def Args(cls, parser):
dataproc = dp.Dataproc(cls.ReleaseTrack())
base.ASYNC_FLAG.AddToParser(parser)
flags.AddClusterResourceArg(parser, 'create', dataproc.api_version)
clusters.ArgsForClusterRef(
parser,
dataproc,
cls.BETA,
include_ttl_config=True,
include_gke_platform_args=cls.BETA)
# Add arguments for failure action for primary workers
if not cls.BETA:
parser.add_argument(
'--action-on-failed-primary-workers',
choices={
'NO_ACTION': 'take no action',
'DELETE': 'delete the failed primary workers',
'FAILURE_ACTION_UNSPECIFIED': 'failure action is not specified'
},
type=arg_utils.ChoiceToEnumName,
help="""
Failure action to take when primary workers fail during cluster creation
""")
# Add gce-pd-kms-key args
kms_flag_overrides = {
'kms-key': '--gce-pd-kms-key',
'kms-keyring': '--gce-pd-kms-key-keyring',
'kms-location': '--gce-pd-kms-key-location',
'kms-project': '--gce-pd-kms-key-project'
}
kms_resource_args.AddKmsKeyResourceArg(
parser, 'cluster', flag_overrides=kms_flag_overrides)
def _CommonArgs(parser, beta=False):
"""Register flags common to all tracks."""
base.ASYNC_FLAG.AddToParser(parser)
parser.add_argument('name', help='The name of this cluster.')
clusters.ArgsForClusterRef(parser, beta)
# Add gce-pd-kms-key args
kms_flag_overrides = {'kms-key': '--gce-pd-kms-key',
'kms-keyring': '--gce-pd-kms-key-keyring',
'kms-location': '--gce-pd-kms-key-location',
'kms-project': '--gce-pd-kms-key-project'}
kms_resource_args.AddKmsKeyResourceArg(
parser, 'cluster', flag_overrides=kms_flag_overrides)
def Args(cls, parser):
dataproc = dp.Dataproc(cls.ReleaseTrack())
parser.add_argument('--cluster-name',
help="""\
The name of the managed dataproc cluster.
If unspecified, the workflow template ID will be used.""")
clusters.ArgsForClusterRef(parser,
cls.Beta(),
include_deprecated=cls.Beta())
flags.AddTemplateResourceArg(parser, 'set managed cluster',
dataproc.api_version)
if cls.Beta():
clusters.BetaArgsForClusterRef(parser)
def Args(cls, parser):
dataproc = dp.Dataproc(cls.ReleaseTrack())
base.ASYNC_FLAG.AddToParser(parser)
flags.AddClusterResourceArg(parser, 'create', dataproc.api_version)
clusters.ArgsForClusterRef(parser, cls.BETA, include_ttl_config=True)
# Add gce-pd-kms-key args
kms_flag_overrides = {
'kms-key': '--gce-pd-kms-key',
'kms-keyring': '--gce-pd-kms-key-keyring',
'kms-location': '--gce-pd-kms-key-location',
'kms-project': '--gce-pd-kms-key-project'
}
kms_resource_args.AddKmsKeyResourceArg(
parser, 'cluster', flag_overrides=kms_flag_overrides)
def _CommonArgs(parser, beta=False):
"""Register flags common to all tracks."""
base.ASYNC_FLAG.AddToParser(parser)
parser.add_argument('name', help='The name of this cluster.')
clusters.ArgsForClusterRef(parser, beta)
def Args(parser):
flags.AddTemplateResourceArg(parser, 'set managed cluster')
parser.add_argument('--cluster-name',
help="""\
The name of the managed dataproc cluster.
If unspecified, the workflow template ID will be used.""")
clusters.ArgsForClusterRef(parser, beta=True)
flags.AddMinCpuPlatformArgs(parser, base.ReleaseTrack.BETA)
# TODO(b/70164645): Consolidate these arguments with the other beta args
# All arguments for these arguments are duplicated from the cluster creation
# beta track. There should be a ArgsForClusterRefBeta method in clusters.py
# that is invoked here so that we don't have to duplicate the arguments.
parser.add_argument('--max-idle',
type=arg_parsers.Duration(),
help="""\
The duration before cluster is auto-deleted after last job completes,
such as "2h" or "1d".
See $ gcloud topic datetimes for information on duration formats.
""")
auto_delete_group = parser.add_mutually_exclusive_group()
auto_delete_group.add_argument('--max-age',
type=arg_parsers.Duration(),
help="""\
The lifespan of the cluster before it is auto-deleted, such as
"2h" or "1d".
See $ gcloud topic datetimes for information on duration formats.
""")
auto_delete_group.add_argument('--expiration-time',
type=arg_parsers.Datetime.Parse,
help="""\
The time when cluster will be auto-deleted, such as
"2017-08-29T18:52:51.142Z." See $ gcloud topic datetimes for
information on time formats.
""")
for instance_type in ('master', 'worker'):
help_msg = """\
Attaches accelerators (e.g. GPUs) to the {instance_type}
instance(s).
""".format(instance_type=instance_type)
if instance_type == 'worker':
help_msg += """
Note:
No accelerators will be attached to preemptible workers, because
preemptible VMs do not support accelerators.
"""
help_msg += """
*type*::: The specific type (e.g. nvidia-tesla-k80 for nVidia Tesla
K80) of accelerator to attach to the instances. Use 'gcloud compute
accelerator-types list' to learn about all available accelerator
types.
*count*::: The number of pieces of the accelerator to attach to each
of the instances. The default value is 1.
"""
parser.add_argument('--{0}-accelerator'.format(instance_type),
type=arg_parsers.ArgDict(spec={
'type': str,
'count': int,
}),
metavar='type=TYPE,[count=COUNT]',
help=help_msg)
def Args(parser):
flags.AddTemplateFlag(parser, 'set managed cluster')
parser.add_argument('--cluster-name',
help='The name of the managed dataproc cluster.')
clusters.ArgsForClusterRef(parser)
flags.AddZoneFlag(parser)
parser.add_argument('--num-masters',
type=int,
help="""\
The number of master nodes in the cluster.
[format="csv",options="header"]
|========
Number of Masters,Cluster Mode
1,Standard
3,High Availability
|========
""")
parser.add_argument('--single-node',
action='store_true',
help="""\
Create a single node cluster.
A single node cluster has all master and worker components.
It cannot have any separate worker nodes.
""")
parser.add_argument('--max-idle',
type=arg_parsers.Duration(),
help="""\
The duration before cluster is auto-deleted after last job completes,
such as "30m", "2h" or "1d".
""")
auto_delete_group = parser.add_mutually_exclusive_group()
auto_delete_group.add_argument('--max-age',
type=arg_parsers.Duration(),
help="""\
The lifespan of the cluster before it is auto-deleted, such as "30m",
"2h" or "1d".
""")
auto_delete_group.add_argument('--expiration-time',
type=arg_parsers.Datetime.Parse,
help="""\
The time when cluster will be auto-deleted, such as
"2017-08-29T18:52:51.142Z"
""")
for instance_type in ('master', 'worker'):
help_msg = """\
Attaches accelerators (e.g. GPUs) to the {instance_type}
instance(s).
""".format(instance_type=instance_type)
if instance_type == 'worker':
help_msg += """
Note:
No accelerators will be attached to preemptible workers, because
preemptible VMs do not support accelerators.
"""
help_msg += """
*type*::: The specific type (e.g. nvidia-tesla-k80 for nVidia Tesla
K80) of accelerator to attach to the instances. Use 'gcloud compute
accelerator-types list' to learn about all available accelerator
types.
*count*::: The number of pieces of the accelerator to attach to each
of the instances. The default value is 1.
"""
parser.add_argument('--{0}-accelerator'.format(instance_type),
type=arg_parsers.ArgDict(spec={
'type': str,
'count': int,
}),
metavar='type=TYPE,[count=COUNT]',
help=help_msg)
parser.add_argument('--no-address',
action='store_true',
help="""\
If provided, the instances in the cluster will not be assigned external
IP addresses.
Note: Dataproc VMs need access to the Dataproc API. This can be achieved
without external IP addresses using Private Google Access
(https://cloud.google.com/compute/docs/private-google-access).
""")
def _CommonArgs(parser, beta, include_deprecated):
parser.add_argument('--cluster-name',
help="""\
The name of the managed dataproc cluster.
If unspecified, the workflow template ID will be used.""")
clusters.ArgsForClusterRef(parser, beta, include_deprecated)
