soma_workflow = Workflow(jobs=jobs)
if soma_workflow_dirpath and soma_workflow_dirpath != "":
out_soma_workflow_file = os.path.join(
soma_workflow_dirpath,
SomaWorkflowEngine.open_me_by_soma_workflow_gui)
Helper.serialize(out_soma_workflow_file, soma_workflow)
os.chdir(cur_work_dir)
def export_to_gui(self, soma_workflow_dirpath, **Xy):
"""
Example
-------
see the directory of "examples/run_somaworkflow_gui.py" in epac
"""
try:
from soma_workflow.client import Job, Workflow
from soma_workflow.client import Helper, FileTransfer
except ImportError:
errmsg = (
"No soma-workflow is found. " "Please verify your soma-worklow" "on your computer (e.g. PYTHONPATH) \n"
)
sys.stderr.write(errmsg)
sys.stdout.write(errmsg)
raise NoSomaWFError
if not os.path.exists(soma_workflow_dirpath):
os.makedirs(soma_workflow_dirpath)
tmp_work_dir_path = soma_workflow_dirpath
cur_work_dir = os.getcwd()
os.chdir(tmp_work_dir_path)
ft_working_directory = FileTransfer(is_input=True, client_path=tmp_work_dir_path, name="working directory")
## Save the database and tree to working directory
## ===============================================
# np.savez(os.path.join(tmp_work_dir_path,
# SomaWorkflowEngine.dataset_relative_path), **Xy)
db_size = estimate_dataset_size(**Xy)
db_size = int(db_size / (1024 * 1024)) # convert it into mega byte
save_dataset(SomaWorkflowEngine.dataset_relative_path, **Xy)
store = StoreFs(dirpath=os.path.join(tmp_work_dir_path, SomaWorkflowEngine.tree_root_relative_path))
self.tree_root.save_tree(store=store)
## Subtree job allocation on disk
## ==============================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root, num_processes=self.num_processes)
nodesinput_list = split_node_input.split(node_input)
keysfile_list = self._save_job_list(tmp_work_dir_path, nodesinput_list)
## Build soma-workflow
## ===================
jobs = self._create_jobs(keysfile_list, is_run_local=False, ft_working_directory=ft_working_directory)
soma_workflow = Workflow(jobs=jobs)
if soma_workflow_dirpath and soma_workflow_dirpath != "":
out_soma_workflow_file = os.path.join(
soma_workflow_dirpath, SomaWorkflowEngine.open_me_by_soma_workflow_gui
)
Helper.serialize(out_soma_workflow_file, soma_workflow)
os.chdir(cur_work_dir)
def run(self, **Xy):
"""Run soma-workflow without gui
Example
-------
>>> from sklearn import datasets
>>> from epac.map_reduce.engine import SomaWorkflowEngine
>>> from epac.tests.wfexamples2test import WFExample2
>>> ## Build dataset
>>> ## =============
>>> X, y = datasets.make_classification(n_samples=10,
... n_features=20,
... n_informative=5,
... random_state=1)
>>> Xy = {'X':X, 'y':y}
>>> ## Build epac tree
>>> ## ===============
>>> tree_root_node = WFExample2().get_workflow()
>>> ## Build SomaWorkflowEngine and run function for each node
>>> ## =======================================================
>>> sfw_engine = SomaWorkflowEngine(tree_root=tree_root_node,
... function_name="transform",
... num_processes=3,
remove_finished_wf=False)
>>> tree_root_node = sfw_engine.run(**Xy)
light mode
>>> ## Run reduce process
>>> ## ==================
>>> tree_root_node.reduce()
ResultSet(
[{'key': SelectKBest/SVC(C=1), 'y/test/score_f1': [ 0.6 0.6], 'y/test/score_recall_mean/pval': [ 0.5], 'y/test/score_recall/pval': [ 0. 0.5], 'y/test/score_accuracy/pval': [ 0.], 'y/test/score_f1/pval': [ 0. 0.5], 'y/test/score_precision/pval': [ 0.5 0. ], 'y/test/score_precision': [ 0.6 0.6], 'y/test/score_recall': [ 0.6 0.6], 'y/test/score_accuracy': 0.6, 'y/test/score_recall_mean': 0.6},
{'key': SelectKBest/SVC(C=3), 'y/test/score_f1': [ 0.6 0.6], 'y/test/score_recall_mean/pval': [ 0.5], 'y/test/score_recall/pval': [ 0. 0.5], 'y/test/score_accuracy/pval': [ 0.], 'y/test/score_f1/pval': [ 0. 0.5], 'y/test/score_precision/pval': [ 0.5 0. ], 'y/test/score_precision': [ 0.6 0.6], 'y/test/score_recall': [ 0.6 0.6], 'y/test/score_accuracy': 0.6, 'y/test/score_recall_mean': 0.6}])
"""
try:
from soma_workflow.client import Job, Workflow
from soma_workflow.client import Helper, FileTransfer
from soma_workflow.client import WorkflowController
except ImportError:
errmsg = (
"No soma-workflow is found. " "Please verify your soma-worklow" "on your computer (e.g. PYTHONPATH) \n"
)
sys.stderr.write(errmsg)
sys.stdout.write(errmsg)
raise NoSomaWFError
tmp_work_dir_path = tempfile.mkdtemp()
cur_work_dir = os.getcwd()
os.chdir(tmp_work_dir_path)
is_run_local = False
if not self.resource_id or self.resource_id == "":
self.resource_id = socket.gethostname()
is_run_local = True
# print "is_run_local=", is_run_local
if not is_run_local:
ft_working_directory = FileTransfer(is_input=True, client_path=tmp_work_dir_path, name="working directory")
else:
ft_working_directory = tmp_work_dir_path
## Save the database and tree to working directory
## ===============================================
# np.savez(os.path.join(tmp_work_dir_path,
# SomaWorkflowEngine.dataset_relative_path), **Xy)
save_dataset(SomaWorkflowEngine.dataset_relative_path, **Xy)
store = StoreFs(dirpath=os.path.join(tmp_work_dir_path, SomaWorkflowEngine.tree_root_relative_path))
self.tree_root.save_tree(store=store)
## Subtree job allocation on disk
## ==============================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root, num_processes=self.num_processes)
nodesinput_list = split_node_input.split(node_input)
keysfile_list = save_job_list(tmp_work_dir_path, nodesinput_list)
## Build soma-workflow
## ===================
jobs = self._create_jobs(keysfile_list, is_run_local, ft_working_directory)
soma_workflow = Workflow(jobs=jobs)
controller = WorkflowController(self.resource_id, self.login, self.pw)
## run soma-workflow
## =================
wf_id = controller.submit_workflow(workflow=soma_workflow, name="epac workflow", queue=self.queue)
Helper.transfer_input_files(wf_id, controller)
Helper.wait_workflow(wf_id, controller)
Helper.transfer_output_files(wf_id, controller)
self.engine_info = self.get_engine_info(controller, wf_id)
if self.remove_finished_wf:
controller.delete_workflow(wf_id)
## read result tree
## ================
self.tree_root = store.load()
os.chdir(cur_work_dir)
if os.path.isdir(tmp_work_dir_path) and self.remove_local_tree:
shutil.rmtree(tmp_work_dir_path)
return self.tree_root
def export_to_gui(self, soma_workflow_dirpath, **Xy):
'''
Example
-------
see the directory of "examples/run_somaworkflow_gui.py" in epac
'''
try:
from soma.workflow.client import Job, Workflow
from soma.workflow.client import Helper, FileTransfer
except ImportError:
errmsg = "No soma-workflow is found. "\
"Please verify your soma-worklow"\
"on your computer (e.g. PYTHONPATH) \n"
sys.stderr.write(errmsg)
sys.stdout.write(errmsg)
raise NoSomaWFError
if not os.path.exists(soma_workflow_dirpath):
os.makedirs(soma_workflow_dirpath)
tmp_work_dir_path = soma_workflow_dirpath
cur_work_dir = os.getcwd()
os.chdir(tmp_work_dir_path)
ft_working_directory = FileTransfer(is_input=True,
client_path=tmp_work_dir_path,
name="working directory")
## Save the database and tree to working directory
## ===============================================
np.savez(
os.path.join(tmp_work_dir_path,
SomaWorkflowEngine.dataset_relative_path), **Xy)
store = StoreFs(dirpath=os.path.join(
tmp_work_dir_path, SomaWorkflowEngine.tree_root_relative_path))
self.tree_root.save_tree(store=store)
## Subtree job allocation on disk
## ==============================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
nodesinput_list = split_node_input.split(node_input)
keysfile_list = self._save_job_list(tmp_work_dir_path, nodesinput_list)
## Build soma-workflow
## ===================
jobs = [
Job(command=[
u"epac_mapper", u'--datasets',
'"%s"' % (SomaWorkflowEngine.dataset_relative_path),
u'--keysfile',
'"%s"' % (nodesfile)
],
referenced_input_files=[ft_working_directory],
referenced_output_files=[ft_working_directory],
name="epac_job_key=%s" % (nodesfile),
working_directory=ft_working_directory)
for nodesfile in keysfile_list
]
soma_workflow = Workflow(jobs=jobs)
if soma_workflow_dirpath and soma_workflow_dirpath != "":
out_soma_workflow_file = os.path.join(
soma_workflow_dirpath,
SomaWorkflowEngine.open_me_by_soma_workflow_gui)
Helper.serialize(out_soma_workflow_file, soma_workflow)
os.chdir(cur_work_dir)
def run(self, **Xy):
'''Run soma-workflow without gui
Example
-------
>>> from sklearn import datasets
>>> from epac.map_reduce.engine import SomaWorkflowEngine
>>> from epac.tests.wfexamples2test import WFExample2
>>> ## Build dataset
>>> ## =============
>>> X, y = datasets.make_classification(n_samples=10,
... n_features=20,
... n_informative=5,
... random_state=1)
>>> Xy = {'X':X, 'y':y}
>>> ## Build epac tree
>>> ## ===============
>>> tree_root_node = WFExample2().get_workflow()
>>> ## Build SomaWorkflowEngine and run function for each node
>>> ## =======================================================
>>> sfw_engine = SomaWorkflowEngine(tree_root=tree_root_node,
... function_name="trasform",
... num_processes=3)
>>> tree_root_node = sfw_engine.run(**Xy)
>>> ## Run reduce process
>>> ## ==================
>>> tree_root_node.reduce()
ResultSet(
[{'key': SelectKBest/SVC(C=1), 'y/test/score_recall_mean/pval': [ 0.], 'y/test/score_recall/pval': [ 0. 0.], 'y/test/score_accuray': 0.8, 'y/test/score_f1/pval': [ 0. 0.], 'y/test/score_precision/pval': [ 0. 0.], 'y/test/score_precision': [ 0.8 0.8], 'y/test/score_recall': [ 0.8 0.8], 'y/test/score_f1': [ 0.8 0.8], 'y/test/score_recall_mean': 0.8, 'y/test/score_accuray/pval': [ 0.]},
{'key': SelectKBest/SVC(C=3), 'y/test/score_recall_mean/pval': [ 0.], 'y/test/score_recall/pval': [ 0. 0.], 'y/test/score_accuray': 0.8, 'y/test/score_f1/pval': [ 0. 0.], 'y/test/score_precision/pval': [ 0. 0.], 'y/test/score_precision': [ 0.8 0.8], 'y/test/score_recall': [ 0.8 0.8], 'y/test/score_f1': [ 0.8 0.8], 'y/test/score_recall_mean': 0.8, 'y/test/score_accuray/pval': [ 0.]}])
'''
try:
from soma.workflow.client import Job, Workflow
from soma.workflow.client import Helper, FileTransfer
from soma.workflow.client import WorkflowController
except ImportError:
errmsg = "No soma-workflow is found. "\
"Please verify your soma-worklow"\
"on your computer (e.g. PYTHONPATH) \n"
sys.stderr.write(errmsg)
sys.stdout.write(errmsg)
raise NoSomaWFError
tmp_work_dir_path = tempfile.mkdtemp()
cur_work_dir = os.getcwd()
os.chdir(tmp_work_dir_path)
ft_working_directory = FileTransfer(is_input=True,
client_path=tmp_work_dir_path,
name="working directory")
## Save the database and tree to working directory
## ===============================================
np.savez(
os.path.join(tmp_work_dir_path,
SomaWorkflowEngine.dataset_relative_path), **Xy)
store = StoreFs(dirpath=os.path.join(
tmp_work_dir_path, SomaWorkflowEngine.tree_root_relative_path))
self.tree_root.save_tree(store=store)
## Subtree job allocation on disk
## ==============================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
nodesinput_list = split_node_input.split(node_input)
keysfile_list = self._save_job_list(tmp_work_dir_path, nodesinput_list)
## Build soma-workflow
## ===================
jobs = [
Job(command=[
u"epac_mapper", u'--datasets',
'"%s"' % (SomaWorkflowEngine.dataset_relative_path),
u'--keysfile',
'"%s"' % (nodesfile)
],
referenced_input_files=[ft_working_directory],
referenced_output_files=[ft_working_directory],
name="epac_job_key=%s" % (nodesfile),
working_directory=ft_working_directory)
for nodesfile in keysfile_list
]
soma_workflow = Workflow(jobs=jobs)
if not self.resource_id or self.resource_id == "":
self.resource_id = socket.gethostname()
controller = WorkflowController(self.resource_id, self.login, self.pw)
## run soma-workflow
## =================
wf_id = controller.submit_workflow(workflow=soma_workflow,
name="epac workflow")
Helper.transfer_input_files(wf_id, controller)
Helper.wait_workflow(wf_id, controller)
Helper.transfer_output_files(wf_id, controller)
controller.delete_workflow(wf_id)
## read result tree
## ================
self.tree_root = store.load()
os.chdir(cur_work_dir)
if os.path.isdir(tmp_work_dir_path):
shutil.rmtree(tmp_work_dir_path)
return self.tree_root
def run(self, **Xy):
'''Run soma-workflow without gui
Example
-------
>>> from sklearn import datasets
>>> from epac.map_reduce.engine import SomaWorkflowEngine
>>> from epac.tests.wfexamples2test import WFExample2
>>> ## Build dataset
>>> ## =============
>>> X, y = datasets.make_classification(n_samples=10,
... n_features=20,
... n_informative=5,
... random_state=1)
>>> Xy = {'X':X, 'y':y}
>>> ## Build epac tree
>>> ## ===============
>>> tree_root_node = WFExample2().get_workflow()
>>> ## Build SomaWorkflowEngine and run function for each node
>>> ## =======================================================
>>> sfw_engine = SomaWorkflowEngine(tree_root=tree_root_node,
... function_name="trasform",
... num_processes=3)
>>> tree_root_node = sfw_engine.run(**Xy)
>>> ## Run reduce process
>>> ## ==================
>>> tree_root_node.reduce()
ResultSet(
[{'key': SelectKBest/SVC(C=1), 'y/test/score_recall_mean/pval': [ 0.], 'y/test/score_recall/pval': [ 0. 0.], 'y/test/score_accuray': 0.8, 'y/test/score_f1/pval': [ 0. 0.], 'y/test/score_precision/pval': [ 0. 0.], 'y/test/score_precision': [ 0.8 0.8], 'y/test/score_recall': [ 0.8 0.8], 'y/test/score_f1': [ 0.8 0.8], 'y/test/score_recall_mean': 0.8, 'y/test/score_accuray/pval': [ 0.]},
{'key': SelectKBest/SVC(C=3), 'y/test/score_recall_mean/pval': [ 0.], 'y/test/score_recall/pval': [ 0. 0.], 'y/test/score_accuray': 0.8, 'y/test/score_f1/pval': [ 0. 0.], 'y/test/score_precision/pval': [ 0. 0.], 'y/test/score_precision': [ 0.8 0.8], 'y/test/score_recall': [ 0.8 0.8], 'y/test/score_f1': [ 0.8 0.8], 'y/test/score_recall_mean': 0.8, 'y/test/score_accuray/pval': [ 0.]}])
'''
try:
from soma.workflow.client import Job, Workflow
from soma.workflow.client import Helper, FileTransfer
from soma.workflow.client import WorkflowController
except ImportError:
errmsg = "No soma-workflow is found. "\
"Please verify your soma-worklow"\
"on your computer (e.g. PYTHONPATH) \n"
sys.stderr.write(errmsg)
sys.stdout.write(errmsg)
raise NoSomaWFError
tmp_work_dir_path = tempfile.mkdtemp()
cur_work_dir = os.getcwd()
os.chdir(tmp_work_dir_path)
ft_working_directory = FileTransfer(is_input=True,
client_path=tmp_work_dir_path,
name="working directory")
## Save the database and tree to working directory
## ===============================================
np.savez(os.path.join(tmp_work_dir_path,
SomaWorkflowEngine.dataset_relative_path), **Xy)
store = StoreFs(dirpath=os.path.join(
tmp_work_dir_path,
SomaWorkflowEngine.tree_root_relative_path))
self.tree_root.save_tree(store=store)
## Subtree job allocation on disk
## ==============================
node_input = NodesInput(self.tree_root.get_key())
split_node_input = SplitNodesInput(self.tree_root,
num_processes=self.num_processes)
nodesinput_list = split_node_input.split(node_input)
keysfile_list = self._save_job_list(tmp_work_dir_path,
nodesinput_list)
## Build soma-workflow
## ===================
jobs = [Job(command=[u"epac_mapper",
u'--datasets', '"%s"' %
(SomaWorkflowEngine.dataset_relative_path),
u'--keysfile', '"%s"' %
(nodesfile)],
referenced_input_files=[ft_working_directory],
referenced_output_files=[ft_working_directory],
name="epac_job_key=%s" % (nodesfile),
working_directory=ft_working_directory)
for nodesfile in keysfile_list]
soma_workflow = Workflow(jobs=jobs)
if not self.resource_id or self.resource_id == "":
self.resource_id = socket.gethostname()
controller = WorkflowController(self.resource_id,
self.login,
self.pw)
## run soma-workflow
## =================
wf_id = controller.submit_workflow(workflow=soma_workflow,
name="epac workflow")
Helper.transfer_input_files(wf_id, controller)
Helper.wait_workflow(wf_id, controller)
Helper.transfer_output_files(wf_id, controller)
controller.delete_workflow(wf_id)
## read result tree
## ================
self.tree_root = store.load()
os.chdir(cur_work_dir)
if os.path.isdir(tmp_work_dir_path):
shutil.rmtree(tmp_work_dir_path)
return self.tree_root