def _workflow_run(template, ffjfile, output):
"""
The solve workflow.
"""
femfatjob = FemfatJob(ffjfile)
##################################
# Collect needed resources.
##################################
femfatjob.jobname = ask_jobname()["jobname"]
femfatjob.timelimit = 60 * int(ask_timelimit()["timelimit"])
femfatjob.femfat_module = ask_femfat_module()
femfatjob.cpus = ask_cpus_int()["cpus"]
femfatjob.partitions = ask_partitions()["partitions"]
##########################################
# Info gathered, dispatch to job rendering
##########################################
templates_dir = os.path.join(os.path.dirname(jobbers.femfat.__file__),
"templates")
if template:
femfatjob.template = template
else:
femfat_template = config["femfat"]["template"].get()
femfatjob.template = "{}/{}".format(templates_dir, femfat_template)
render_to_out(femfatjob, output)
def _workflow_solve(template, inpfile, output):
"""
The solve workflow.
"""
solvejob = SolveJob(inpfile)
# Collect needed resources.
solvejob.abaqus_module = ask_abaqus_module()
solvejob.cpus = ask_cpus_int()["cpus"]
solvejob.abaqus_licenses = ask_abaqus_licenses()
solvejob.partitions = ask_partitions()["partitions"]
# Info gathered, dispatch to job rendering
templates_dir = os.path.join(os.path.dirname(jobbers.abaqus.__file__),
"templates")
if template:
solvejob.template = template
else:
solve_template = config["abaqus"]["solve_template"].get()
solvejob.template = "{}/{}".format(templates_dir, solve_template)
render_to_out(solvejob, output)
def _workflow_run(template, output):
"""
The solve workflow.
"""
tensorjob = TensorJob()
##################################
## Collect needed resources.
##################################
tensorjob.jobname = ask_jobname()['jobname']
tensorjob.timelimit = 60 * int(ask_timelimit()['timelimit'])
tensorjob.gpus = ask_gpus()['gpus']
tensorjob.partitions = ask_partitions()['partitions']
##########################################
# Info gathered, dispatch to job rendering
##########################################
templates_dir = os.path.join(os.path.dirname(jobbers.tensorflow.__file__),
'templates')
if template:
tensorjob.template = template
else:
tensor_template = config['tensorflow']['template'].get()
tensorjob.template = "{}/{}".format(templates_dir, tensor_template)
render_to_out(tensorjob, output)
def _workflow_solve(template, inpfile, output):
"""
The solve workflow.
"""
solvejob = SolveJob(inpfile)
##################################
## Collect needed resources.
##################################
solvejob.abaqus_module = ask_abaqus_module()
solvejob.cpus = ask_cpus_int()['cpus']
lics_needed = calculate_abaqus_licenses(solvejob.cpus)
solvejob.abaqus_licenses = ask_abaqus_licenses()
solvejob.partitions = ask_partitions()['partitions']
##########################################
# Info gathered, dispatch to job rendering
##########################################
templates_dir = os.path.join(os.path.dirname(jobbers.abaqus.__file__),
'templates')
if template:
solvejob.template = template
else:
solve_template = config['abaqus']['solve_template'].get()
solvejob.template = "{}/{}".format(templates_dir, solve_template)
render_to_out(solvejob, output)
def _workflow_generic(template, output):
generic_job = GenericJob()
generic_job.generic_resources = ask_generic_resources()
templates_dir = os.path.join(os.path.dirname(jobbers.abaqus.__file__),
"templates")
if template:
generic_job.template = template
else:
generic_job.template = "{}/{}".format(templates_dir,
"abaqus-generic-template.j2")
render_to_out(generic_job, output)
def _workflow_solve_parallel(template, inpfile, output):
"""
The solve-parallel sub workflow.
"""
solvejob = SolveJob(inpfile)
##################################
## Collect needed resources.
##################################
solvejob.abaqus_module = ask_abaqus_module()
solvejob.jobname = ask_jobname()['jobname']
solvejob.nodes = ask_nodes()['nodes']
solvejob.ntasks_per_node = 36 # We guess that cores =36 based on cluster sizes
solvejob.cpus = int(solvejob.nodes * solvejob.ntasks_per_node)
lics_needed = calculate_abaqus_licenses(solvejob.cpus)
# solvejob.abaqus_licenses = ask_abaqus_licenses_parallel()
solvejob.abaqus_licenses = {
'license': 'abaqus@flex_host',
'volume': lics_needed
}
solvejob.scratch = ask_scratch()['scratch']
solvejob.partitions = ask_partitions()['partitions']
solvejob.timelimit = int(ask_timelimit()['timelimit']) * 60
##########################################
# Info gathered, dispatch to job rendering
##########################################
templates_dir = os.path.join(os.path.dirname(jobbers.abaqus.__file__),
'templates')
if template:
solvejob.template = template
else:
solve_par_template = config['abaqus']['solve_parallel_template'].get()
solvejob.template = "{}/{}".format(templates_dir,
str(solve_par_template))
render_to_out(solvejob, output)
def _workflow_solve_parallel(template, inpfile, output):
"""
The solve-parallel sub workflow.
"""
solvejob = SolveJob(inpfile)
# If job is a restart read job, ask for restart files.
if inpfile.restart_read:
restartfile = ask_restart()
solvejob.restartjobname = os.path.splitext(
os.path.basename(str(restartfile)))[0]
solvejob.inpfile.restart_file = solvejob.restartjobname
# Collect needed resources.
solvejob.abaqus_module = ask_abaqus_module()
solvejob.jobname = ask_jobname(solvejob.inpfile.file.stem)["jobname"]
solvejob.nodes = ask_nodes()["nodes"]
# TODO: This should not be hardcoded here. Cluster config?
# SLURM alternative --mincpus <n> Controls the minimum number of CPUs allocated per node as the number
# as the number of nodes is set and exclusive mode is used. Only relevant then whe have more type of hardware´s
solvejob.ntasks_per_node = 36 # We guess that cores =36 based on cluster sizes
solvejob.cpus = int(solvejob.nodes * solvejob.ntasks_per_node)
lics_needed = calculate_abaqus_licenses(solvejob.cpus)
solvejob.abaqus_licenses = {
"license": "abaqus@slurmdbd",
"volume": lics_needed
}
# solvejob.abaqus_licenses = ask_abaqus_licenses_parallel()
solvejob.abaqus_licenses = {
"license": "abaqus@slurmdbd",
"volume": lics_needed
}
# 20190521: Do not ask for scratch at the moment, go with config default /jhacxc
# solvejob.scratch = ask_scratch()['scratch']
solvejob.scratch = config["slurm"]["shared_scratch"].get()
# 20190521: Do not ask for partitions at the moment, go with config defaults /jhacxc
# solvejob.partitions = ask_partitions()['partitions']
solvejob.partitions.append(config["slurm"]["default_partition"].get())
solvejob.timelimit = int(ask_timelimit()["timelimit"]) * 60
# Ask for masternode mem (GiB), convert to MiB which is Slurm default
# Note: Multiply by 950 (not 1024) to make sure limit is below memory output of 'slurm -C'
solvejob.masternode_mem = int(
float(ask_masternode_mem()["memory"]) * float(1024) * 0.95)
# For distributed jobs, explicitly set worker node limit if defined
try:
solvejob.workernode_mem = int(
float(config["abaqus"]["workernode_mem_default"].get()[0]) *
1024.0 * 0.95)
except confuse.NotFoundError:
pass
# Info gathered, dispatch to job rendering
templates_dir = os.path.join(os.path.dirname(jobbers.abaqus.__file__),
"templates")
if template:
solvejob.template = template
else:
solve_par_template = config["abaqus"][
"solve_distributed_template"].get()
solvejob.template = str(pathlib.Path(templates_dir,
solve_par_template))
render_to_out(solvejob, output)
def _workflow_solve_eigen(template, inpfile, output):
"""
The solve-eigen sub workflow.
"""
solvejob = SolveJob(inpfile)
# If job is a restart read job, ask for restart files.
if inpfile.restart_read:
restartfile = ask_restart()
solvejob.restartjobname = os.path.splitext(
os.path.basename(str(restartfile)))[0]
solvejob.inpfile.restart_file = solvejob.restartjobname
##################################
# Collect needed resources.
##################################
solvejob.abaqus_module = ask_abaqus_module()
solvejob.jobname = ask_jobname(solvejob.inpfile.file.stem)["jobname"]
solvejob.nodes = 1
solvejob.gpus = ask_gpus_bool()["gpus"]
# TODO: This should not be hardcoded here. Cluster config?
solvejob.ntasks_per_node = 36 # We guess that cores =36 based on cluster sizes
solvejob.cpus = int(solvejob.nodes * solvejob.ntasks_per_node)
lics_needed = calculate_abaqus_licenses(solvejob.cpus)
# solvejob.abaqus_licenses = ask_abaqus_licenses_parallel()
solvejob.abaqus_licenses = {
"license": "abaqus@slurmdbd",
"volume": lics_needed
}
# 20190521: Do not ask for scratch at the moment, go with config default /jhacxc
# solvejob.scratch = ask_scratch()['scratch']
solvejob.scratch = config["slurm"]["shared_scratch"].get()
# 20190521: Do not ask for partitions at the moment, go with config defaults /jhacxc
# solvejob.partitions = ask_partitions()['partitions']
solvejob.partitions.append(config["slurm"]["default_partition"].get())
solvejob.timelimit = int(ask_timelimit()["timelimit"]) * 60
# Ask for masternode mem (GiB), convert to MiB which is Slurm default
# Note: Multiply by 950 (not 1024) to make sure limit is below memory output of 'slurm -C'
# TEMPHACK: If GPU is selected, do not ask for mem, set over 1TB to get a GPU node
if solvejob.gpus:
solvejob.masternode_mem = 1048576
else:
solvejob.masternode_mem = int(
float(ask_masternode_mem()["memory"]) * float(1024) * 0.95)
##########################################
# Info gathered, dispatch to job rendering
##########################################
templates_dir = os.path.join(os.path.dirname(jobbers.abaqus.__file__),
"templates")
if template:
solvejob.template = template
else:
solve_par_template = config["abaqus"][
"solve_eigenfrequency_template"].get()
# solvejob.template = "{}/{}".format(templates_dir, str(solve_par_template))
solvejob.template = str(pathlib.Path(templates_dir,
solve_par_template))
render_to_out(solvejob, output)
