Name Description Type Default
name The name of this object.

This will be used in the following places:
- how you refer to this object in Python/YAML/CLI
- visualization
- log message header
- ...

When not given, then the default naming strategy will apply.
string None
workspace The working directory for any IO operations in this object. If not set, then derive from its parent workspace. string None
log_config The YAML config of the logger used in this object. string default
quiet If set, then no log will be emitted from this object. boolean False
quiet_error If set, then exception stack information will not be added to the log boolean False
optout_telemetry If set, disables telemetry during the Flow/Pod/Runtime start. boolean False
timeout_ctrl The timeout in milliseconds of the control request, -1 for waiting forever number 60
polling The polling strategy of the Deployment and its endpoints (when shards>1).
Can be defined for all endpoints of a Deployment or by endpoint.
Define per Deployment:
- ANY: only one (whoever is idle) Pod polls the message
- ALL: all Pods poll the message (like a broadcast)
Define per Endpoint:
JSON dict, {endpoint: PollingType}
{'/custom': 'ALL', '/search': 'ANY', '*': 'ANY'}
string ANY
uses The config of the executor, it could be one of the followings:
* 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
string BaseExecutor
uses_with Dictionary of keyword arguments that will override the with configuration in uses object None
uses_metas Dictionary of keyword arguments that will override the metas configuration in uses object None
uses_requests Dictionary of keyword arguments that will override the requests configuration in uses object None
py_modules 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/>__
array None
port The port for input data to bind to, default is a random port between [49152, 65535] number random in [49152, 65535]
host_in The host address for binding to, by default it is 0.0.0.0 string 0.0.0.0
native If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime. boolean False
output_array_type 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.
string None
entrypoint The entrypoint command overrides the ENTRYPOINT in Docker image. when not set then the Docker image ENTRYPOINT takes effective. string None
docker_kwargs Dictionary of kwargs arguments that will be passed to Docker SDK when starting the docker '
container.

More details can be found in the Docker SDK docs: https://docker-py.readthedocs.io/en/stable/
object None
volumes The path on the host to be mounted inside the container.

Note,
- If separated by :, then the first part will be considered as the local host path and the second part is the path in the container system.
- If no split provided, then the basename of that directory will be mounted into container's root path, e.g. --volumes="/user/test/my-workspace" will be mounted into /my-workspace inside the container.
- All volumes are mounted with read-write mode.
array None
gpus This argument allows dockerized Jina executor discover local gpu devices.

Note,
- To access all gpus, use --gpus all.
- To access multiple gpus, e.g. make use of 2 gpus, use --gpus 2.
- To access specified gpus based on device id, use --gpus device=[YOUR-GPU-DEVICE-ID]
- To access specified gpus based on multiple device id, use --gpus device=[YOUR-GPU-DEVICE-ID1],device=[YOUR-GPU-DEVICE-ID2]
- To specify more parameters, use --gpus device=[YOUR-GPU-DEVICE-ID],runtime=nvidia,capabilities=display |string|None`
disable_auto_volume Do not automatically mount a volume for dockerized Executors. boolean False
host The host address of the runtime, by default it is 0.0.0.0. string 0.0.0.0
quiet_remote_logs Do not display the streaming of remote logs on local console boolean False
upload_files The files on the host to be uploaded to the remote
workspace. This can be useful when your Deployment has more
file dependencies beyond a single YAML file, e.g.
Python files, data files.

Note,
- currently only flatten structure is supported, which means if you upload [./foo/a.py, ./foo/b.pp, ./bar/c.yml], then they will be put under the same workspace on the remote, losing all hierarchies.
- by default, --uses YAML file is always uploaded.
- uploaded files are by default isolated across the runs. To ensure files are submitted to the same workspace across different runs, use --workspace-id to specify the workspace.
array None
runtime_cls The runtime class to run inside the Pod string WorkerRuntime
timeout_ready The timeout in milliseconds of a Pod waits for the runtime to be ready, -1 for waiting forever number 600000
env The map of environment variables that are available inside runtime object None
shards The number of shards in the deployment running at the same time. For more details check https://docs.jina.ai/fundamentals/flow/create-flow/#complex-flow-topologies number 1
replicas The number of replicas in the deployment number 1
monitoring If set, spawn an http server with a prometheus endpoint to expose metrics boolean False
port_monitoring The port on which the prometheus server is exposed, default is a random port between [49152, 65535] number random in [49152, 65535]
retries Number of retries per gRPC call. If <0 it defaults to max(3, num_replicas) number -1
floating If set, the current Pod/Deployment can not be further chained, and the next .add() will chain after the last Pod/Deployment not this current one. boolean False
install_requirements If set, install requirements.txt in the Hub Executor bundle to local boolean False
force_update If set, always pull the latest Hub Executor bundle even it exists on local boolean False
compression The compression mechanism used when sending requests from the Head to the WorkerRuntimes. For more details, check https://grpc.github.io/grpc/python/grpc.html#compression. string None
uses_before_address The address of the uses-before runtime string None
uses_after_address The address of the uses-before runtime string None
connection_list dictionary JSON with a list of connections to configure string None
disable_reduce Disable the built-in reduce mechanism, set this if the reduction is to be handled by the Executor connected to this Head boolean False
timeout_send The timeout in milliseconds used when sending data requests to Executors, -1 means no timeout, disabled by default number None