"""Argparser module for WorkerRuntime"""
from jina import __default_host__, helper
from jina.parsers.helper import KVAppendAction, add_arg_group
[docs]def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:param parser: the parser instance to which we add arguments
"""
gp = add_arg_group(parser, title='WorkerRuntime')
from jina import __default_executor__
gp.add_argument(
'--uses',
type=str,
default=__default_executor__,
help='''
The config of the executor, it could be one of the followings:
* the string literal of an Executor class name
* an Executor YAML file (.yml, .yaml, .jaml)
* a Jina Hub Executor (must start with `jinahub://` or `jinahub+docker://`)
* a docker image (must start with `docker://`)
* the string literal of a YAML config (must start with `!` or `jtype: `)
* the string literal of a JSON config
When use it under Python, one can use the following values additionally:
- a Python dict that represents the config
- a text file stream has `.read()` interface
''',
)
gp.add_argument(
'--uses-with',
action=KVAppendAction,
metavar='KEY: VALUE',
nargs='*',
help='''
Dictionary of keyword arguments that will override the `with` configuration in `uses`
''',
)
gp.add_argument(
'--uses-metas',
action=KVAppendAction,
metavar='KEY: VALUE',
nargs='*',
help='''
Dictionary of keyword arguments that will override the `metas` configuration in `uses`
''',
)
gp.add_argument(
'--uses-requests',
action=KVAppendAction,
metavar='KEY: VALUE',
nargs='*',
help='''
Dictionary of keyword arguments that will override the `requests` configuration in `uses`
''',
)
gp.add_argument(
'--py-modules',
type=str,
nargs='*',
metavar='PATH',
help='''
The customized python modules need to be imported before loading the executor
Note that the recommended way is to only import a single module - a simple python file, if your
executor can be defined in a single file, or an ``__init__.py`` file if you have multiple files,
which should be structured as a python package. For more details, please see the
`Executor cookbook <https://docs.jina.ai/fundamentals/executor/executor-files/>`__
''',
)
gp.add_argument(
'--port-in',
type=int,
default=helper.random_port(),
dest='port',
help='The port for input data to bind to, default a random port between [49152, 65535]',
)
gp.add_argument(
'--host-in',
type=str,
default=__default_host__,
help=f'The host address for binding to, by default it is {__default_host__}',
)
gp.add_argument(
'--native',
action='store_true',
default=False,
help='If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.',
)
gp.add_argument(
'--output-array-type',
type=str,
default=None,
help='''
The type of array `tensor` and `embedding` will be serialized to.
Supports the same types as `docarray.to_protobuf(.., ndarray_type=...)`, which can be found
`here <https://docarray.jina.ai/fundamentals/document/serialization/#from-to-protobuf>`.
Defaults to retaining whatever type is returned by the Executor.
''',
)
gp.add_argument(
'--grpc-server-options',
action=KVAppendAction,
metavar='KEY: VALUE',
nargs='*',
help="Dictionary of kwargs arguments that will be passed to the grpc server as options when starting the server, example : {'grpc.max_send_message_length': -1}",
default=None,
)
