def _submit_graph(self, pyfiles, dependencies, nodes):
jobs = []
soma_deps = []
for idx, fname in enumerate(pyfiles):
name = os.path.splitext(os.path.split(fname)[1])[0]
jobs.append(Job(command=[sys.executable, fname], name=name))
for key, values in list(dependencies.items()):
for val in values:
soma_deps.append((jobs[val], jobs[key]))
wf = Workflow(jobs, soma_deps)
logger.info('serializing workflow')
Helper.serialize('workflow', wf)
controller = WorkflowController()
logger.info('submitting workflow')
wf_id = controller.submit_workflow(wf)
Helper.wait_workflow(wf_id, controller)
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
