def run_task(self, fw_spec):
handler_groups = {
"default": [VaspErrorHandler(), MeshSymmetryErrorHandler(),
UnconvergedErrorHandler(), NonConvergingErrorHandler(),
PotimErrorHandler(), PositiveEnergyErrorHandler(),
FrozenJobErrorHandler()],
"strict": [VaspErrorHandler(), MeshSymmetryErrorHandler(),
UnconvergedErrorHandler(), NonConvergingErrorHandler(),
PotimErrorHandler(), PositiveEnergyErrorHandler(),
FrozenJobErrorHandler(), AliasingErrorHandler()],
"md": [VaspErrorHandler(), NonConvergingErrorHandler()],
"no_handler": []
}
vasp_cmd = env_chk(self["vasp_cmd"], fw_spec)
if isinstance(vasp_cmd, six.string_types):
vasp_cmd = os.path.expandvars(vasp_cmd)
vasp_cmd = shlex.split(vasp_cmd)
# initialize variables
job_type = self.get("job_type", "normal")
scratch_dir = env_chk(self.get("scratch_dir"), fw_spec)
gzip_output = self.get("gzip_output", True)
max_errors = self.get("max_errors", 5)
auto_npar = env_chk(self.get("auto_npar"), fw_spec, strict=False, default=False)
gamma_vasp_cmd = env_chk(self.get("gamma_vasp_cmd"), fw_spec, strict=False, default=None)
if gamma_vasp_cmd:
gamma_vasp_cmd = shlex.split(gamma_vasp_cmd)
# construct jobs
if job_type == "normal":
jobs = [VaspJob(vasp_cmd, auto_npar=auto_npar, gamma_vasp_cmd=gamma_vasp_cmd)]
elif job_type == "double_relaxation_run":
jobs = VaspJob.double_relaxation_run(vasp_cmd, auto_npar=auto_npar, ediffg=self.get("ediffg"),
half_kpts_first_relax=False)
elif job_type == "full_opt_run":
jobs = VaspJob.full_opt_run(vasp_cmd, auto_npar=auto_npar, ediffg=self.get("ediffg"),
max_steps=5, half_kpts_first_relax=False)
else:
raise ValueError("Unsupported job type: {}".format(job_type))
# construct handlers
handlers = handler_groups[self.get("handler_group", "default")]
if self.get("max_force_threshold"):
handlers.append(MaxForceErrorHandler(max_force_threshold=self["max_force_threshold"]))
if self.get("wall_time"):
handlers.append(WalltimeHandler(wall_time=self["wall_time"]))
validators = [VasprunXMLValidator()]
c = Custodian(handlers, jobs, validators=validators, max_errors=max_errors,
scratch_dir=scratch_dir, gzipped_output=gzip_output)
c.run()
def runvasp(cmd,
opt=False,
max_errors=3,
backup=False,
auto_gamma=False,
auto_npar=False,
ediffg=-.05):
"""
cmd example:
cmd=['mpirun', '-np', '32' , '-machinefile', 'hosts','vasp_std']
"""
if opt:
jobs = cvj.full_opt_run(cmd,
auto_npar=auto_npar,
ediffg=ediffg,
backup=backup,
auto_gamma=auto_gamma)
else:
jobs = [
cvj(cmd, auto_npar=auto_npar, backup=backup, auto_gamma=auto_gamma)
]
c = Custodian(handlers, jobs, validators=validators, max_errors=max_errors)
c.run()
def run_task(self, fw_spec):
handler_groups = {
"default": [
VaspErrorHandler(),
MeshSymmetryErrorHandler(),
UnconvergedErrorHandler(),
NonConvergingErrorHandler(),
PotimErrorHandler(),
PositiveEnergyErrorHandler(),
FrozenJobErrorHandler(),
StdErrHandler(),
DriftErrorHandler()
],
"strict": [
VaspErrorHandler(),
MeshSymmetryErrorHandler(),
UnconvergedErrorHandler(),
NonConvergingErrorHandler(),
PotimErrorHandler(),
PositiveEnergyErrorHandler(),
FrozenJobErrorHandler(),
StdErrHandler(),
AliasingErrorHandler(),
DriftErrorHandler()
],
"md": [VaspErrorHandler(),
NonConvergingErrorHandler()],
"no_handler": []
}
vasp_cmd = env_chk(self["vasp_cmd"], fw_spec)
if isinstance(vasp_cmd, str):
vasp_cmd = os.path.expandvars(vasp_cmd)
vasp_cmd = shlex.split(vasp_cmd)
# initialize variables
job_type = self.get("job_type", "normal")
scratch_dir = env_chk(self.get("scratch_dir"), fw_spec)
gzip_output = self.get("gzip_output", True)
max_errors = self.get("max_errors", CUSTODIAN_MAX_ERRORS)
auto_npar = env_chk(self.get("auto_npar"),
fw_spec,
strict=False,
default=False)
gamma_vasp_cmd = env_chk(self.get("gamma_vasp_cmd"),
fw_spec,
strict=False,
default=None)
if gamma_vasp_cmd:
gamma_vasp_cmd = shlex.split(gamma_vasp_cmd)
# construct jobs
if job_type == "normal":
jobs = [
VaspJob(vasp_cmd,
auto_npar=auto_npar,
gamma_vasp_cmd=gamma_vasp_cmd)
]
elif job_type == "double_relaxation_run":
jobs = VaspJob.double_relaxation_run(
vasp_cmd,
auto_npar=auto_npar,
ediffg=self.get("ediffg"),
half_kpts_first_relax=self.get("half_kpts_first_relax",
HALF_KPOINTS_FIRST_RELAX))
elif job_type == "metagga_opt_run":
jobs = VaspJob.metagga_opt_run(vasp_cmd,
auto_npar=auto_npar,
ediffg=self.get("ediffg"),
half_kpts_first_relax=self.get(
"half_kpts_first_relax",
HALF_KPOINTS_FIRST_RELAX))
elif job_type == "full_opt_run":
jobs = VaspJob.full_opt_run(vasp_cmd,
auto_npar=auto_npar,
ediffg=self.get("ediffg"),
max_steps=9,
half_kpts_first_relax=self.get(
"half_kpts_first_relax",
HALF_KPOINTS_FIRST_RELAX))
elif job_type == "neb":
# TODO: @shyuep @HanmeiTang This means that NEB can only be run (i) in reservation mode
# and (ii) when the queueadapter parameter is overridden and (iii) the queue adapter
# has a convention for nnodes (with that name). Can't the number of nodes be made a
# parameter that the user sets differently? e.g., fw_spec["neb_nnodes"] must be set
# when setting job_type=NEB? Then someone can use this feature in non-reservation
# mode and without this complication. -computron
nnodes = int(fw_spec["_queueadapter"]["nnodes"])
# TODO: @shyuep @HanmeiTang - I am not sure what the code below is doing. It looks like
# it is trying to override the number of processors. But I tried running the code
# below after setting "vasp_cmd = 'mpirun -n 16 vasp'" and the code fails.
# (i) Is this expecting an array vasp_cmd rather than String? If so, that's opposite to
# the rest of this task's convention and documentation
# (ii) can we get rid of this hacking in the first place? e.g., allowing the user to
# separately set the NEB_VASP_CMD as an env_variable and not rewriting the command
# inside this.
# -computron
# Index the tag "-n" or "-np"
index = [i for i, s in enumerate(vasp_cmd) if '-n' in s]
ppn = int(vasp_cmd[index[0] + 1])
vasp_cmd[index[0] + 1] = str(nnodes * ppn)
# Do the same for gamma_vasp_cmd
if gamma_vasp_cmd:
index = [i for i, s in enumerate(gamma_vasp_cmd) if '-n' in s]
ppn = int(gamma_vasp_cmd[index[0] + 1])
gamma_vasp_cmd[index[0] + 1] = str(nnodes * ppn)
jobs = [
VaspNEBJob(vasp_cmd,
final=False,
auto_npar=auto_npar,
gamma_vasp_cmd=gamma_vasp_cmd)
]
else:
raise ValueError("Unsupported job type: {}".format(job_type))
# construct handlers
handler_group = self.get("handler_group", "default")
if isinstance(handler_group, str):
handlers = handler_groups[handler_group]
else:
handlers = handler_group
if self.get("max_force_threshold"):
handlers.append(
MaxForceErrorHandler(
max_force_threshold=self["max_force_threshold"]))
if self.get("wall_time"):
handlers.append(WalltimeHandler(wall_time=self["wall_time"]))
if job_type == "neb":
validators = [
] # CINEB vasprun.xml sometimes incomplete, file structure different
else:
validators = [VasprunXMLValidator(), VaspFilesValidator()]
c = Custodian(handlers,
jobs,
validators=validators,
max_errors=max_errors,
scratch_dir=scratch_dir,
gzipped_output=gzip_output)
c.run()
if os.path.exists(zpath("custodian.json")):
stored_custodian_data = {
"custodian": loadfn(zpath("custodian.json"))
}
return FWAction(stored_data=stored_custodian_data)
def run_task(self, fw_spec):
handler_groups = {
"default": [
VaspErrorHandler(),
MeshSymmetryErrorHandler(),
UnconvergedErrorHandler(),
NonConvergingErrorHandler(),
PotimErrorHandler(),
PositiveEnergyErrorHandler(),
FrozenJobErrorHandler()
],
"strict": [
VaspErrorHandler(),
MeshSymmetryErrorHandler(),
UnconvergedErrorHandler(),
NonConvergingErrorHandler(),
PotimErrorHandler(),
PositiveEnergyErrorHandler(),
FrozenJobErrorHandler(),
AliasingErrorHandler()
],
"md": [VaspErrorHandler(),
NonConvergingErrorHandler()],
"no_handler": []
}
vasp_cmd = env_chk(self["vasp_cmd"], fw_spec)
if isinstance(vasp_cmd, six.string_types):
vasp_cmd = os.path.expandvars(vasp_cmd)
vasp_cmd = shlex.split(vasp_cmd)
# initialize variables
job_type = self.get("job_type", "normal")
scratch_dir = env_chk(self.get("scratch_dir"), fw_spec)
gzip_output = self.get("gzip_output", True)
max_errors = self.get("max_errors", 5)
auto_npar = env_chk(self.get("auto_npar"),
fw_spec,
strict=False,
default=False)
gamma_vasp_cmd = env_chk(self.get("gamma_vasp_cmd"),
fw_spec,
strict=False,
default=None)
if gamma_vasp_cmd:
gamma_vasp_cmd = shlex.split(gamma_vasp_cmd)
# construct jobs
if job_type == "normal":
jobs = [
VaspJob(vasp_cmd,
auto_npar=auto_npar,
gamma_vasp_cmd=gamma_vasp_cmd)
]
elif job_type == "double_relaxation_run":
jobs = VaspJob.double_relaxation_run(vasp_cmd,
auto_npar=auto_npar,
ediffg=self.get("ediffg"),
half_kpts_first_relax=False)
elif job_type == "full_opt_run":
jobs = VaspJob.full_opt_run(vasp_cmd,
auto_npar=auto_npar,
ediffg=self.get("ediffg"),
max_steps=5,
half_kpts_first_relax=False)
else:
raise ValueError("Unsupported job type: {}".format(job_type))
# construct handlers
handlers = handler_groups[self.get("handler_group", "default")]
if self.get("max_force_threshold"):
handlers.append(
MaxForceErrorHandler(
max_force_threshold=self["max_force_threshold"]))
if self.get("wall_time"):
handlers.append(WalltimeHandler(wall_time=self["wall_time"]))
validators = [VasprunXMLValidator()]
c = Custodian(handlers,
jobs,
validators=validators,
max_errors=max_errors,
scratch_dir=scratch_dir,
gzipped_output=gzip_output)
c.run()
def get_jobs(args):
# Returns a generator of jobs. Allows of "infinite" jobs.
vasp_command = args.command.split()
# save initial INCAR for rampU runs
n_ramp_u = args.jobs.count('rampU')
ramps = 0
if n_ramp_u:
incar = Incar.from_file('INCAR')
ldauu = incar['LDAUU']
ldauj = incar['LDAUJ']
njobs = len(args.jobs)
post_settings = [
] # append to this list to have settings applied on next job
for i, job in enumerate(args.jobs):
final = False if i != njobs - 1 else True
if any(c.isdigit() for c in job):
suffix = "." + job
else:
suffix = ".{}{}".format(job, i + 1)
settings = post_settings
post_settings = []
backup = True if i == 0 else False
copy_magmom = False
vinput = VaspInput.from_directory(".")
if i > 0:
settings.append({
"file": "CONTCAR",
"action": {
"_file_copy": {
"dest": "POSCAR"
}
}
})
job_type = job.lower()
auto_npar = True
if args.no_auto_npar:
auto_npar = False
if job_type.startswith("static_derived"):
from pymatgen.io.vasp.sets import MPStaticSet
vis = MPStaticSet.from_prev_calc(".",
user_incar_settings={
"LWAVE": True,
"EDIFF": 1e-6
},
ediff_per_atom=False)
settings.extend([{
"dict": "INCAR",
"action": {
"_set": dict(vis.incar)
}
}, {
'dict': 'KPOINTS',
'action': {
'_set': vis.kpoints.as_dict()
}
}])
if job_type.startswith("static_dielectric_derived"):
from pymatgen.io.vasp.sets import MPStaticSet, MPStaticDielectricDFPTVaspInputSet
# vis = MPStaticSet.from_prev_calc(
# ".", user_incar_settings={"EDIFF": 1e-6, "IBRION": 8,
# "LEPSILON": True, 'LREAL':False,
# "LPEAD": True, "ISMEAR": 0,
# "SIGMA": 0.01},
# ediff_per_atom=False)
vis = MPStaticDielectricDFPTVaspInputSet()
incar = vis.get_incar(vinput["POSCAR"].structure)
unset = {}
for k in [
"NPAR", "KPOINT_BSE", "LAECHG", "LCHARG", "LVHAR", "NSW"
]:
incar.pop(k, None)
if k in vinput["INCAR"]:
unset[k] = 1
kpoints = vis.get_kpoints(vinput["POSCAR"].structure)
settings.extend([{
"dict": "INCAR",
"action": {
"_set": dict(incar),
"_unset": unset
}
}, {
'dict': 'KPOINTS',
'action': {
'_set': kpoints.as_dict()
}
}])
auto_npar = False
elif job_type.startswith("static"):
m = [i * args.static_kpoint for i in vinput["KPOINTS"].kpts[0]]
settings.extend([{
"dict": "INCAR",
"action": {
"_set": {
"NSW": 0
}
}
}, {
'dict': 'KPOINTS',
'action': {
'_set': {
'kpoints': [m]
}
}
}])
elif job_type.startswith("nonscf_derived"):
from pymatgen.io.vasp.sets import MPNonSCFSet
vis = MPNonSCFSet.from_prev_calc(
".", copy_chgcar=False, user_incar_settings={"LWAVE": True})
settings.extend([{
"dict": "INCAR",
"action": {
"_set": dict(vis.incar)
}
}, {
'dict': 'KPOINTS',
'action': {
'_set': vis.kpoints.as_dict()
}
}])
elif job_type.startswith("optics_derived"):
from pymatgen.io.vasp.sets import MPNonSCFSet
vis = MPNonSCFSet.from_prev_calc(".",
optics=True,
copy_chgcar=False,
nedos=2001,
mode="uniform",
nbands_factor=5,
user_incar_settings={
"LWAVE": True,
"ALGO": "Exact",
"SIGMA": 0.01,
"EDIFF": 1e-6
},
ediff_per_atom=False)
settings.extend([{
"dict": "INCAR",
"action": {
"_set": dict(vis.incar)
}
}, {
'dict': 'KPOINTS',
'action': {
'_set': vis.kpoints.as_dict()
}
}])
elif job_type.startswith("rampu"):
f = ramps / (n_ramp_u - 1)
settings.append({
"dict": "INCAR",
"action": {
"_set": {
"LDAUJ": [j * f for j in ldauj],
"LDAUU": [u * f for u in ldauu]
}
}
})
copy_magmom = True
ramps += 1
elif job_type.startswith("quick_relax") or job_type.startswith(\
"quickrelax"):
kpoints = vinput["KPOINTS"]
incar = vinput["INCAR"]
structure = vinput["POSCAR"].structure
if "ISMEAR" in incar:
post_settings.append({
"dict": "INCAR",
"action": {
"_set": {
"ISMEAR": incar["ISMEAR"]
}
}
})
else:
post_settings.append({
"dict": "INCAR",
"action": {
"_unset": {
"ISMEAR": 1
}
}
})
post_settings.append({
"dict": "KPOINTS",
"action": {
"_set": kpoints.as_dict()
}
})
# lattice vectors with length < 9 will get >1 KPOINT
low_kpoints = Kpoints.gamma_automatic(
[max(int(18 / l), 1) for l in structure.lattice.abc])
settings.extend([{
"dict": "INCAR",
"action": {
"_set": {
"ISMEAR": 0
}
}
}, {
'dict': 'KPOINTS',
'action': {
'_set': low_kpoints.as_dict()
}
}])
# let vasp determine encut (will be lower than
# needed for compatibility with other runs)
if "ENCUT" in incar:
post_settings.append({
"dict": "INCAR",
"action": {
"_set": {
"ENCUT": incar["ENCUT"]
}
}
})
settings.append({
"dict": "INCAR",
"action": {
"_unset": {
"ENCUT": 1
}
}
})
elif job_type.startswith("relax"):
pass
elif job_type.startswith("full_relax"):
for j in VaspJob.full_opt_run(vasp_command):
yield j
else:
print("Unsupported job type: {}".format(job))
sys.exit(-1)
if not job_type.startswith("full_relax"):
yield VaspJob(vasp_command,
final=final,
suffix=suffix,
backup=backup,
settings_override=settings,
copy_magmom=copy_magmom,
auto_npar=auto_npar)
def get_jobs(args):
# Returns a generator of jobs. Allows of "infinite" jobs.
vasp_command = args.command.split()
# save initial INCAR for rampU runs
n_ramp_u = args.jobs.count('rampU')
ramps = 0
if n_ramp_u:
incar = Incar.from_file('INCAR')
ldauu = incar['LDAUU']
ldauj = incar['LDAUJ']
njobs = len(args.jobs)
post_settings = [] # append to this list to have settings applied on next job
for i, job in enumerate(args.jobs):
final = False if i != njobs - 1 else True
if any(c.isdigit() for c in job):
suffix = "." + job
else:
suffix = ".{}{}".format(job, i + 1)
settings = post_settings
post_settings = []
backup = True if i == 0 else False
copy_magmom = False
vinput = VaspInput.from_directory(".")
if i > 0:
settings.append(
{"file": "CONTCAR",
"action": {"_file_copy": {"dest": "POSCAR"}}})
job_type = job.lower()
auto_npar = True
if args.no_auto_npar:
auto_npar = False
if job_type.startswith("static_derived"):
from pymatgen.io.vasp.sets import MPStaticSet
vis = MPStaticSet.from_prev_calc(
".", user_incar_settings={"LWAVE": True, "EDIFF": 1e-6},
ediff_per_atom=False)
settings.extend([
{"dict" : "INCAR",
"action": {"_set": dict(vis.incar)}},
{'dict': 'KPOINTS',
'action': {'_set': vis.kpoints.as_dict()}}])
if job_type.startswith("static_dielectric_derived"):
from pymatgen.io.vasp.sets import MPStaticSet, MPStaticDielectricDFPTVaspInputSet
# vis = MPStaticSet.from_prev_calc(
# ".", user_incar_settings={"EDIFF": 1e-6, "IBRION": 8,
# "LEPSILON": True, 'LREAL':False,
# "LPEAD": True, "ISMEAR": 0,
# "SIGMA": 0.01},
# ediff_per_atom=False)
vis = MPStaticDielectricDFPTVaspInputSet()
incar = vis.get_incar(vinput["POSCAR"].structure)
unset = {}
for k in ["NPAR", "KPOINT_BSE", "LAECHG", "LCHARG", "LVHAR",
"NSW"]:
incar.pop(k, None)
if k in vinput["INCAR"]:
unset[k] = 1
kpoints = vis.get_kpoints(vinput["POSCAR"].structure)
settings.extend([
{"dict": "INCAR",
"action": {"_set": dict(incar),
"_unset": unset}},
{'dict': 'KPOINTS',
'action': {'_set': kpoints.as_dict()}}])
auto_npar = False
elif job_type.startswith("static"):
m = [i * args.static_kpoint for i in vinput["KPOINTS"].kpts[0]]
settings.extend([
{"dict": "INCAR",
"action": {"_set": {"NSW": 0}}},
{'dict': 'KPOINTS',
'action': {'_set': {'kpoints': [m]}}}])
elif job_type.startswith("nonscf_derived"):
from pymatgen.io.vasp.sets import MPNonSCFSet
vis = MPNonSCFSet.from_prev_calc(".", copy_chgcar=False,
user_incar_settings={"LWAVE": True})
settings.extend([
{"dict": "INCAR",
"action": {"_set": dict(vis.incar)}},
{'dict': 'KPOINTS',
'action': {'_set': vis.kpoints.as_dict()}}])
elif job_type.startswith("optics_derived"):
from pymatgen.io.vasp.sets import MPNonSCFSet
vis = MPNonSCFSet.from_prev_calc(
".", optics=True, copy_chgcar=False,
nedos=2001, mode="uniform", nbands_factor=5,
user_incar_settings={"LWAVE": True, "ALGO": "Exact", "SIGMA": 0.01, "EDIFF": 1e-6},
ediff_per_atom=False)
settings.extend([
{"dict": "INCAR",
"action": {"_set": dict(vis.incar)}},
{'dict': 'KPOINTS',
'action': {'_set': vis.kpoints.as_dict()}}])
elif job_type.startswith("rampu"):
f = ramps / (n_ramp_u - 1)
settings.append(
{"dict": "INCAR",
"action": {"_set": {"LDAUJ": [j * f for j in ldauj],
"LDAUU": [u * f for u in ldauu]}}})
copy_magmom = True
ramps += 1
elif job_type.startswith("quick_relax") or job_type.startswith(\
"quickrelax"):
kpoints = vinput["KPOINTS"]
incar = vinput["INCAR"]
structure = vinput["POSCAR"].structure
if "ISMEAR" in incar:
post_settings.append(
{"dict": "INCAR",
"action": {"_set": {"ISMEAR": incar["ISMEAR"]}}})
else:
post_settings.append(
{"dict": "INCAR",
"action": {"_unset": {"ISMEAR": 1}}})
post_settings.append({"dict": "KPOINTS",
"action": {"_set": kpoints.as_dict()}})
# lattice vectors with length < 9 will get >1 KPOINT
low_kpoints = Kpoints.gamma_automatic(
[max(int(18/l), 1) for l in structure.lattice.abc])
settings.extend([
{"dict": "INCAR",
"action": {"_set": {"ISMEAR": 0}}},
{'dict': 'KPOINTS',
'action': {'_set': low_kpoints.as_dict()}}])
# let vasp determine encut (will be lower than
# needed for compatibility with other runs)
if "ENCUT" in incar:
post_settings.append(
{"dict": "INCAR",
"action": {"_set": {"ENCUT": incar["ENCUT"]}}})
settings.append(
{"dict": "INCAR",
"action": {"_unset": {"ENCUT": 1}}})
elif job_type.startswith("relax"):
pass
elif job_type.startswith("full_relax"):
for j in VaspJob.full_opt_run(
vasp_command):
yield j
else:
print("Unsupported job type: {}".format(job))
sys.exit(-1)
if not job_type.startswith("full_relax"):
yield VaspJob(vasp_command, final=final, suffix=suffix,
backup=backup, settings_override=settings,
copy_magmom=copy_magmom, auto_npar=auto_npar)
def run_task(self, fw_spec):
handler_groups = {
"default": [VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(),
NonConvergingErrorHandler(),PotimErrorHandler(),
PositiveEnergyErrorHandler(), FrozenJobErrorHandler(), StdErrHandler(),
DriftErrorHandler()],
"strict": [VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(),
NonConvergingErrorHandler(),PotimErrorHandler(),
PositiveEnergyErrorHandler(), FrozenJobErrorHandler(),
StdErrHandler(), AliasingErrorHandler(), DriftErrorHandler()],
"md": [VaspErrorHandler(), NonConvergingErrorHandler()],
"no_handler": []
}
vasp_cmd = env_chk(self["vasp_cmd"], fw_spec)
if isinstance(vasp_cmd, six.string_types):
vasp_cmd = os.path.expandvars(vasp_cmd)
vasp_cmd = shlex.split(vasp_cmd)
# initialize variables
job_type = self.get("job_type", "normal")
scratch_dir = env_chk(self.get("scratch_dir"), fw_spec)
gzip_output = self.get("gzip_output", True)
max_errors = self.get("max_errors", 5)
auto_npar = env_chk(self.get("auto_npar"), fw_spec, strict=False, default=False)
gamma_vasp_cmd = env_chk(self.get("gamma_vasp_cmd"), fw_spec, strict=False, default=None)
if gamma_vasp_cmd:
gamma_vasp_cmd = shlex.split(gamma_vasp_cmd)
# construct jobs
if job_type == "normal":
jobs = [VaspJob(vasp_cmd, auto_npar=auto_npar, gamma_vasp_cmd=gamma_vasp_cmd)]
elif job_type == "double_relaxation_run":
jobs = VaspJob.double_relaxation_run(vasp_cmd, auto_npar=auto_npar,
ediffg=self.get("ediffg"),
half_kpts_first_relax=self.get("half_kpts_first_relax", HALF_KPOINTS_FIRST_RELAX))
elif job_type == "metagga_opt_run":
jobs = VaspJob.metagga_opt_run(vasp_cmd, auto_npar=auto_npar,
ediffg=self.get("ediffg"),
half_kpts_first_relax=self.get("half_kpts_first_relax", HALF_KPOINTS_FIRST_RELAX))
elif job_type == "full_opt_run":
jobs = VaspJob.full_opt_run(vasp_cmd, auto_npar=auto_npar,
ediffg=self.get("ediffg"),
max_steps=9,
half_kpts_first_relax=self.get("half_kpts_first_relax", HALF_KPOINTS_FIRST_RELAX))
elif job_type == "neb":
# TODO: @shyuep @HanmeiTang This means that NEB can only be run (i) in reservation mode
# and (ii) when the queueadapter parameter is overridden and (iii) the queue adapter
# has a convention for nnodes (with that name). Can't the number of nodes be made a
# parameter that the user sets differently? e.g., fw_spec["neb_nnodes"] must be set
# when setting job_type=NEB? Then someone can use this feature in non-reservation
# mode and without this complication. -computron
nnodes = int(fw_spec["_queueadapter"]["nnodes"])
# TODO: @shyuep @HanmeiTang - I am not sure what the code below is doing. It looks like
# it is trying to override the number of processors. But I tried running the code
# below after setting "vasp_cmd = 'mpirun -n 16 vasp'" and the code fails.
# (i) Is this expecting an array vasp_cmd rather than String? If so, that's opposite to
# the rest of this task's convention and documentation
# (ii) can we get rid of this hacking in the first place? e.g., allowing the user to
# separately set the NEB_VASP_CMD as an env_variable and not rewriting the command
# inside this.
# -computron
# Index the tag "-n" or "-np"
index = [i for i, s in enumerate(vasp_cmd) if '-n' in s]
ppn = int(vasp_cmd[index[0] + 1])
vasp_cmd[index[0] + 1] = str(nnodes * ppn)
# Do the same for gamma_vasp_cmd
if gamma_vasp_cmd:
index = [i for i, s in enumerate(gamma_vasp_cmd) if '-n' in s]
ppn = int(gamma_vasp_cmd[index[0] + 1])
gamma_vasp_cmd[index[0] + 1] = str(nnodes * ppn)
jobs = [VaspNEBJob(vasp_cmd, final=False, auto_npar=auto_npar,
gamma_vasp_cmd=gamma_vasp_cmd)]
else:
raise ValueError("Unsupported job type: {}".format(job_type))
# construct handlers
handler_group = self.get("handler_group", "default")
if isinstance(handler_group, six.string_types):
handlers = handler_groups[handler_group]
else:
handlers = handler_group
if self.get("max_force_threshold"):
handlers.append(MaxForceErrorHandler(max_force_threshold=self["max_force_threshold"]))
if self.get("wall_time"):
handlers.append(WalltimeHandler(wall_time=self["wall_time"]))
if job_type == "neb":
validators = [] # CINEB vasprun.xml sometimes incomplete, file structure different
else:
validators = [VasprunXMLValidator(), VaspFilesValidator()]
c = Custodian(handlers, jobs, validators=validators, max_errors=max_errors,
scratch_dir=scratch_dir, gzipped_output=gzip_output)
c.run()
if os.path.exists(zpath("custodian.json")):
return FWAction(stored_data=loadfn(zpath("custodian.json")))
