Distributed module
This commit is contained in:
Petrônio Cândido
parent
88e788cdca
commit
e6247b0779
@ -207,128 +207,3 @@ def load_env(file):
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def start_dispy_cluster(method, nodes):
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import dispy, dispy.httpd, logging
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cluster = dispy.JobCluster(method, nodes=nodes, loglevel=logging.DEBUG, ping_interval=1000)
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http_server = dispy.httpd.DispyHTTPServer(cluster)
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return cluster, http_server
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def stop_dispy_cluster(cluster, http_server):
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cluster.wait() # wait for all jobs to finish
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cluster.print_status()
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http_server.shutdown() # this waits until browser gets all updates
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cluster.close()
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def simple_model_train(model, data, parameters):
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model.train(data, **parameters)
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return model
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def distributed_train(model, train_method, nodes, fts_method, data, num_batches=10,
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train_parameters={}, **kwargs):
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import dispy, dispy.httpd, datetime
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batch_save = kwargs.get('batch_save', False) # save model between batches
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batch_save_interval = kwargs.get('batch_save_interval', 1)
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file_path = kwargs.get('file_path', None)
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cluster, http_server = start_dispy_cluster(train_method, nodes)
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print("[{0: %H:%M:%S}] Distrituted Train Started".format(datetime.datetime.now()))
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jobs = []
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n = len(data)
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batch_size = int(n / num_batches)
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bcount = 1
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for ct in range(model.order, n, batch_size):
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if model.is_multivariate:
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ndata = data.iloc[ct - model.order:ct + batch_size]
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else:
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ndata = data[ct - model.order: ct + batch_size]
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tmp_model = fts_method(str(bcount))
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tmp_model.clone_parameters(model)
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job = cluster.submit(tmp_model, ndata, train_parameters)
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job.id = bcount # associate an ID to identify jobs (if needed later)
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jobs.append(job)
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bcount += 1
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for job in jobs:
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print("[{0: %H:%M:%S}] Processing batch ".format(datetime.datetime.now()) + str(job.id))
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tmp = job()
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if job.status == dispy.DispyJob.Finished and tmp is not None:
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model.merge(tmp)
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if batch_save and (job.id % batch_save_interval) == 0:
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persist_obj(model, file_path)
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else:
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print(job.exception)
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print(job.stdout)
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print("[{0: %H:%M:%S}] Finished batch ".format(datetime.datetime.now()) + str(job.id))
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print("[{0: %H:%M:%S}] Distrituted Train Finished".format(datetime.datetime.now()))
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stop_dispy_cluster(cluster, http_server)
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return model
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def simple_model_predict(model, data, parameters):
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return model.predict(data, **parameters)
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def distributed_predict(model, parameters, nodes, data, num_batches):
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import dispy, dispy.httpd
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cluster, http_server = start_dispy_cluster(simple_model_predict, nodes)
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jobs = []
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n = len(data)
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batch_size = int(n / num_batches)
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bcount = 1
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for ct in range(model.order, n, batch_size):
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if model.is_multivariate:
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ndata = data.iloc[ct - model.order:ct + batch_size]
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else:
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ndata = data[ct - model.order: ct + batch_size]
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job = cluster.submit(model, ndata, parameters)
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job.id = bcount # associate an ID to identify jobs (if needed later)
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jobs.append(job)
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bcount += 1
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ret = []
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for job in jobs:
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tmp = job()
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if job.status == dispy.DispyJob.Finished and tmp is not None:
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if job.id < batch_size:
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ret.extend(tmp[:-1])
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else:
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ret.extend(tmp)
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else:
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print(job.exception)
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print(job.stdout)
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stop_dispy_cluster(cluster, http_server)
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return ret
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@ -147,10 +147,21 @@ class FTS(object):
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else:
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if distributed == 'dispy':
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from pyFTS.distributed import dispy
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nodes = kwargs.get("nodes", ['127.0.0.1'])
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num_batches = kwargs.get('num_batches', 10)
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ret = Util.distributed_predict(self, kwargs, nodes, ndata, num_batches)
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ret = dispy.distributed_predict(self, kwargs, nodes, ndata, num_batches)
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elif distributed == 'spark':
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from pyFTS.distributed import spark
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nodes = kwargs.get("nodes", 'spark://192.168.0.110:7077')
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app = kwargs.get("app", 'pyFTS')
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ret = spark.distributed_predict(data=ndata, model=self, url=nodes, app=app)
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if not self.is_multivariate:
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kwargs['type'] = type
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@ -323,12 +334,20 @@ class FTS(object):
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batch_save_interval = kwargs.get('batch_save_interval', 10)
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if distributed:
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if distributed is not None:
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if distributed == 'dispy':
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from pyFTS.distributed import dispy
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nodes = kwargs.get('nodes', False)
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train_method = kwargs.get('train_method', Util.simple_model_train)
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Util.distributed_train(self, train_method, nodes, type(self), data, num_batches, {},
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train_method = kwargs.get('train_method', dispy.simple_model_train)
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dispy.distributed_train(self, train_method, nodes, type(self), data, num_batches, {},
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batch_save=batch_save, file_path=file_path,
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batch_save_interval=batch_save_interval)
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elif distributed == 'spark':
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from pyFTS.distributed import spark
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spark.distributed_train(self, data, self.partitioner,
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url='spark://192.168.0.110:7077', app='pyFTS')
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else:
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if dump == 'time':
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0
pyFTS/distributed/__init__.py
Normal file
0
pyFTS/distributed/__init__.py
Normal file
127
pyFTS/distributed/dispy.py
Normal file
127
pyFTS/distributed/dispy.py
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@ -0,0 +1,127 @@
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import dispy, dispy.httpd, logging
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from pyFTS.common import Util
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def start_dispy_cluster(method, nodes):
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cluster = dispy.JobCluster(method, nodes=nodes, loglevel=logging.DEBUG, ping_interval=1000)
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http_server = dispy.httpd.DispyHTTPServer(cluster)
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return cluster, http_server
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def stop_dispy_cluster(cluster, http_server):
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cluster.wait() # wait for all jobs to finish
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cluster.print_status()
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http_server.shutdown() # this waits until browser gets all updates
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cluster.close()
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def simple_model_train(model, data, parameters):
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model.train(data, **parameters)
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return model
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def distributed_train(model, train_method, nodes, fts_method, data, num_batches=10,
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train_parameters={}, **kwargs):
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import dispy, dispy.httpd, datetime
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batch_save = kwargs.get('batch_save', False) # save model between batches
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batch_save_interval = kwargs.get('batch_save_interval', 1)
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file_path = kwargs.get('file_path', None)
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cluster, http_server = start_dispy_cluster(train_method, nodes)
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print("[{0: %H:%M:%S}] Distrituted Train Started".format(datetime.datetime.now()))
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jobs = []
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n = len(data)
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batch_size = int(n / num_batches)
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bcount = 1
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for ct in range(model.order, n, batch_size):
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if model.is_multivariate:
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ndata = data.iloc[ct - model.order:ct + batch_size]
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else:
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ndata = data[ct - model.order: ct + batch_size]
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tmp_model = fts_method(str(bcount))
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tmp_model.clone_parameters(model)
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job = cluster.submit(tmp_model, ndata, train_parameters)
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job.id = bcount # associate an ID to identify jobs (if needed later)
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jobs.append(job)
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bcount += 1
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for job in jobs:
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print("[{0: %H:%M:%S}] Processing batch ".format(datetime.datetime.now()) + str(job.id))
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tmp = job()
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if job.status == dispy.DispyJob.Finished and tmp is not None:
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model.merge(tmp)
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if batch_save and (job.id % batch_save_interval) == 0:
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Util.persist_obj(model, file_path)
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else:
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print(job.exception)
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print(job.stdout)
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print("[{0: %H:%M:%S}] Finished batch ".format(datetime.datetime.now()) + str(job.id))
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print("[{0: %H:%M:%S}] Distrituted Train Finished".format(datetime.datetime.now()))
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stop_dispy_cluster(cluster, http_server)
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return model
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def simple_model_predict(model, data, parameters):
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return model.predict(data, **parameters)
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def distributed_predict(model, parameters, nodes, data, num_batches):
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import dispy, dispy.httpd
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cluster, http_server = start_dispy_cluster(simple_model_predict, nodes)
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jobs = []
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n = len(data)
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batch_size = int(n / num_batches)
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bcount = 1
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for ct in range(model.order, n, batch_size):
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if model.is_multivariate:
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ndata = data.iloc[ct - model.order:ct + batch_size]
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else:
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ndata = data[ct - model.order: ct + batch_size]
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job = cluster.submit(model, ndata, parameters)
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job.id = bcount # associate an ID to identify jobs (if needed later)
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jobs.append(job)
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bcount += 1
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ret = []
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for job in jobs:
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tmp = job()
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if job.status == dispy.DispyJob.Finished and tmp is not None:
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if job.id < batch_size:
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ret.extend(tmp[:-1])
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else:
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ret.extend(tmp)
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else:
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print(job.exception)
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print(job.stdout)
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stop_dispy_cluster(cluster, http_server)
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return ret
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83
pyFTS/distributed/spark.py
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83
pyFTS/distributed/spark.py
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@ -0,0 +1,83 @@
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import numpy as np
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import pandas as pd
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from pyFTS.data import Enrollments, TAIEX
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from pyFTS.partitioners import Grid, Simple
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from pyFTS.models import hofts
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from pyspark import SparkConf
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from pyspark import SparkContext
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import os
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# make sure pyspark tells workers to use python3 not 2 if both are installed
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os.environ['PYSPARK_PYTHON'] = '/usr/bin/python3'
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os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/bin/python3'
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conf = None
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def get_conf(url, app):
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"""
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:param url:
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:param app:
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:return:
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"""
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if conf is None:
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conf = SparkConf()
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conf.setMaster(url)
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conf.setAppName(app)
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return conf
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def get_partitioner(shared_partitioner):
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"""
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:param part:
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:return:
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"""
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fs_tmp = Simple.SimplePartitioner()
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for fset in shared_partitioner.value.keys():
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fz = shared_partitioner.value[fset]
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fs_tmp.append(fset, fz.mf, fz.parameters)
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return fs_tmp
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def slave_train(data):
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"""
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:param data:
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:return:
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"""
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model = shared_method.value(partitioner=get_partitioner(shared_partitioner),
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order=shared_order.value)
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ndata = [k for k in data]
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model.train(ndata)
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return [(k, model.flrgs[k]) for k in model.flrgs]
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def distributed_train(model, data, partitioner, url='spark://192.168.0.110:7077', app='pyFTS'):
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with SparkContext(conf=get_conf(url=url, app=app)) as context:
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shared_partitioner = context.broadcast(partitioner.sets)
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flrgs = context.parallelize(data).mapPartitions(slave_train)
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model = hofts.WeightedHighOrderFTS(partitioner=partitioner, order=shared_order.value)
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for k in flrgs.collect():
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model.append_rule(k[1])
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return model
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def distributed_predict(data, model, url='spark://192.168.0.110:7077', app='pyFTS'):
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return None
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2
setup.py
2
setup.py
@ -5,7 +5,7 @@ setup(
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packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.models.ensemble',
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'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic',
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'pyFTS.tests', 'pyFTS.models.nonstationary', 'pyFTS.models.multivariate',
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'pyFTS.models.incremental', 'pyFTS.hyperparam'],
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'pyFTS.models.incremental', 'pyFTS.hyperparam', 'pyFTS.distributed'],
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version='1.4',
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description='Fuzzy Time Series for Python',
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author='Petronio Candido L. e Silva',
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