def __init__(self):
self.set_argparse()
self._set_rmq()
self.am = entk.AppManager(hostname=self.rmq_hostname,
port=self.rmq_port)
self.p = entk.Pipeline()
self.s = entk.Stage()
def __init__(self):
self.set_argparse()
self._set_rmq()
self.am = entk.AppManager(hostname=self.rmq_hostname,
port=self.rmq_port,
username=self.rmq_username,
password=self.rmq_password)
#pipelines = []
self.pipelines = []
self.p1 = entk.Pipeline()
self.p2 = entk.Pipeline()
#pipelines.append(self.p1)
#pipelines.append(self.p2)
self.s1 = entk.Stage()
self.s2 = entk.Stage()
self.s3 = entk.Stage()
self.s4 = entk.Stage()
self.s5 = entk.Stage()
self.s6 = entk.Stage()
self.s7 = entk.Stage()
def generate_discover_pipe(self, filetype='csv', img_ftype='tif'):
'''
This function takes as an input paths on Bridges and returns a pipeline
that will provide a file for all the images that exist in that path.
'''
pipeline = re.Pipeline()
pipeline.name = 'Disc'
stage = re.Stage()
stage.name = 'Disc.S0'
if self._paths is None:
raise RuntimeError('Images paths are not set.')
# Create the module load list
modules_load = list()
if self._modules:
for module in self._modules:
tmp_load = 'module load %s' % module
modules_load.append(tmp_load)
tmp_pre_execs = ['unset PYTHONPATH']
if self._pre_execs:
tmp_pre_execs = tmp_pre_execs + modules_load + self._pre_execs
else:
tmp_pre_execs = tmp_pre_execs + modules_load
for i in range(len(self._paths)):
task = re.Task()
task.name = 'Disc.T%d' % i
task.pre_exec = tmp_pre_execs
task.executable = 'python' # Assign executable to the task
task.arguments = [
'image_disc.py',
'%s' % self._paths[i],
'--image_ftype=%s' % img_ftype,
'--filename=images%d' % i,
'--filetype=%s' % filetype, '--filesize'
]
task.download_output_data = ['images%d.csv' % i]
task.upload_input_data = [
os.path.dirname(os.path.abspath(__file__)) + '/image_disc.py'
]
task.cpu_reqs = {
'cpu_processes': 1,
'cpu_process_type': '',
'cpu_threads': 1,
'cpu_thread_type': 'OpenMP'
}
stage.add_tasks(task)
# Add Stage to the Pipeline
pipeline.add_stages(stage)
return pipeline
def _generate_pipeline(self, name, pre_execs, image, gpu_id):
'''
This function creates a pipeline for an image that will be analyzed.
:Arguments:
:name: Pipeline name, str
:image: image path, str
:model_path: Path to the model file, str
:model_arch: Prediction Model Architecture, str
:model_name: Prediction Model Name, str
:hyperparam_set: Which hyperparameter set to use, str
'''
# Create a Pipeline object
entk_pipeline = re.Pipeline()
entk_pipeline.name = name
# Create a Stage object
stage0 = re.Stage()
stage0.name = '%s-S0' % (name)
# Create Task 1, training
task1 = re.Task()
task1.name = '%s-T0' % stage0.name
task1.pre_exec = pre_execs
task1.executable = 'iceberg_penguins.detect' # Assign task executable
# Assign arguments for the task executable
task1.arguments = ['--gpu_ids', gpu_id,
'--name', self._model_name,
'--epoch', self._epoch,
'--checkpoints_dir', self._model_path,
'--output', self._output_path,
'--testset', 'GE',
'--input_im', image.split('/')[-1]]
task1.link_input_data = ['%s' % image]
task1.cpu_reqs = {'cpu_processes': 1, 'cpu_threads': 1,
'cpu_process_type': None, 'cpu_thread_type': 'OpenMP'}
task1.gpu_reqs = {'gpu_processes': 1, 'gpu_threads': 1,
'gpu_process_type': None, 'gpu_thread_type': 'OpenMP'}
# Download resuting images
# task1.download_output_data = ['%s/ > %s' % (image.split('/')[-1].
# split('.')[0],
# image.split('/')[-1])]
# task1.tag = task0.name
stage0.add_tasks(task1)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage0)
return entk_pipeline
def setup(self):
# prepare input for all replicas
writeInputs.writeInputs(max_temp=self._max_temp,
min_temp=self._min_temp,
replicas=self._en_size,
timesteps=self._timesteps,
basename=self._basename)
# and tar it up
tar = tarfile.open("input_files.tar", "w")
for name in [self._basename + ".prmtop",
self._basename + ".inpcrd",
self._basename + ".mdin"]:
tar.add(name)
for replica in self._replicas:
tar.add ('mdin-%s-0' % replica.rid) #how does this work
os.remove('mdin-%s-0' % replica.rid)
tar.close()
# create a single pipeline with one stage to transfer the tarball
task = re.Task()
task.name = 'untarTsk'
task.executable = ['python']
task.upload_input_data = ['untar_input_files.py', 'input_files.tar']
task.arguments = ['untar_input_files.py', 'input_files.tar']
task.cpu_reqs = {'processes' : 1,
'thread_type' : None,
'threads_per_process': 1,
'process_type': None}
task.post_exec = []
stage = re.Stage()
stage.name = 'untarStg'
stage.add_tasks(task)
#setup = re.Pipeline()
#setup.name = 'untarPipe'
#setup.add_stages(stage)
setup_pipeline = re.Pipeline()
setup_pipeline.name = 'untarPipe'
setup_pipeline.add_stages(stage)
return [setup_pipeline]
def setup_replicas(replicas, min_temp, max_temp, timesteps, basename):
writeInputs.writeInputs(max_temp=max_temp,
min_temp=min_temp,
replicas=replicas,
timesteps=timesteps,
basename=basename)
tar = tarfile.open("input_files.tar", "w")
for name in [
basename + ".prmtop", basename + ".inpcrd", basename + ".mdin"
]:
tar.add(name)
for r in range(replicas):
tar.add('mdin-{replica}-{cycle}'.format(replica=r, cycle=0))
os.remove('mdin-{replica}-{cycle}'.format(replica=r, cycle=0))
tar.close()
setup_p = re.Pipeline()
setup_p.name = 'untarPipe'
# # unused
# repo = git.Repo('.', search_parent_directories=True)
# aux_function_path = repo.working_tree_dir
untar_stg = re.Stage()
untar_stg.name = 'untarStg'
# Untar Task
untar_tsk = re.Task()
untar_tsk.name = 'untarTsk'
untar_tsk.executable = ['python']
untar_tsk.upload_input_data = ['untar_input_files.py', 'input_files.tar']
untar_tsk.arguments = ['untar_input_files.py', 'input_files.tar']
untar_tsk.cpu_reqs = 1
untar_tsk.post_exec = []
untar_stg.add_tasks(untar_tsk)
setup_p.add_stages(untar_stg)
replica_sandbox = '$Pipeline_%s_Stage_%s_Task_%s' \
% (setup_p.name, untar_stg.name, untar_tsk.name)
return setup_p, replica_sandbox
def setup(self):
self._log.debug('=== data staging')
# prepare input for all replicas
writeInputs.writeInputs(max_temp=self._max_temp,
min_temp=self._min_temp,
replicas=self._size,
timesteps=self._timesteps,
basename=self._basename)
# and tar it up
tar = tarfile.open("input_files.tar", "w")
for name in [
self._basename + ".prmtop", self._basename + ".inpcrd",
self._basename + ".mdin"
]:
tar.add(name)
for replica in self._replicas:
tar.add('mdin-%s-0' % replica.rid)
os.remove('mdin-%s-0' % replica.rid)
tar.close()
# create a single pipeline with one stage to transfer the tarball
task = re.Task()
task.name = 'untarTsk'
task.executable = 'python'
task.upload_input_data = ['untar_input_files.py', 'input_files.tar']
task.arguments = ['untar_input_files.py', 'input_files.tar']
task.cpu_reqs = 1
task.post_exec = []
stage = re.Stage()
stage.name = 'untarStg'
stage.add_tasks(task)
setup = re.Pipeline()
setup.name = 'untarPipe'
setup.add_stages(stage)
# run the setup pipeline
self.workflow = set([setup])
self.run()
def get_wf3_input(appman, cfg):
# Assuming shared filesystem on login node this can be executed by the
# script instead of EnTK.
p = entk.Pipeline()
p.name = 'get_wf3_input'
s = entk.Stage()
t = entk.Task()
t.executable = ['python3']
t.arguments = [
'gather.py', '-f', cfg['outlier_path'], '-p', cfg['top_path']
]
s.add_tasks(t)
p.add_stages(s)
appman.workflow = [p]
appman.run()
def _generate_pipeline(self, name, pre_execs, image, image_size):
'''
This function creates a pipeline for an image that will be analyzed.
:Arguments:
:name: Pipeline name, str
:image: image path, str
:image_size: image size in MBs, int
:tile_size: The size of each tile, int
:model_path: Path to the model file, str
:model_arch: Prediction Model Architecture, str
:model_name: Prediction Model Name, str
:hyperparam_set: Which hyperparameter set to use, str
'''
# Create a Pipeline object
entk_pipeline = re.Pipeline()
entk_pipeline.name = name
# Create a Stage object
stage0 = re.Stage()
stage0.name = '%s.S0' % (name)
# Create Task 1, training
task0 = re.Task()
task0.name = '%s.T0' % stage0.name
task0.pre_exec = pre_execs
task0.executable = 'iceberg_seals.tiling' # Assign tak executable
# Assign arguments for the task executable
task0.arguments = [
'--input_image=%s' % image.split('/')[-1],
'--output_folder=$NODE_LFS_PATH/%s' % task0.name,
'--bands=%s' % self._bands,
'--stride=%s' % self._stride,
'--patch_size=%s' % self._patch_size,
'--geotiff=%s' % self._geotiff
]
task0.link_input_data = [image]
task0.cpu_reqs = {
'cpu_processes': 1,
'cpu_threads': 4,
'cpu_process_type': None,
'cpu_thread_type': 'OpenMP'
}
task0.lfs_per_process = image_size
stage0.add_tasks(task0)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage0)
# Create a Stage object
stage1 = re.Stage()
stage1.name = '%s.S1' % (name)
# Create Task 1, training
task1 = re.Task()
task1.name = '%s.T1' % stage1.name
task1.pre_exec = pre_execs
task1.executable = 'iceberg_seals.predicting' # Assign task executable
# Assign arguments for the task executable
task1.arguments = [
'--input_dir=$NODE_LFS_PATH/%s' % task0.name,
'--model_architecture=%s' % self._model_arch,
'--hyperparameter_set=%s' % self._hyperparam,
'--model_name=%s' % self._model_name,
'--models_folder=./',
'--output_dir=./%s' % image.split('/')[-1].split('.')[0],
]
task1.link_input_data = ['$SHARED/%s' % self._model_name]
task1.cpu_reqs = {
'cpu_processes': 1,
'cpu_threads': 1,
'cpu_process_type': None,
'cpu_thread_type': 'OpenMP'
}
task1.gpu_reqs = {
'gpu_processes': 1,
'gpu_threads': 1,
'gpu_process_type': None,
'gpu_thread_type': 'OpenMP'
}
# Download resulting images
# task1.download_output_data = ['%s/ > %s' % (image.split('/')[-1].
# split('.')[0],
# image.split('/')[-1])]
task1.tags = {'colocate': task0.name}
stage1.add_tasks(task1)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage1)
return entk_pipeline
def _generate_pipeline(self, name, pre_execs, image, image_size):
'''
This function creates a pipeline for an image that will be analyzed.
:Arguments:
:name: Pipeline name, str
:pre_execs: things need to happen before execution
:image: image path, str
:image_size: image size in MBs, int
'''
# Create a Pipeline object
entk_pipeline = re.Pipeline()
entk_pipeline.name = name
# Create a Stage object
stage0 = re.Stage()
stage0.name = '%s.S0' % (name)
# Create Task 1, training
task0 = re.Task()
task0.name = '%s.T0' % stage0.name
task0.pre_exec = pre_execs
task0.executable = 'iceberg_rivers.tiling' # Assign tak executable
task0.arguments = [
'--input=%s' % image.split('/')[-1],
'--output=$NODE_LFS_PATH/%s/' % task0.name,
'--tile_size=%s' % self._tile_size,
'--step=%s' % self._step
]
task0.link_input_data = [image]
task0.cpu_reqs = {
'cpu_processes': 1,
'cpu_threads': 4,
'cpu_process_type': None,
'cpu_thread_type': None
}
task0.lfs_per_process = image_size
stage0.add_tasks(task0)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage0)
# Create a Stage object
stage1 = re.Stage()
stage1.name = '%s.S1' % (name)
# Create Task 1, training
task1 = re.Task()
task1.name = '%s.T1' % stage1.name
task1.pre_exec = pre_execs
task1.executable = 'iceberg_rivers.predicting' # Assign task executable
# # Assign arguments for the task executable
task1.arguments = [
'--input=$NODE_LFS_PATH/%s/' % task0.name,
'--weights_path=%s' % self._weights_path,
'--output_folder=$NODE_LFS_PATH/%s/' % task1.name
]
# # task1.link_input_data = ['$SHARED/%s' % self._model_name]
task1.cpu_reqs = {
'processes': 1,
'threads_per_process': 1,
'process_type': None,
'thread_type': None
}
task1.gpu_reqs = {
'processes': 1,
'threads_per_process': 1,
'process_type': None,
'thread_type': None
}
task0.lfs_per_process = image_size
# Download resulting images
# task1.download_output_data = ['%s/ > %s' % (image.split('/')[-1].
# split('.')[0],
# image.split('/')[-1])]
task1.tags = {'colocate': task0.name}
stage1.add_tasks(task1)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage1)
# Create a Stage object
stage2 = re.Stage()
stage2.name = '%s.S2' % (name)
# Create Task 1, training
task2 = re.Task()
task2.name = '%s.T2' % stage2.name
task2.pre_exec = pre_execs
task2.executable = 'iceberg_rivers.mosaic' # Assign task executable
# # Assign arguments for the task executable
task2.arguments = [
'--input=$NODE_LFS_PATH/%s/' % task1.name,
'--input_WV=%s' % image.split('/')[-1],
'--tile_size=%s' % self._tile_size,
'--step=%s' % self._step, '--output_folder=./'
]
task2.cpu_reqs = {
'processes': 1,
'threads_per_process': 1,
'process_type': None,
'thread_type': None
}
task2.link_input_data = [image]
task2.tags = {'colocate': task0.name}
stage2.add_tasks(task2)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage2)
return entk_pipeline
def _generate_pipeline(self, name, pre_execs, image, image_size):
'''
This function creates a pipeline for an image that will be analyzed.
:Arguments:
:name: Pipeline name, str
:image: image path, str
:image_size: image size in MBs, int
:tile_size: The size of each tile, int
:model_path: Path to the model file, str
:model_arch: Prediction Model Architecture, str
:model_name: Prediction Model Name, str
:hyperparam_set: Which hyperparameter set to use, str
'''
# Create a Pipeline object
entk_pipeline = re.Pipeline()
entk_pipeline.name = name
# Create a Stage object
stage0 = re.Stage()
stage0.name = '%s-S0' % (name)
# Create Task 1, training
task0 = re.Task()
task0.name = '%s-T0' % stage0.name
task0.pre_exec = pre_execs
task0.executable = 'iceberg_seals.tiling' # Assign tak executable
# Assign arguments for the task executable
task0.arguments = ['--scale_bands=%s' % self._scale_bands,
'--input_image=%s' % image.split('/')[-1],
# This line points to the local filesystem of the
# node that the tiling of the image happened.
'--output_folder=$NODE_LFS_PATH/%s' % task0.name]
task0.link_input_data = [image]
task0.cpu_reqs = {'processes': 1, 'threads_per_process': 4,
'process_type': None, 'thread_type': 'OpenMP'}
task0.lfs_per_process = image_size
stage0.add_tasks(task0)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage0)
# Create a Stage object
stage1 = re.Stage()
stage1.name = '%s-S1' % (name)
# Create Task 1, training
task1 = re.Task()
task1.name = '%s-T1' % stage1.name
task1.pre_exec = pre_execs
task1.executable = 'iceberg_seals.predicting' # Assign task executable
# Assign arguments for the task executable
task1.arguments = ['--input_image', image.split('/')[-1],
'--model_architecture', self._model_arch,
'--hyperparameter_set', self._hyperparam,
'--training_set', 'test_vanilla',
'--test_folder', '$NODE_LFS_PATH/%s' % task0.name,
'--model_path', './',
'--output_folder', './%s' % image.split('/')[-1].
split('.')[0]]
task1.link_input_data = ['$SHARED/%s' % self._model_name]
task1.cpu_reqs = {'processes': 1, 'threads_per_process': 1,
'process_type': None, 'thread_type': 'OpenMP'}
task1.gpu_reqs = {'processes': 1, 'threads_per_process': 1,
'process_type': None, 'thread_type': 'OpenMP'}
# Download resuting images
task1.download_output_data = ['%s/ > %s' % (image.split('/')[-1].
split('.')[0],
image.split('/')[-1])]
task1.tag = task0.name
stage1.add_tasks(task1)
# Add Stage to the Pipeline
entk_pipeline.add_stages(stage1)
return entk_pipeline
def replica_pipeline(self, rid, cycle, replica_cores, md_executable,
timesteps, replica_sandbox):
# ----------------------------------------------------------------------
def add_md_stg(rid, cycle):
# md stg here
print 'cycle: ', self.cycle
md_tsk = re.Task()
md_stg = re.Stage()
md_tsk.name = 'mdtsk-{replica}-{cycle}'.format(replica=rid,
cycle=self.cycle)
md_tsk.link_input_data = [
'%s/inpcrd > inpcrd-{replica}-{cycle}'.format(
replica=rid, cycle=self.cycle) % replica_sandbox,
'%s/prmtop' % replica_sandbox,
'%s/mdin-{replica}-{cycle} > mdin'.format(
replica=rid, cycle=self.cycle) % replica_sandbox
]
md_tsk.arguments = [
'-O', '-i', 'mdin', '-p', 'prmtop', '-c',
'inpcrd-{replica}-{cycle}'.format(replica=rid,
cycle=self.cycle), '-o',
'out', '-x', 'mdcrd', '-r',
'%s/inpcrd-{replica}-{cycle}'.format(
replica=rid, cycle=self.cycle + 1) % replica_sandbox,
'-inf', '%s/mdinfo-{replica}-{cycle}'.format(
replica=rid, cycle=self.cycle) % replica_sandbox
]
md_tsk.executable = [md_executable]
md_tsk.cpu_reqs = {
'processes': replica_cores,
'process_type': '',
'threads_per_process': 1,
'thread_type': None
}
md_tsk.pre_exec = ['echo $SHARED']
md_stg.add_tasks(md_tsk)
md_stg.post_exec = {
'condition': post_md,
'on_true': start_ex,
'on_false': suspend_replica
}
return md_stg
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def add_ex_stg(rid, cycle):
# ex stg here
ex_tsk = re.Task()
ex_stg = re.Stage()
ex_tsk.name = 'extsk-{replica}-{cycle}'.format(replica=rid,
cycle=cycle)
for rid in range(len(waiting_replicas)):
ex_tsk.link_input_data += [
'%s/mdinfo-{replica}-{cycle}'.format(
replica=rid, cycle=self.cycle) % replica_sandbox
]
ex_tsk.arguments = ['t_ex_gibbs.py',
len(waiting_replicas)
] # This needs to be fixed
ex_tsk.executable = ['python']
ex_tsk.cpu_reqs = {
'processes': 1,
'process_type': '',
'threads_per_process': 1,
'thread_type': None
}
ex_stg.add_tasks(ex_tsk)
ex_stg.post_exec = {
'condition': post_ex,
'on_true': terminate_replicas,
'on_false': continue_md
}
return ex_stg
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def post_md():
global replica_cycles
print 'replica cyles: %s [%]' % (replica_cycles, rid)
self.cycle += 1
replica_cycles[rid] += 1
print 'replica cyles: %s' % replica_cycles
waiting_replicas.append(rid)
if len(waiting_replicas) < max_waiting_list:
return False
return True
# ----------------------------------------------------------------------
def suspend_replica():
p_replica.suspend()
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def start_ex():
ex_stg = add_ex_stg(rid, cycle=self.cycle)
p_replica.add_stages(ex_stg)
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def post_ex():
if cycle > min_completed_cycles:
return True
return False
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def terminate_replicas():
# Resume all replicas in list without adding stages
for rid in waiting_replicas:
replica_pipelines[rid].resume()
print "DONE"
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
def continue_md():
# This needs to resume replica_pipelines[rid]
# for all rid's in wait list
print "continuing replicas"
global waiting_replicas
for rid in waiting_replicas:
try:
md_stg = add_md_stg(rid, cycle)
replica_pipelines[rid].add_stages(md_stg)
if replica_pipelines[rid] is rid:
pass
else:
replica_pipelines[rid].resume()
# This is throwing an error: cannot resume itself since
# it is not suspended. Since the pipeline that is
# triggering this choice is NOT suspended,
# pipeline.resume() fails. This seems to be happening on
# ALL pipelines somehow.
except:
print "replica is not suspended, cannot resume"
waiting_replicas = []
# ----------------------------------------------------------------------
p_replica = re.Pipeline()
p_replica.name = 'p_{rid}'.format(rid=rid)
md_stg = add_md_stg(rid, cycle)
p_replica.add_stages(md_stg)
return p_replica
