def submit_calculation(task, input_, cluster, image, run_parameters,
root_folder, container_description, input_folder,
output_folder, scratch_folder):
job = _create_job(task, cluster, image, run_parameters,
container_description, input_folder, output_folder,
scratch_folder)
girder_token = task.taskflow.girder_token
task.taskflow.set_metadata('cluster', cluster)
# Now download and submit job to the cluster
task.taskflow.logger.info('Downloading input files to cluster.')
download_job_input_folders(cluster,
job,
girder_token=girder_token,
submit=False)
task.taskflow.logger.info('Downloading complete.')
task.taskflow.logger.info('Submitting job %s to cluster.' % job['_id'])
submit_job(cluster, job, girder_token=girder_token, monitor=False)
monitor_job.apply_async(
(cluster, job), {
'girder_token': girder_token,
'monitor_interval': 10
},
link=postprocess_job.s(input_, cluster, image, run_parameters,
root_folder, container_description,
input_folder, output_folder, scratch_folder,
job))
def submit(task, input_, cluster, run_folder, input_file, input_folder):
job = _create_job(task, cluster, input_file, input_folder)
girder_token = task.taskflow.girder_token
task.taskflow.set_metadata('cluster', cluster)
# Now download and submit job to the cluster
task.taskflow.logger.info('Downloading input files to cluster.')
download_job_input_folders(cluster,
job,
girder_token=girder_token,
submit=False)
task.taskflow.logger.info('Downloading complete.')
task.taskflow.logger.info('Submitting job %s to cluster.' % job['_id'])
girder_token = task.taskflow.girder_token
try:
submit_job(cluster, job, girder_token=girder_token, monitor=False)
except:
import traceback
traceback.print_exc()
monitor_job.apply_async((cluster, job), {
'girder_token': girder_token,
'monitor_interval': 10
},
link=postprocess.s(run_folder, input_, cluster,
job))
def submit(task, job, *args, **kwargs):
task.taskflow.logger.info('Submitting job to cluster.')
girder_token = task.taskflow.girder_token
cluster = kwargs.pop('cluster')
task.taskflow.set_metadata('cluster', cluster)
# Now download and submit job to the cluster
task.logger.info('Uploading input files to cluster.')
download_job_input_folders(cluster, job, log_write_url=None,
girder_token=girder_token, submit=False)
task.logger.info('Uploading complete.')
submit_pyfr_job.delay(cluster, job, *args, **kwargs)
def submit(task, job, *args, **kwargs):
task.taskflow.logger.info('Submitting job to cluster.')
girder_token = task.taskflow.girder_token
cluster = kwargs.pop('cluster')
task.taskflow.set_metadata('cluster', cluster)
# Now download and submit job to the cluster
task.logger.info('Uploading input files to cluster.')
download_job_input_folders(cluster, job, log_write_url=None,
girder_token=girder_token, submit=False)
task.logger.info('Uploading complete.')
submit_pyfr_job.delay(cluster, job, *args, **kwargs)
def start(task, input_, user, cluster, image, run_parameters):
"""
The flow is the following:
- Dry run the container with the -d flag to obtain a description of the input/output formats
- Convert the cjson input geometry to conform to the container's expected format
- Run the container
- Convert the container output format into cjson
- Ingest the output in the database
"""
client = create_girder_client(task.taskflow.girder_api_url,
task.taskflow.girder_token)
if cluster.get('name') == 'cori':
cluster = get_cori(client)
if '_id' not in cluster:
log_and_raise(task, 'Invalid cluster configurations: %s' % cluster)
oc_folder = get_oc_folder(client)
root_folder = client.createFolder(
oc_folder['_id'],
datetime.datetime.now().strftime("%Y_%m_%d-%H_%M_%f"))
# temporary folder to save the container in/out description
description_folder = client.createFolder(root_folder['_id'], 'description')
job = _create_description_job(task, cluster, description_folder, image,
run_parameters)
task.taskflow.logger.info(
'Preparing job to obtain the container description.')
download_job_input_folders(cluster,
job,
girder_token=task.taskflow.girder_token,
submit=False)
task.taskflow.logger.info(
'Submitting job to obtain the container description.')
submit_job(cluster,
job,
girder_token=task.taskflow.girder_token,
monitor=False)
monitor_job.apply_async(
(cluster, job), {
'girder_token': task.taskflow.girder_token,
'monitor_interval': 10
},
countdown=countdown(cluster),
link=postprocess_description.s(input_, user, cluster, image,
run_parameters, root_folder, job,
description_folder))
def start(task, user, cluster, image, container):
client = create_girder_client(task.taskflow.girder_api_url,
task.taskflow.girder_token)
if cluster.get('name') == 'cori':
cluster = get_cori(client)
if '_id' not in cluster:
log_and_raise(task, 'Invalid cluster configurations: %s' % cluster)
oc_folder = get_oc_folder(client)
root_folder = client.createFolder(
oc_folder['_id'],
datetime.datetime.now().strftime("%Y_%m_%d-%H_%M_%f"))
# temporary folder to save the container in/out description
folder = client.createFolder(root_folder['_id'], 'pull_folder')
# save the pull.py script to the job directory
with open(os.path.join(os.path.dirname(__file__), 'utils/pull.py'),
'rb') as f:
# Get the size of the file
size = f.seek(0, 2)
f.seek(0)
name = 'pull.py'
client.uploadFile(folder['_id'], f, name, size, parentType='folder')
job = _create_job(task, cluster, folder, image, container)
# Now download pull.py script to the cluster
task.taskflow.logger.info('Preparing job to pull the container.')
download_job_input_folders(cluster,
job,
girder_token=task.taskflow.girder_token,
submit=False)
task.taskflow.logger.info('Submitting job to pull the container.')
submit_job(cluster,
job,
girder_token=task.taskflow.girder_token,
monitor=False)
monitor_job.apply_async((cluster, job), {
'girder_token': task.taskflow.girder_token,
'monitor_interval': 10
},
countdown=countdown(cluster),
link=postprocess_job.s(user, cluster, image, job,
folder, container))
def submit(task, upstream_result):
task.taskflow.logger.info('Submitting job to cluster.')
girder_token = task.taskflow.girder_token
cluster = upstream_result['cluster']
job = upstream_result['job']
task.taskflow.set_metadata('cluster', cluster)
# Now download and submit job to the cluster
task.logger.info('Uploading input files to cluster.')
download_job_input_folders(cluster, job, log_write_url=None,
girder_token=girder_token, submit=False)
if 'geometryFilename' in upstream_result:
create_geometry_symlink(task, job, cluster, upstream_result['geometryFilename'])
task.logger.info('Uploading complete.')
return upstream_result
def submit_open_foam_job(task, cluster, job, *args, **kwargs):
# Now download job inputs
task.logger.info('Uploading input files to cluster.')
download_job_input_folders(cluster,
job,
log_write_url=None,
girder_token=task.taskflow.girder_token,
submit=False)
task.logger.info('Uploading complete.')
# Setup job parameters
task.taskflow.logger.info('Submitting job to cluster.')
job['params'] = {}
## parallel_environment
parallel_environment = parse('config.parallelEnvironment').find(cluster)
if parallel_environment:
parallel_environment = parallel_environment[0].value
job['params']['parallelEnvironment'] = parallel_environment
## slots
job['params']['numberOfSlots'] = 1
## output dir
job_output_dir = get_cluster_job_output_dir(cluster)
if job_output_dir:
job['params']['jobOutputDir'] = job_output_dir
# Submit job to the queue
submit_job(cluster,
job,
log_write_url=None,
girder_token=task.taskflow.girder_token,
monitor=False)
# Move to the next task
monitor_open_foam_job.delay(cluster, job, *args, **kwargs)
def start(task, input_, cluster, image, run_parameters):
"""
The flow is the following:
- Dry run the container with the -d flag to obtain a description of the input/output formats
- Convert the cjson input geometry to conform to the container's expected format
- Run the container
- Convert the container output format into cjson
- Ingest the output in the database
"""
client = create_girder_client(task.taskflow.girder_api_url,
task.taskflow.girder_token)
if cluster.get('name') == 'cori':
cluster = _get_cori(client)
if '_id' not in cluster:
raise Exception('Invalid cluster configurations: %s' % cluster)
oc_folder = _get_oc_folder(client)
root_folder = client.createFolder(
oc_folder['_id'],
datetime.datetime.now().strftime("%Y_%m_%d-%H_%M_%f"))
# temporary folder to save the container in/out description
description_folder = client.createFolder(root_folder['_id'], 'description')
# save the pull.py script to the job directory
with open(os.path.join(os.path.dirname(__file__), 'utils/pull.py'),
'rb') as f:
# Get the size of the file
size = f.seek(0, 2)
f.seek(0)
name = 'pull.py'
input_parameters_file = client.uploadFile(description_folder['_id'],
f,
name,
size,
parentType='folder')
job = _create_description_job(task, cluster, description_folder, image,
run_parameters)
# Now download pull.py script to the cluster
task.taskflow.logger.info(
'Downloading description input files to cluster.')
download_job_input_folders(cluster,
job,
girder_token=task.taskflow.girder_token,
submit=False)
task.taskflow.logger.info('Downloading complete.')
submit_job(cluster,
job,
girder_token=task.taskflow.girder_token,
monitor=False)
monitor_job.apply_async(
(cluster, job), {
'girder_token': task.taskflow.girder_token,
'monitor_interval': 10
},
link=postprocess_description.s(input_, cluster, image, run_parameters,
root_folder, job, description_folder))
