import argparse
import base64
import copy
import inspect
import json
import multiprocessing
import os
import sys
import threading
import time
import uuid
import warnings
from collections import OrderedDict
from contextlib import ExitStack
from typing import (
TYPE_CHECKING,
Dict,
List,
Optional,
Set,
Tuple,
Type,
Union,
overload,
)
from rich import print
from rich.panel import Panel
from rich.progress import (
BarColumn,
MofNCompleteColumn,
Progress,
SpinnerColumn,
TextColumn,
TimeElapsedColumn,
)
from rich.table import Table
from jina import __default_host__, __docker_host__, helper
from jina.clients import Client
from jina.clients.mixin import AsyncPostMixin, HealthCheckMixin, PostMixin, ProfileMixin
from jina.enums import (
DeploymentRoleType,
FlowBuildLevel,
FlowInspectType,
GatewayProtocolType,
)
from jina.excepts import (
FlowMissingDeploymentError,
FlowTopologyError,
PortAlreadyUsed,
RuntimeFailToStart,
)
from jina.helper import (
ArgNamespace,
CatchAllCleanupContextManager,
download_mermaid_url,
get_internal_ip,
get_public_ip,
is_port_free,
send_telemetry_event,
typename,
)
from jina.jaml import JAMLCompatible
from jina.logging.logger import JinaLogger
from jina.orchestrate.deployments import Deployment
from jina.orchestrate.flow.builder import _hanging_deployments, allowed_levels
from jina.parsers import (
set_client_cli_parser,
set_deployment_parser,
set_gateway_parser,
)
from jina.parsers.flow import set_flow_parser
from jina.serve.networking import host_is_local, in_docker
__all__ = ['Flow']
class FlowType(type(ExitStack), type(JAMLCompatible)):
"""Type of Flow, metaclass of :class:`BaseFlow`"""
pass
_regex_port = r'(.*?):([0-9]{1,4}|[1-5][0-9]{4}|6[0-4][0-9]{3}|65[0-4][0-9]{2}|655[0-2][0-9]|6553[0-5])$'
if TYPE_CHECKING:
from jina.clients.base import BaseClient
from jina.orchestrate.flow.asyncio import AsyncFlow
from jina.serve.executors import BaseExecutor
GATEWAY_NAME = 'gateway'
FALLBACK_PARSERS = [
set_gateway_parser(),
set_deployment_parser(),
set_client_cli_parser(),
set_flow_parser(),
]
[docs]class Flow(
PostMixin,
ProfileMixin,
HealthCheckMixin,
JAMLCompatible,
ExitStack,
metaclass=FlowType,
):
"""Flow is how Jina streamlines and distributes Executors."""
# overload_inject_start_client_flow
@overload
def __init__(
self,
*,
asyncio: Optional[bool] = False,
host: Optional[str] = '0.0.0.0',
port: Optional[int] = None,
protocol: Optional[str] = 'GRPC',
proxy: Optional[bool] = False,
tls: Optional[bool] = False,
**kwargs,
):
"""Create a Flow. Flow is how Jina streamlines and scales Executors. This overloaded method provides arguments from `jina client` CLI.
:param asyncio: If set, then the input and output of this Client work in an asynchronous manner.
:param host: The host address of the runtime, by default it is 0.0.0.0.
:param port: The port of the Gateway, which the client should connect to.
:param protocol: Communication protocol between server and client.
:param proxy: If set, respect the http_proxy and https_proxy environment variables. otherwise, it will unset these proxy variables before start. gRPC seems to prefer no proxy
:param tls: If set, connect to gateway using tls encryption
.. # noqa: DAR202
.. # noqa: DAR101
.. # noqa: DAR003
"""
# overload_inject_end_client_flow
# overload_inject_start_gateway_flow
@overload
def __init__(
self,
*,
compression: Optional[str] = None,
cors: Optional[bool] = False,
deployments_addresses: Optional[str] = '{}',
deployments_disable_reduce: Optional[str] = '[]',
description: Optional[str] = None,
env: Optional[dict] = None,
expose_endpoints: Optional[str] = None,
expose_graphql_endpoint: Optional[bool] = False,
floating: Optional[bool] = False,
graph_conditions: Optional[str] = '{}',
graph_description: Optional[str] = '{}',
grpc_server_options: Optional[dict] = None,
host: Optional[str] = '0.0.0.0',
host_in: Optional[str] = '0.0.0.0',
log_config: Optional[str] = None,
monitoring: Optional[bool] = False,
name: Optional[str] = 'gateway',
native: Optional[bool] = False,
no_crud_endpoints: Optional[bool] = False,
no_debug_endpoints: Optional[bool] = False,
output_array_type: Optional[str] = None,
polling: Optional[str] = 'ANY',
port: Optional[int] = None,
port_monitoring: Optional[str] = None,
prefetch: Optional[int] = 1000,
protocol: Optional[str] = 'GRPC',
proxy: Optional[bool] = False,
py_modules: Optional[List[str]] = None,
quiet: Optional[bool] = False,
quiet_error: Optional[bool] = False,
replicas: Optional[int] = 1,
retries: Optional[int] = -1,
runtime_cls: Optional[str] = 'GRPCGatewayRuntime',
shards: Optional[int] = 1,
ssl_certfile: Optional[str] = None,
ssl_keyfile: Optional[str] = None,
timeout_ctrl: Optional[int] = 60,
timeout_ready: Optional[int] = 600000,
timeout_send: Optional[int] = None,
title: Optional[str] = None,
uses: Optional[Union[str, Type['BaseExecutor'], dict]] = 'BaseExecutor',
uses_metas: Optional[dict] = None,
uses_requests: Optional[dict] = None,
uses_with: Optional[dict] = None,
uvicorn_kwargs: Optional[dict] = None,
workspace: Optional[str] = None,
**kwargs,
):
"""Create a Flow. Flow is how Jina streamlines and scales Executors. This overloaded method provides arguments from `jina gateway` CLI.
:param 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.
:param cors: If set, a CORS middleware is added to FastAPI frontend to allow cross-origin access.
:param deployments_addresses: dictionary JSON with the input addresses of each Deployment
:param deployments_disable_reduce: list JSON disabling the built-in merging mechanism for each Deployment listed
:param description: The description of this HTTP server. It will be used in automatics docs such as Swagger UI.
:param env: The map of environment variables that are available inside runtime
:param expose_endpoints: A JSON string that represents a map from executor endpoints (`@requests(on=...)`) to HTTP endpoints.
:param expose_graphql_endpoint: If set, /graphql endpoint is added to HTTP interface.
:param 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.
:param graph_conditions: Dictionary stating which filtering conditions each Executor in the graph requires to receive Documents.
:param graph_description: Routing graph for the gateway
:param grpc_server_options: 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}
:param host: The host address of the runtime, by default it is 0.0.0.0.
:param host_in: The host address for binding to, by default it is 0.0.0.0
:param log_config: The YAML config of the logger used in this object.
:param monitoring: If set, spawn an http server with a prometheus endpoint to expose metrics
:param 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.
:param native: If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.
:param no_crud_endpoints: If set, `/index`, `/search`, `/update`, `/delete` endpoints are removed from HTTP interface.
Any executor that has `@requests(on=...)` bind with those values will receive data requests.
:param no_debug_endpoints: If set, `/status` `/post` endpoints are removed from HTTP interface.
:param 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.
:param 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'}
:param port: The port for input data to bind to, default is a random port between [49152, 65535]
:param port_monitoring: The port on which the prometheus server is exposed, default is a random port between [49152, 65535]
:param prefetch: Number of requests fetched from the client before feeding into the first Executor.
Used to control the speed of data input into a Flow. 0 disables prefetch (1000 requests is the default)
:param protocol: Communication protocol between server and client.
:param proxy: If set, respect the http_proxy and https_proxy environment variables. otherwise, it will unset these proxy variables before start. gRPC seems to prefer no proxy
:param 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/>`__
:param quiet: If set, then no log will be emitted from this object.
:param quiet_error: If set, then exception stack information will not be added to the log
:param replicas: The number of replicas in the deployment
:param retries: Number of retries per gRPC call. If <0 it defaults to max(3, num_replicas)
:param runtime_cls: The runtime class to run inside the Pod
:param 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
:param ssl_certfile: the path to the certificate file
:param ssl_keyfile: the path to the key file
:param timeout_ctrl: The timeout in milliseconds of the control request, -1 for waiting forever
:param timeout_ready: The timeout in milliseconds of a Pod waits for the runtime to be ready, -1 for waiting forever
:param timeout_send: The timeout in milliseconds used when sending data requests to Executors, -1 means no timeout, disabled by default
:param title: The title of this HTTP server. It will be used in automatics docs such as Swagger UI.
:param uses: 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
:param uses_metas: Dictionary of keyword arguments that will override the `metas` configuration in `uses`
:param uses_requests: Dictionary of keyword arguments that will override the `requests` configuration in `uses`
:param uses_with: Dictionary of keyword arguments that will override the `with` configuration in `uses`
:param uvicorn_kwargs: Dictionary of kwargs arguments that will be passed to Uvicorn server when starting the server
More details can be found in Uvicorn docs: https://www.uvicorn.org/settings/
:param workspace: The working directory for any IO operations in this object. If not set, then derive from its parent `workspace`.
.. # noqa: DAR202
.. # noqa: DAR101
.. # noqa: DAR003
"""
# overload_inject_end_gateway_flow
# overload_inject_start_flow
@overload
def __init__(
self,
*,
env: Optional[dict] = None,
inspect: Optional[str] = 'COLLECT',
log_config: Optional[str] = None,
name: Optional[str] = None,
quiet: Optional[bool] = False,
quiet_error: Optional[bool] = False,
uses: Optional[str] = None,
workspace: Optional[str] = None,
**kwargs,
):
"""Create a Flow. Flow is how Jina streamlines and scales Executors. This overloaded method provides arguments from `jina flow` CLI.
:param env: The map of environment variables that are available inside runtime
:param inspect: The strategy on those inspect deployments in the flow.
If `REMOVE` is given then all inspect deployments are removed when building the flow.
:param log_config: The YAML config of the logger used in this object.
:param 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.
:param quiet: If set, then no log will be emitted from this object.
:param quiet_error: If set, then exception stack information will not be added to the log
:param uses: The YAML path represents a flow. It can be either a local file path or a URL.
:param workspace: The working directory for any IO operations in this object. If not set, then derive from its parent `workspace`.
.. # noqa: DAR202
.. # noqa: DAR101
.. # noqa: DAR003
"""
# overload_inject_end_flow
def __init__(
self,
args: Optional['argparse.Namespace'] = None,
**kwargs,
):
# implementation_stub_inject_start_flow
"""Create a Flow. Flow is how Jina streamlines and scales Executors.
EXAMPLE USAGE
Python API
.. code-block:: python
from jina import Flow
f = Flow().add(uses='jinahub+docker://SimpleIndexer') # create Flow and add Executor
with f:
f.bock() # serve Flow
To and from YAML configuration
.. code-block:: python
from jina import Flow
f = Flow().add(uses='jinahub+docker://SimpleIndexer') # create Flow and add Executor
f.save_config('flow.yml') # save YAML config file
f = Flow.load_config('flow.yml') # load Flow from YAML config
with f:
f.bock() # serve Flow
:param asyncio: If set, then the input and output of this Client work in an asynchronous manner.
:param host: The host address of the runtime, by default it is 0.0.0.0.
:param port: The port of the Gateway, which the client should connect to.
:param protocol: Communication protocol between server and client.
:param proxy: If set, respect the http_proxy and https_proxy environment variables. otherwise, it will unset these proxy variables before start. gRPC seems to prefer no proxy
:param tls: If set, connect to gateway using tls encryption
:param 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.
:param cors: If set, a CORS middleware is added to FastAPI frontend to allow cross-origin access.
:param deployments_addresses: dictionary JSON with the input addresses of each Deployment
:param deployments_disable_reduce: list JSON disabling the built-in merging mechanism for each Deployment listed
:param description: The description of this HTTP server. It will be used in automatics docs such as Swagger UI.
:param env: The map of environment variables that are available inside runtime
:param expose_endpoints: A JSON string that represents a map from executor endpoints (`@requests(on=...)`) to HTTP endpoints.
:param expose_graphql_endpoint: If set, /graphql endpoint is added to HTTP interface.
:param 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.
:param graph_conditions: Dictionary stating which filtering conditions each Executor in the graph requires to receive Documents.
:param graph_description: Routing graph for the gateway
:param grpc_server_options: 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}
:param host: The host address of the runtime, by default it is 0.0.0.0.
:param host_in: The host address for binding to, by default it is 0.0.0.0
:param log_config: The YAML config of the logger used in this object.
:param monitoring: If set, spawn an http server with a prometheus endpoint to expose metrics
:param 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.
:param native: If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.
:param no_crud_endpoints: If set, `/index`, `/search`, `/update`, `/delete` endpoints are removed from HTTP interface.
Any executor that has `@requests(on=...)` bind with those values will receive data requests.
:param no_debug_endpoints: If set, `/status` `/post` endpoints are removed from HTTP interface.
:param 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.
:param 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'}
:param port: The port for input data to bind to, default is a random port between [49152, 65535]
:param port_monitoring: The port on which the prometheus server is exposed, default is a random port between [49152, 65535]
:param prefetch: Number of requests fetched from the client before feeding into the first Executor.
Used to control the speed of data input into a Flow. 0 disables prefetch (1000 requests is the default)
:param protocol: Communication protocol between server and client.
:param proxy: If set, respect the http_proxy and https_proxy environment variables. otherwise, it will unset these proxy variables before start. gRPC seems to prefer no proxy
:param 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/>`__
:param quiet: If set, then no log will be emitted from this object.
:param quiet_error: If set, then exception stack information will not be added to the log
:param replicas: The number of replicas in the deployment
:param retries: Number of retries per gRPC call. If <0 it defaults to max(3, num_replicas)
:param runtime_cls: The runtime class to run inside the Pod
:param 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
:param ssl_certfile: the path to the certificate file
:param ssl_keyfile: the path to the key file
:param timeout_ctrl: The timeout in milliseconds of the control request, -1 for waiting forever
:param timeout_ready: The timeout in milliseconds of a Pod waits for the runtime to be ready, -1 for waiting forever
:param timeout_send: The timeout in milliseconds used when sending data requests to Executors, -1 means no timeout, disabled by default
:param title: The title of this HTTP server. It will be used in automatics docs such as Swagger UI.
:param uses: 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
:param uses_metas: Dictionary of keyword arguments that will override the `metas` configuration in `uses`
:param uses_requests: Dictionary of keyword arguments that will override the `requests` configuration in `uses`
:param uses_with: Dictionary of keyword arguments that will override the `with` configuration in `uses`
:param uvicorn_kwargs: Dictionary of kwargs arguments that will be passed to Uvicorn server when starting the server
More details can be found in Uvicorn docs: https://www.uvicorn.org/settings/
:param workspace: The working directory for any IO operations in this object. If not set, then derive from its parent `workspace`.
:param env: The map of environment variables that are available inside runtime
:param inspect: The strategy on those inspect deployments in the flow.
If `REMOVE` is given then all inspect deployments are removed when building the flow.
:param log_config: The YAML config of the logger used in this object.
:param 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.
:param quiet: If set, then no log will be emitted from this object.
:param quiet_error: If set, then exception stack information will not be added to the log
:param uses: The YAML path represents a flow. It can be either a local file path or a URL.
:param workspace: The working directory for any IO operations in this object. If not set, then derive from its parent `workspace`.
.. # noqa: DAR102
.. # noqa: DAR202
.. # noqa: DAR101
.. # noqa: DAR003
"""
# implementation_stub_inject_end_flow
super().__init__()
self._version = '1' #: YAML version number, this will be later overridden if YAML config says the other way
self._deployment_nodes = OrderedDict() # type: Dict[str, Deployment]
self._inspect_deployments = {} # type: Dict[str, str]
self._endpoints_mapping = {} # type: Dict[str, Dict]
self._build_level = FlowBuildLevel.EMPTY
self._last_changed_deployment = [
GATEWAY_NAME
] #: default first deployment is gateway, will add when build()
self._update_args(args, **kwargs)
if isinstance(self.args, argparse.Namespace):
self.logger = JinaLogger(
self.__class__.__name__, **vars(self.args), **self._common_kwargs
)
else:
self.logger = JinaLogger(self.__class__.__name__, **self._common_kwargs)
def _update_args(self, args, **kwargs):
from jina.helper import ArgNamespace
from jina.parsers.flow import set_flow_parser
_flow_parser = set_flow_parser()
if args is None:
args = ArgNamespace.kwargs2namespace(
kwargs, _flow_parser, True, fallback_parsers=FALLBACK_PARSERS
)
self.args = args
# common args should be the ones that can not be parsed by _flow_parser
known_keys = vars(args)
self._common_kwargs = {k: v for k, v in kwargs.items() if k not in known_keys}
self._kwargs = ArgNamespace.get_non_defaults_args(
args, _flow_parser
) #: for yaml dump
if self._common_kwargs.get('asyncio', False) and not isinstance(
self, AsyncPostMixin
):
from jina.orchestrate.flow.asyncio import AsyncFlow
self.__class__ = AsyncFlow
@staticmethod
def _parse_endpoints(
op_flow, deployment_name, endpoint, connect_to_last_deployment=False
) -> Set:
# parsing needs
if isinstance(endpoint, str):
endpoint = [endpoint]
elif not endpoint:
if op_flow._last_changed_deployment and connect_to_last_deployment:
endpoint = [op_flow._last_deployment]
else:
endpoint = []
if isinstance(endpoint, (list, tuple)):
for idx, s in enumerate(endpoint):
if s == deployment_name:
raise FlowTopologyError(
'the income/output of a deployment can not be itself'
)
else:
raise ValueError(f'endpoint={endpoint} is not parsable')
# if an endpoint is being inspected, then replace it with inspected Deployment
endpoint = set(op_flow._inspect_deployments.get(ep, ep) for ep in endpoint)
return endpoint
@property
def _last_deployment(self):
"""Last deployment
.. # noqa: DAR401
.. # noqa: DAR201
"""
return self._last_changed_deployment[-1]
@_last_deployment.setter
def _last_deployment(self, name: str):
"""
Set a Deployment as the last Deployment in the Flow, useful when modifying the Flow.
.. # noqa: DAR401
:param name: the name of the existing Deployment
"""
if name not in self._deployment_nodes:
raise FlowMissingDeploymentError(f'{name} can not be found in this Flow')
if self._last_changed_deployment and name == self._last_deployment:
pass
else:
self._last_changed_deployment.append(name)
# graph is now changed so we need to
# reset the build level to the lowest
self._build_level = FlowBuildLevel.EMPTY
@allowed_levels([FlowBuildLevel.EMPTY])
def _add_gateway(
self,
needs: str,
graph_description: Dict[str, List[str]],
deployments_addresses: Dict[str, List[str]],
graph_conditions: Dict[str, Dict],
deployments_disabled_reduce: List[str],
**kwargs,
):
kwargs.update(
dict(
name=GATEWAY_NAME,
ctrl_with_ipc=True, # otherwise ctrl port would be conflicted
host=self.host,
protocol=self.protocol,
port=self.port,
deployment_role=DeploymentRoleType.GATEWAY,
expose_endpoints=json.dumps(self._endpoints_mapping),
env=self.env,
)
)
kwargs.update(self._common_kwargs)
args = ArgNamespace.kwargs2namespace(kwargs, set_gateway_parser())
# We need to check later if the port was manually set or randomly
args.default_port = (
kwargs.get('port', None) is None and kwargs.get('port_expose', None) is None
)
args.noblock_on_start = True
args.graph_description = json.dumps(graph_description)
args.graph_conditions = json.dumps(graph_conditions)
args.deployments_addresses = json.dumps(deployments_addresses)
args.deployments_disable_reduce = json.dumps(deployments_disabled_reduce)
self._deployment_nodes[GATEWAY_NAME] = Deployment(args, needs)
def _get_deployments_addresses(self) -> Dict[str, List[str]]:
graph_dict = {}
for node, v in self._deployment_nodes.items():
if node == 'gateway':
continue
if v.head_args:
# add head information
graph_dict[node] = [f'{v.protocol}://{v.host}:{v.head_port}']
else:
# there is no head, add the worker connection information instead
host = v.host
if host_is_local(host) and in_docker() and v.dockerized_uses:
host = __docker_host__
graph_dict[node] = [f'{v.protocol}://{host}:{port}' for port in v.ports]
return graph_dict
def _get_k8s_deployments_addresses(
self, k8s_namespace: str
) -> Dict[str, List[str]]:
graph_dict = {}
from jina.orchestrate.deployments.config.helper import to_compatible_name
from jina.serve.networking import GrpcConnectionPool
for node, v in self._deployment_nodes.items():
if node == 'gateway':
continue
if v.external:
deployment_k8s_address = f'{v.host}'
elif v.head_args:
deployment_k8s_address = (
f'{to_compatible_name(v.head_args.name)}.{k8s_namespace}.svc'
)
else:
deployment_k8s_address = (
f'{to_compatible_name(v.name)}.{k8s_namespace}.svc'
)
external_port = v.head_port if v.head_port else v.port
graph_dict[node] = [
f'{v.protocol}://{deployment_k8s_address}:{external_port if v.external else GrpcConnectionPool.K8S_PORT}'
]
return graph_dict if graph_dict else None
def _get_docker_compose_deployments_addresses(self) -> Dict[str, List[str]]:
graph_dict = {}
from jina.orchestrate.deployments.config.docker_compose import port
from jina.orchestrate.deployments.config.helper import to_compatible_name
for node, v in self._deployment_nodes.items():
if node == 'gateway':
continue
if v.external:
deployment_docker_compose_address = [
f'{v.protocol}://{v.host}:{v.port}'
]
elif v.head_args:
deployment_docker_compose_address = [
f'{to_compatible_name(v.head_args.name)}:{port}'
]
else:
if v.args.replicas == 1:
deployment_docker_compose_address = [
f'{to_compatible_name(v.name)}:{port}'
]
else:
deployment_docker_compose_address = []
for rep_id in range(v.args.replicas):
node_name = f'{v.name}/rep-{rep_id}'
deployment_docker_compose_address.append(
f'{to_compatible_name(node_name)}:{port}'
)
graph_dict[node] = deployment_docker_compose_address
return graph_dict
def _get_graph_conditions(self) -> Dict[str, Dict]:
graph_condition = {}
for node, v in self._deployment_nodes.items():
if v.args.when is not None: # condition on input docs
graph_condition[node] = v.args.when
return graph_condition
def _get_disabled_reduce_deployments(self) -> List[str]:
disabled_deployments = []
for node, v in self._deployment_nodes.items():
if v.args.disable_reduce:
disabled_deployments.append(node)
return disabled_deployments
def _get_graph_representation(self) -> Dict[str, List[str]]:
def _add_node(graph, n):
# in the graph we need to distinguish between start and end gateway, although they are the same deployment
if n == 'gateway':
n = 'start-gateway'
if n not in graph:
graph[n] = []
return n
graph_dict = {}
for node, v in self._deployment_nodes.items():
node = _add_node(graph_dict, node)
if node == 'start-gateway':
continue
for need in sorted(v.needs):
need = _add_node(graph_dict, need)
graph_dict[need].append(node)
# find all non floating leafs
last_deployment = self._last_deployment
if last_deployment != 'gateway':
graph_dict[last_deployment].append('end-gateway')
return graph_dict
[docs] @allowed_levels([FlowBuildLevel.EMPTY])
def needs(
self, needs: Union[Tuple[str], List[str]], name: str = 'joiner', *args, **kwargs
) -> 'Flow':
"""
Add a blocker to the Flow, wait until all pods defined in **needs** completed.
.. # noqa: DAR401
:param needs: list of service names to wait
:param name: the name of this joiner, by default is ``joiner``
:param args: additional positional arguments forwarded to the add function
:param kwargs: additional key value arguments forwarded to the add function
:return: the modified Flow
"""
if len(needs) <= 1:
raise FlowTopologyError(
'no need to wait for a single service, need len(needs) > 1'
)
return self.add(
name=name,
needs=needs,
deployment_role=DeploymentRoleType.JOIN,
*args,
**kwargs,
)
[docs] @allowed_levels([FlowBuildLevel.EMPTY])
def needs_all(self, name: str = 'joiner', *args, **kwargs) -> 'Flow':
"""
Collect all floating Deployments so far and add a blocker to the Flow; wait until all handing pods completed.
:param name: the name of this joiner (default is ``joiner``)
:param args: additional positional arguments which are forwarded to the add and needs function
:param kwargs: additional key value arguments which are forwarded to the add and needs function
:return: the modified Flow
"""
needs = _hanging_deployments(self)
if len(needs) == 1:
return self.add(name=name, needs=needs, *args, **kwargs)
return self.needs(name=name, needs=needs, *args, **kwargs)
# overload_inject_start_deployment
@overload
def add(
self,
*,
compression: Optional[str] = None,
connection_list: Optional[str] = None,
disable_auto_volume: Optional[bool] = False,
disable_reduce: Optional[bool] = False,
docker_kwargs: Optional[dict] = None,
entrypoint: Optional[str] = None,
env: Optional[dict] = None,
external: Optional[bool] = False,
floating: Optional[bool] = False,
force_update: Optional[bool] = False,
gpus: Optional[str] = None,
grpc_server_options: Optional[dict] = None,
host: Optional[str] = '0.0.0.0',
host_in: Optional[str] = '0.0.0.0',
install_requirements: Optional[bool] = False,
log_config: Optional[str] = None,
monitoring: Optional[bool] = False,
name: Optional[str] = None,
native: Optional[bool] = False,
output_array_type: Optional[str] = None,
polling: Optional[str] = 'ANY',
port: Optional[int] = None,
port_monitoring: Optional[str] = None,
py_modules: Optional[List[str]] = None,
quiet: Optional[bool] = False,
quiet_error: Optional[bool] = False,
quiet_remote_logs: Optional[bool] = False,
replicas: Optional[int] = 1,
retries: Optional[int] = -1,
runtime_cls: Optional[str] = 'WorkerRuntime',
shards: Optional[int] = 1,
timeout_ctrl: Optional[int] = 60,
timeout_ready: Optional[int] = 600000,
timeout_send: Optional[int] = None,
tls: Optional[bool] = False,
upload_files: Optional[List[str]] = None,
uses: Optional[Union[str, Type['BaseExecutor'], dict]] = 'BaseExecutor',
uses_after: Optional[Union[str, Type['BaseExecutor'], dict]] = None,
uses_after_address: Optional[str] = None,
uses_before: Optional[Union[str, Type['BaseExecutor'], dict]] = None,
uses_before_address: Optional[str] = None,
uses_metas: Optional[dict] = None,
uses_requests: Optional[dict] = None,
uses_with: Optional[dict] = None,
volumes: Optional[List[str]] = None,
when: Optional[dict] = None,
workspace: Optional[str] = None,
**kwargs,
) -> Union['Flow', 'AsyncFlow']:
"""Add an Executor to the current Flow object.
:param 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.
:param connection_list: dictionary JSON with a list of connections to configure
:param disable_auto_volume: Do not automatically mount a volume for dockerized Executors.
:param disable_reduce: Disable the built-in reduce mechanism, set this if the reduction is to be handled by the Executor connected to this Head
:param 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/
:param entrypoint: The entrypoint command overrides the ENTRYPOINT in Docker image. when not set then the Docker image ENTRYPOINT takes effective.
:param env: The map of environment variables that are available inside runtime
:param external: The Deployment will be considered an external Deployment that has been started independently from the Flow.This Deployment will not be context managed by the Flow.
:param 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.
:param force_update: If set, always pull the latest Hub Executor bundle even it exists on local
:param 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
:param grpc_server_options: 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}
:param host: The host address of the runtime, by default it is 0.0.0.0.
:param host_in: The host address for binding to, by default it is 0.0.0.0
:param install_requirements: If set, install `requirements.txt` in the Hub Executor bundle to local
:param log_config: The YAML config of the logger used in this object.
:param monitoring: If set, spawn an http server with a prometheus endpoint to expose metrics
:param 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.
:param native: If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.
:param 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.
:param 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'}
:param port: The port for input data to bind to, default is a random port between [49152, 65535]
:param port_monitoring: The port on which the prometheus server is exposed, default is a random port between [49152, 65535]
:param 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/>`__
:param quiet: If set, then no log will be emitted from this object.
:param quiet_error: If set, then exception stack information will not be added to the log
:param quiet_remote_logs: Do not display the streaming of remote logs on local console
:param replicas: The number of replicas in the deployment
:param retries: Number of retries per gRPC call. If <0 it defaults to max(3, num_replicas)
:param runtime_cls: The runtime class to run inside the Pod
:param 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
:param timeout_ctrl: The timeout in milliseconds of the control request, -1 for waiting forever
:param timeout_ready: The timeout in milliseconds of a Pod waits for the runtime to be ready, -1 for waiting forever
:param timeout_send: The timeout in milliseconds used when sending data requests to Executors, -1 means no timeout, disabled by default
:param tls: If set, connect to deployment using tls encryption
:param 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.
:param uses: 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
:param uses_after: The executor attached after the Pods described by --uses, typically used for receiving from all shards, accepted type follows `--uses`. This argument only applies for sharded Deployments (shards > 1).
:param uses_after_address: The address of the uses-before runtime
:param uses_before: The executor attached before the Pods described by --uses, typically before sending to all shards, accepted type follows `--uses`. This argument only applies for sharded Deployments (shards > 1).
:param uses_before_address: The address of the uses-before runtime
:param uses_metas: Dictionary of keyword arguments that will override the `metas` configuration in `uses`
:param uses_requests: Dictionary of keyword arguments that will override the `requests` configuration in `uses`
:param uses_with: Dictionary of keyword arguments that will override the `with` configuration in `uses`
:param 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.
:param when: The condition that the documents need to fulfill before reaching the Executor.The condition can be defined in the form of a `DocArray query condition <https://docarray.jina.ai/fundamentals/documentarray/find/#query-by-conditions>`
:param workspace: The working directory for any IO operations in this object. If not set, then derive from its parent `workspace`.
:return: a (new) Flow object with modification
.. # noqa: DAR202
.. # noqa: DAR101
.. # noqa: DAR003
"""
# overload_inject_end_deployment
@overload
def add(
self,
*,
needs: Optional[Union[str, Tuple[str], List[str]]] = None,
copy_flow: bool = True,
deployment_role: 'DeploymentRoleType' = DeploymentRoleType.DEPLOYMENT,
**kwargs,
) -> Union['Flow', 'AsyncFlow']:
"""
Add a Deployment to the current Flow object and return the new modified Flow object.
The attribute of the Deployment can be later changed with :py:meth:`set` or deleted with :py:meth:`remove`
:param needs: the name of the Deployment(s) that this Deployment receives data from.
One can also use 'gateway' to indicate the connection with the gateway.
:param deployment_role: the role of the Deployment, used for visualization and route planning
:param copy_flow: when set to true, then always copy the current Flow and do the modification on top of it then return, otherwise, do in-line modification
:param kwargs: other keyword-value arguments that the Deployment CLI supports
:return: a (new) Flow object with modification
.. # noqa: DAR202
.. # noqa: DAR101
.. # noqa: DAR003
.. # noqa: DAR401
"""
[docs] @allowed_levels([FlowBuildLevel.EMPTY])
def add(
self,
**kwargs,
) -> Union['Flow', 'AsyncFlow']:
# implementation_stub_inject_start_add
"""Add a Deployment to the current Flow object and return the new modified Flow object.
The attribute of the Deployment can be later changed with :py:meth:`set` or deleted with :py:meth:`remove`
:param 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.
:param connection_list: dictionary JSON with a list of connections to configure
:param disable_auto_volume: Do not automatically mount a volume for dockerized Executors.
:param disable_reduce: Disable the built-in reduce mechanism, set this if the reduction is to be handled by the Executor connected to this Head
:param 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/
:param entrypoint: The entrypoint command overrides the ENTRYPOINT in Docker image. when not set then the Docker image ENTRYPOINT takes effective.
:param env: The map of environment variables that are available inside runtime
:param external: The Deployment will be considered an external Deployment that has been started independently from the Flow.This Deployment will not be context managed by the Flow.
:param 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.
:param force_update: If set, always pull the latest Hub Executor bundle even it exists on local
:param 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
:param grpc_server_options: 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}
:param host: The host address of the runtime, by default it is 0.0.0.0.
:param host_in: The host address for binding to, by default it is 0.0.0.0
:param install_requirements: If set, install `requirements.txt` in the Hub Executor bundle to local
:param log_config: The YAML config of the logger used in this object.
:param monitoring: If set, spawn an http server with a prometheus endpoint to expose metrics
:param 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.
:param native: If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.
:param 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.
:param 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'}
:param port: The port for input data to bind to, default is a random port between [49152, 65535]
:param port_monitoring: The port on which the prometheus server is exposed, default is a random port between [49152, 65535]
:param 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/>`__
:param quiet: If set, then no log will be emitted from this object.
:param quiet_error: If set, then exception stack information will not be added to the log
:param quiet_remote_logs: Do not display the streaming of remote logs on local console
:param replicas: The number of replicas in the deployment
:param retries: Number of retries per gRPC call. If <0 it defaults to max(3, num_replicas)
:param runtime_cls: The runtime class to run inside the Pod
:param 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
:param timeout_ctrl: The timeout in milliseconds of the control request, -1 for waiting forever
:param timeout_ready: The timeout in milliseconds of a Pod waits for the runtime to be ready, -1 for waiting forever
:param timeout_send: The timeout in milliseconds used when sending data requests to Executors, -1 means no timeout, disabled by default
:param tls: If set, connect to deployment using tls encryption
:param 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.
:param uses: 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
:param uses_after: The executor attached after the Pods described by --uses, typically used for receiving from all shards, accepted type follows `--uses`. This argument only applies for sharded Deployments (shards > 1).
:param uses_after_address: The address of the uses-before runtime
:param uses_before: The executor attached before the Pods described by --uses, typically before sending to all shards, accepted type follows `--uses`. This argument only applies for sharded Deployments (shards > 1).
:param uses_before_address: The address of the uses-before runtime
:param uses_metas: Dictionary of keyword arguments that will override the `metas` configuration in `uses`
:param uses_requests: Dictionary of keyword arguments that will override the `requests` configuration in `uses`
:param uses_with: Dictionary of keyword arguments that will override the `with` configuration in `uses`
:param 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.
:param when: The condition that the documents need to fulfill before reaching the Executor.The condition can be defined in the form of a `DocArray query condition <https://docarray.jina.ai/fundamentals/documentarray/find/#query-by-conditions>`
:param workspace: The working directory for any IO operations in this object. If not set, then derive from its parent `workspace`.
:param needs: the name of the Deployment(s) that this Deployment receives data from. One can also use "gateway" to indicate the connection with the gateway.
:param deployment_role: the role of the Deployment, used for visualization and route planning
:param copy_flow: when set to true, then always copy the current Flow and do the modification on top of it then return, otherwise, do in-line modification
:param kwargs: other keyword-value arguments that the Deployment CLI supports
:return: a (new) Flow object with modification
:return: a (new) Flow object with modification
.. # noqa: DAR102
.. # noqa: DAR202
.. # noqa: DAR101
.. # noqa: DAR003
"""
# implementation_stub_inject_end_add
needs = kwargs.pop('needs', None)
copy_flow = kwargs.pop('copy_flow', True)
deployment_role = kwargs.get('deployment_role', DeploymentRoleType.DEPLOYMENT)
op_flow = copy.deepcopy(self) if copy_flow else self
# deployment naming logic
deployment_name = kwargs.get('name', None)
if deployment_name in op_flow._deployment_nodes:
new_name = f'{deployment_name}{len(op_flow._deployment_nodes)}'
self.logger.debug(
f'"{deployment_name}" is used in this Flow already! renamed it to "{new_name}"'
)
deployment_name = new_name
if not deployment_name:
deployment_name = f'executor{len(op_flow._deployment_nodes)}'
if not deployment_name.isidentifier():
# hyphen - can not be used in the name
raise ValueError(
f'name: {deployment_name} is invalid, please follow the python variable name conventions'
)
# needs logic
needs = op_flow._parse_endpoints(
op_flow, deployment_name, needs, connect_to_last_deployment=True
)
# set the kwargs inherit from `Flow(kwargs1=..., kwargs2=)`
for key, value in op_flow._common_kwargs.items():
# do not inherit from all the argument from the flow
if key not in kwargs and key not in [
'port',
'port_monitoring',
]:
kwargs[key] = value
# update kwargs of this Deployment
kwargs.update(
dict(
name=deployment_name,
deployment_role=deployment_role,
)
)
parser = set_deployment_parser()
if deployment_role == DeploymentRoleType.GATEWAY:
parser = set_gateway_parser()
args = ArgNamespace.kwargs2namespace(
kwargs, parser, True, fallback_parsers=FALLBACK_PARSERS
)
# deployment workspace if not set then derive from flow workspace
if args.workspace:
args.workspace = os.path.abspath(args.workspace)
else:
args.workspace = self.workspace
args.noblock_on_start = True
port = kwargs.get('port', None)
if not port:
port = helper.random_port()
args.port = port
if len(needs) > 1 and args.external and args.disable_reduce:
raise ValueError(
'External Executors with multiple needs have to do auto reduce.'
)
op_flow._deployment_nodes[deployment_name] = Deployment(args, needs)
if not args.floating:
op_flow._last_deployment = deployment_name
return op_flow
[docs] @allowed_levels([FlowBuildLevel.EMPTY])
def inspect(self, name: str = 'inspect', *args, **kwargs) -> 'Flow':
"""Add an inspection on the last changed Deployment in the Flow
Internally, it adds two Deployments to the Flow. But don't worry, the overhead is minimized and you
can remove them by simply using `Flow(inspect=FlowInspectType.REMOVE)` before using the Flow.
.. highlight:: bash
.. code-block:: bash
Flow -- PUB-SUB -- BaseDeployment(_pass) -- Flow
|
-- PUB-SUB -- InspectDeployment (Hanging)
In this way, :class:`InspectDeployment` looks like a simple ``_pass`` from outside and
does not introduce side-effects (e.g. changing the socket type) to the original Flow.
The original incoming and outgoing socket types are preserved.
This function is very handy for introducing an Evaluator into the Flow.
.. seealso::
:meth:`gather_inspect`
:param name: name of the Deployment
:param args: args for .add()
:param kwargs: kwargs for .add()
:return: the new instance of the Flow
"""
_last_deployment = self._last_deployment
op_flow = self.add(
name=name,
needs=_last_deployment,
deployment_role=DeploymentRoleType.INSPECT,
*args,
**kwargs,
)
# now remove uses and add an auxiliary Deployment
if 'uses' in kwargs:
kwargs.pop('uses')
op_flow = op_flow.add(
name=f'_aux_{name}',
needs=_last_deployment,
deployment_role=DeploymentRoleType.INSPECT_AUX_PASS,
*args,
**kwargs,
)
# register any future connection to _last_deployment by the auxiliary Deployment
op_flow._inspect_deployments[_last_deployment] = op_flow._last_deployment
return op_flow
[docs] @allowed_levels([FlowBuildLevel.EMPTY])
def gather_inspect(
self,
name: str = 'gather_inspect',
include_last_deployment: bool = True,
*args,
**kwargs,
) -> 'Flow':
"""Gather all inspect Deployments output into one Deployment. When the Flow has no inspect Deployment then the Flow itself
is returned.
.. note::
If ``--no-inspect`` is **not** given, then :meth:`gather_inspect` is auto called before :meth:`build`. So
in general you don't need to manually call :meth:`gather_inspect`.
:param name: the name of the gather Deployment
:param include_last_deployment: if to include the last modified Deployment in the Flow
:param args: args for .add()
:param kwargs: kwargs for .add()
:return: the modified Flow or the copy of it
.. seealso::
:meth:`inspect`
"""
needs = [
k
for k, v in self._deployment_nodes.items()
if v.role == DeploymentRoleType.INSPECT
]
if needs:
if include_last_deployment:
needs.append(self._last_deployment)
return self.add(
name=name,
needs=needs,
deployment_role=DeploymentRoleType.JOIN_INSPECT,
*args,
**kwargs,
)
else:
# no inspect node is in the graph, return the current graph
return self
def _get_gateway_target(self, prefix):
gateway_deployment = self._deployment_nodes[GATEWAY_NAME]
return (
f'{prefix}-{GATEWAY_NAME}',
{
'host': gateway_deployment.head_host,
'port': gateway_deployment.head_port,
'expected_parts': 0,
},
)
[docs] @allowed_levels([FlowBuildLevel.EMPTY])
def build(
self, copy_flow: bool = False, disable_build_sandbox: bool = False
) -> 'Flow':
"""
Build the current Flow and make it ready to use
.. note::
No need to manually call it since 0.0.8. When using Flow with the
context manager, or using :meth:`start`, :meth:`build` will be invoked.
:param copy_flow: when set to true, then always copy the current Flow and do the modification on top of it then return, otherwise, do in-line modification
:param disable_build_sandbox: when set to true, the sandbox building part will be skipped, will be used by `plot`
:return: the current Flow (by default)
.. note::
``copy_flow=True`` is recommended if you are building the same Flow multiple times in a row. e.g.
.. highlight:: python
.. code-block:: python
f = Flow()
with f:
f.index()
with f.build(copy_flow=True) as fl:
fl.search()
.. # noqa: DAR401
"""
op_flow = copy.deepcopy(self) if copy_flow else self
if op_flow.args.inspect == FlowInspectType.COLLECT:
op_flow.gather_inspect(copy_flow=False)
if not disable_build_sandbox:
for deployment in self._deployment_nodes.values():
deployment.update_sandbox_args()
if GATEWAY_NAME not in op_flow._deployment_nodes:
op_flow._add_gateway(
needs={op_flow._last_deployment},
graph_description=op_flow._get_graph_representation(),
deployments_addresses=op_flow._get_deployments_addresses(),
graph_conditions=op_flow._get_graph_conditions(),
deployments_disabled_reduce=op_flow._get_disabled_reduce_deployments(),
)
removed_deployments = []
# if set no_inspect then all inspect related nodes are removed
if op_flow.args.inspect == FlowInspectType.REMOVE:
filtered_deployment_nodes = OrderedDict()
for k, v in op_flow._deployment_nodes.items():
if not v.role.is_inspect:
filtered_deployment_nodes[k] = v
else:
removed_deployments.append(v.name)
op_flow._deployment_nodes = filtered_deployment_nodes
reverse_inspect_map = {
v: k for k, v in op_flow._inspect_deployments.items()
}
while (
len(op_flow._last_changed_deployment) > 0
and len(removed_deployments) > 0
and op_flow._last_deployment in removed_deployments
):
op_flow._last_changed_deployment.pop()
for end, deployment in op_flow._deployment_nodes.items():
# if an endpoint is being inspected, then replace it with inspected Deployment
# but not those inspect related node
if op_flow.args.inspect.is_keep:
deployment.needs = set(
ep
if deployment.role.is_inspect
else op_flow._inspect_deployments.get(ep, ep)
for ep in deployment.needs
)
else:
deployment.needs = set(
reverse_inspect_map.get(ep, ep) for ep in deployment.needs
)
hanging_deployments = _hanging_deployments(op_flow)
if hanging_deployments:
op_flow.logger.warning(
f'{hanging_deployments} are "floating" in this flow with no deployment receiving from them, '
f'you may want to double check if it is intentional or some mistake'
)
op_flow._build_level = FlowBuildLevel.GRAPH
if len(removed_deployments) > 0:
# very dirty
op_flow._deployment_nodes[GATEWAY_NAME].args.graph_description = json.dumps(
op_flow._get_graph_representation()
)
op_flow._deployment_nodes[
GATEWAY_NAME
].args.deployments_addresses = json.dumps(
op_flow._get_deployments_addresses()
)
op_flow._deployment_nodes[GATEWAY_NAME].update_pod_args()
return op_flow
def __call__(self, *args, **kwargs):
"""Builds the Flow
:param args: args for build
:param kwargs: kwargs for build
:return: the built Flow
"""
return self.build(*args, **kwargs)
def __enter__(self):
with CatchAllCleanupContextManager(self):
return self.start()
def __exit__(self, exc_type, exc_val, exc_tb):
if hasattr(self, '_stop_event'):
self._stop_event.set()
super().__exit__(exc_type, exc_val, exc_tb)
# unset all envs to avoid any side-effect
if self.args.env:
for k in self.args.env.keys():
os.environ.pop(k, None)
# do not know why but removing these 2 lines make 2 tests fail
if GATEWAY_NAME in self._deployment_nodes:
self._deployment_nodes.pop(GATEWAY_NAME)
self._build_level = FlowBuildLevel.EMPTY
self.logger.debug('flow is closed!')
self.logger.close()
[docs] @allowed_levels([FlowBuildLevel.EMPTY, FlowBuildLevel.GRAPH])
def start(self):
"""Start to run all Deployments in this Flow.
Remember to close the Flow with :meth:`close`.
Note that this method has a timeout of ``timeout_ready`` set in CLI,
which is inherited all the way from :class:`jina.orchestrate.pods.Pod`
.. # noqa: DAR401
:return: this instance
"""
if self._build_level.value < FlowBuildLevel.GRAPH.value:
self.build(copy_flow=False)
port_gateway = self._deployment_nodes[GATEWAY_NAME].args.port
if not (
is_port_free(__default_host__, port_gateway)
): # we check if the port is not used at parsing time as well for robustness
raise PortAlreadyUsed(f'port:{port_gateway}')
# set env only before the Deployment get started
if self.args.env:
for k, v in self.args.env.items():
os.environ[k] = str(v)
for k, v in self:
if not v.external:
self.enter_context(v)
self._wait_until_all_ready()
self._build_level = FlowBuildLevel.RUNNING
send_telemetry_event(event='start', obj=self)
return self
def _wait_until_all_ready(self):
results = {}
threads = []
def _wait_ready(_deployment_name, _deployment):
try:
if not _deployment.external:
results[_deployment_name] = 'pending'
_deployment.wait_start_success()
results[_deployment_name] = 'done'
except Exception as ex:
results[_deployment_name] = repr(ex)
def _polling_status(progress, task):
progress.update(task, total=len(results))
progress.start_task(task)
while True:
num_done = 0
pendings = []
for _k, _v in results.items():
sys.stdout.flush()
if _v == 'pending':
pendings.append(_k)
elif _v == 'done':
num_done += 1
else:
if 'JINA_EARLY_STOP' in os.environ:
self.logger.error(f'Flow is aborted due to {_k} {_v}.')
os._exit(1)
pending_str = ' '.join(pendings)
progress.update(task, completed=num_done, pending_str=pending_str)
if not pendings:
break
time.sleep(0.1)
progress = Progress(
SpinnerColumn(),
TextColumn('Waiting [b]{task.fields[pending_str]}[/]...', justify='right'),
BarColumn(),
MofNCompleteColumn(),
TimeElapsedColumn(),
transient=True,
)
with progress:
task = progress.add_task(
'wait', total=len(threads), pending_str='', start=False
)
# kick off all deployments wait-ready threads
for k, v in self:
t = threading.Thread(
target=_wait_ready,
args=(
k,
v,
),
daemon=True,
)
threads.append(t)
# kick off ip getter thread, address, http, graphq
all_panels = []
t_ip = threading.Thread(
target=self._get_summary_table, args=(all_panels, results), daemon=True
)
threads.append(t_ip)
# kick off spinner thread
t_m = threading.Thread(
target=_polling_status, args=(progress, task), daemon=True
)
threads.append(t_m)
for t in threads:
t.start()
for t in threads:
t.join()
error_deployments = [k for k, v in results.items() if v != 'done']
if error_deployments:
self.logger.error(
f'Flow is aborted due to {error_deployments} can not be started.'
)
self.close()
raise RuntimeFailToStart
from rich.rule import Rule
print(
Rule(':tada: Flow is ready to serve!'), *all_panels
) # can't use logger here see : https://github.com/Textualize/rich/discussions/2024
self.logger.debug(
f'{self.num_deployments} Deployments (i.e. {self.num_pods} Pods) are running in this Flow'
)
@property
def num_deployments(self) -> int:
"""Get the number of Deployments in this Flow
.. # noqa: DAR201"""
return len(self._deployment_nodes)
@property
def num_pods(self) -> int:
"""Get the number of pods (shards count) in this Flow
.. # noqa: DAR201"""
return sum(v.num_pods for v in self._deployment_nodes.values())
def __eq__(self, other: 'Flow') -> bool:
"""
Compare the topology of a Flow with another Flow.
Identification is defined by whether two flows share the same set of edges.
:param other: the second Flow object
:return: result of equality check
"""
if self._build_level.value < FlowBuildLevel.GRAPH.value:
op_flow = copy.deepcopy(self)
a = op_flow.build()
else:
a = self
if other._build_level.value < FlowBuildLevel.GRAPH.value:
op_flow_b = copy.deepcopy(other)
b = op_flow_b.build()
else:
b = other
return a._deployment_nodes == b._deployment_nodes
@property
def client(self) -> 'BaseClient':
"""Return a :class:`BaseClient` object attach to this Flow.
.. # noqa: DAR201"""
kwargs = dict(
host=self.host,
port=self.port,
protocol=self.protocol,
)
kwargs.update(self._common_kwargs)
return Client(**kwargs)
@property
def _mermaid_str(self):
mermaid_graph = [
'''
%%{init:{
"theme": "base",
"themeVariables": {
"primaryColor": "#fff",
"primaryBorderColor": "#fff",
"mainBkg": "#32C8CD",
"clusterBkg": "#EEEDE78C",
"secondaryBorderColor": "none",
"tertiaryBorderColor": "none",
"lineColor": "#a6d8da"
}
}}%%
'''.replace(
'\n', ''
),
'flowchart LR;',
]
deployment_nodes = []
# plot subgraphs
for node, v in self._deployment_nodes.items():
deployment_nodes.append(v.name)
deployment_mermaid = v._mermaid_str
mermaid_graph.extend(deployment_mermaid)
for node, v in self._deployment_nodes.items():
for need in sorted(v.needs):
need_print = need
if need == 'gateway':
need_print = 'gatewaystart[gateway]'
node_print = node
if node == 'gateway':
node_print = 'gatewayend[gateway]'
_s_role = self._deployment_nodes[need].role
_e_role = self._deployment_nodes[node].role
if self._deployment_nodes[need].external:
_s_role = 'EXTERNAL'
if self._deployment_nodes[node].external:
_e_role = 'EXTERNAL'
line_st = '-->'
if (
_s_role == DeploymentRoleType.INSPECT
or _e_role == DeploymentRoleType.INSPECT
):
line_st = '-.->'
mermaid_graph.append(
f'{need_print}:::{str(_s_role)} {line_st} {node_print}:::{str(_e_role)};'
)
mermaid_graph.append(
f'classDef {str(DeploymentRoleType.INSPECT)} stroke:#F29C9F'
)
mermaid_graph.append(
f'classDef {str(DeploymentRoleType.JOIN_INSPECT)} stroke:#F29C9F'
)
mermaid_graph.append(
f'classDef {str(DeploymentRoleType.GATEWAY)} fill:none,color:#000,stroke:none'
)
mermaid_graph.append(
f'classDef {str(DeploymentRoleType.INSPECT_AUX_PASS)} stroke-dasharray: 2 2'
)
mermaid_graph.append(f'classDef HEADTAIL fill:#32C8CD1D')
mermaid_graph.append(f'\nclassDef EXTERNAL fill:#fff,stroke:#32C8CD')
return '\n'.join(mermaid_graph)
[docs] def plot(
self,
output: Optional[str] = None,
vertical_layout: bool = False,
inline_display: bool = False,
build: bool = True,
copy_flow: bool = True,
) -> 'Flow':
"""
Visualize the Flow up to the current point
If a file name is provided it will create a jpg image with that name,
otherwise it will display the URL for mermaid.
If called within IPython notebook, it will be rendered inline,
otherwise an image will be created.
Example,
.. highlight:: python
.. code-block:: python
flow = Flow().add(name='deployment_a').plot('flow.svg')
:param output: a filename specifying the name of the image to be created,
the suffix svg/jpg determines the file type of the output image
:param vertical_layout: top-down or left-right layout
:param inline_display: show image directly inside the Jupyter Notebook
:param build: build the Flow first before plotting, gateway connection can be better showed
:param copy_flow: when set to true, then always copy the current Flow and
do the modification on top of it then return, otherwise, do in-line modification
:return: the Flow
"""
# deepcopy causes the below error while reusing a Flow in Jupyter
# 'Pickling an AuthenticationString object is disallowed for security reasons'
# no need to deep copy if the Graph is built because no change will be made to the Flow
op_flow = (
copy.deepcopy(self)
if (copy_flow and self._build_level.value == FlowBuildLevel.EMPTY)
else self
)
if build and op_flow._build_level.value == FlowBuildLevel.EMPTY:
op_flow.build(copy_flow=False, disable_build_sandbox=True)
mermaid_str = op_flow._mermaid_str
if vertical_layout:
mermaid_str = mermaid_str.replace('flowchart LR', 'flowchart TD')
image_type = 'svg'
if output and not output.endswith('svg'):
image_type = 'img'
url = op_flow._mermaid_to_url(mermaid_str, image_type)
showed = False
if inline_display:
try:
from IPython.display import Image, display
display(Image(url=url))
showed = True
except:
# no need to panic users
pass
if output:
download_mermaid_url(url, output)
elif not showed:
print(f'[link={url}]Click here to see the visualization in browser[/]')
return self
def _ipython_display_(self):
"""Displays the object in IPython as a side effect"""
self.plot(
inline_display=True, build=(self._build_level != FlowBuildLevel.GRAPH)
)
def _mermaid_to_url(self, mermaid_str: str, img_type: str) -> str:
"""
Render the current Flow as URL points to a SVG. It needs internet connection
:param mermaid_str: the mermaid representation
:param img_type: image type (svg/jpg)
:return: the url points to a SVG
"""
encoded_str = base64.b64encode(bytes(mermaid_str, 'utf-8')).decode('utf-8')
return f'https://mermaid.ink/{img_type}/{encoded_str}'
@property
def port(self) -> int:
"""Return the exposed port of the gateway
.. # noqa: DAR201
"""
if GATEWAY_NAME in self._deployment_nodes:
return self._deployment_nodes[GATEWAY_NAME].port
else:
return self._common_kwargs.get('port', None)
@port.setter
def port(self, value: int):
"""Set the new exposed port of the Flow (affects Gateway and Client)
:param value: the new port to expose
"""
self._common_kwargs['port'] = value
# Flow is build to graph already
if self._build_level >= FlowBuildLevel.GRAPH:
self[GATEWAY_NAME].args.port = self._common_kwargs['port']
# Flow is running already, then close the existing gateway
if self._build_level >= FlowBuildLevel.RUNNING:
self[GATEWAY_NAME].close()
self.enter_context(self[GATEWAY_NAME])
self[GATEWAY_NAME].wait_start_success()
@property
def host(self) -> str:
"""Return the local address of the gateway
.. # noqa: DAR201
"""
if GATEWAY_NAME in self._deployment_nodes:
return self._deployment_nodes[GATEWAY_NAME].host
else:
return self._common_kwargs.get('host', __default_host__)
@host.setter
def host(self, value: str):
"""Set the new host of the Flow (affects Gateway and Client)
:param value: the new port to expose
"""
self._common_kwargs['host'] = value
# Flow is build to graph already
if self._build_level >= FlowBuildLevel.GRAPH:
self[GATEWAY_NAME].args.host = self._common_kwargs['host']
# Flow is running already, then close the existing gateway
if self._build_level >= FlowBuildLevel.RUNNING:
self[GATEWAY_NAME].close()
self.enter_context(self[GATEWAY_NAME])
self[GATEWAY_NAME].wait_start_success()
@property
def monitoring(self) -> bool:
"""Return if the monitoring is enabled
.. # noqa: DAR201
"""
if GATEWAY_NAME in self._deployment_nodes:
return self[GATEWAY_NAME].args.monitoring
else:
return False
@property
def port_monitoring(self) -> Optional[int]:
"""Return if the monitoring is enabled
.. # noqa: DAR201
"""
if GATEWAY_NAME in self._deployment_nodes:
return self[GATEWAY_NAME].args.port_monitoring
else:
return self._common_kwargs.get('port_monitoring', None)
@property
def address_private(self) -> str:
"""Return the private IP address of the gateway for connecting from other machine in the same network
.. # noqa: DAR201"""
return get_internal_ip()
@property
def address_public(self) -> str:
"""Return the public IP address of the gateway for connecting from other machine in the public network
.. # noqa: DAR201"""
return get_public_ip()
def __iter__(self):
return self._deployment_nodes.items().__iter__()
def _init_table(self):
table = Table(
title=None, box=None, highlight=True, show_header=False, min_width=40
)
table.add_column('', justify='left')
table.add_column('', justify='right')
table.add_column('', justify='right')
return table
def _get_summary_table(self, all_panels: List[Panel], results):
results['summary'] = 'pending'
address_table = self._init_table()
_protocol = str(self.protocol)
if self.gateway_args.ssl_certfile and self.gateway_args.ssl_keyfile:
_protocol = f'{self.protocol}S'
address_table.add_row(
':chains:', 'Protocol', f':closed_lock_with_key: {_protocol}'
)
else:
address_table.add_row(':chains:', 'Protocol', _protocol)
_protocol = _protocol.lower()
address_table.add_row(
':house:',
'Local',
f'[link={_protocol}://{self.host}:{self.port}]{self.host}:{self.port}[/]',
)
address_table.add_row(
':lock:',
'Private',
f'[link={_protocol}://{self.address_private}:{self.port}]{self.address_private}:{self.port}[/]',
)
if self.address_public:
address_table.add_row(
':earth_africa:',
'Public',
f'[link={_protocol}://{self.address_public}:{self.port}]{self.address_public}:{self.port}[/]',
)
all_panels.append(
Panel(
address_table,
title=':link: [b]Endpoint[/]',
expand=False,
)
)
if self.protocol == GatewayProtocolType.HTTP:
http_ext_table = self._init_table()
_address = [
f'[link={_protocol}://localhost:{self.port}/docs]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/docs]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol}://{self.address_public}:{self.port}/docs]Public[/]'
)
http_ext_table.add_row(
':speech_balloon:',
'Swagger UI',
'.../docs',
)
_address = [
f'[link={_protocol}://localhost:{self.port}/redoc]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/redoc]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol}://{self.address_public}:{self.port}/redoc]Public[/]'
)
http_ext_table.add_row(
':books:',
'Redoc',
'.../redoc',
)
if self.gateway_args.expose_graphql_endpoint:
_address = [
f'[link={_protocol}://localhost:{self.port}/graphql]Local[/]',
f'[link={_protocol}://{self.address_private}:{self.port}/graphql]Private[/]',
]
if self.address_public:
_address.append(
f'[link={_protocol}://{self.address_public}:{self.port}/graphql]Public[/]'
)
http_ext_table.add_row(
':strawberry:',
'GraphQL UI',
'.../graphql',
)
all_panels.append(
Panel(
http_ext_table,
title=':gem: [b]HTTP extension[/]',
expand=False,
)
)
if self.monitoring:
monitor_ext_table = self._init_table()
for name, deployment in self:
if deployment.args.monitoring:
for replica in deployment.pod_args['pods'][0]:
_address = [
f'[link=http://localhost:{replica.port_monitoring}]Local[/]',
f'[link=http://{self.address_private}:{replica.port_monitoring}]Private[/]',
]
if self.address_public:
_address.append(
f'[link=http://{self.address_public}:{deployment.args.port_monitoring}]Public[/]'
)
_name = (
name
if len(deployment.pod_args['pods'][0]) == 1
else replica.name
)
monitor_ext_table.add_row(
':flashlight:', # upstream issue: they dont have :torch: emoji, so we use :flashlight:
# to represent observability of Prometheus (even they have :torch: it will be a war
# between AI community and Cloud-native community fighting on this emoji)
_name,
f'...[b]:{replica.port_monitoring}[/]',
)
all_panels.append(
Panel(
monitor_ext_table,
title=':gem: [b]Prometheus extension[/]',
expand=False,
)
)
results['summary'] = 'done'
return all_panels
[docs] @allowed_levels([FlowBuildLevel.RUNNING])
def block(
self, stop_event: Optional[Union[threading.Event, multiprocessing.Event]] = None
):
"""Block the Flow until `stop_event` is set or user hits KeyboardInterrupt
:param stop_event: a threading event or a multiprocessing event that onces set will resume the control Flow
to main thread.
"""
try:
if stop_event is None:
self._stop_event = (
threading.Event()
) #: this allows `.close` to close the Flow from another thread/proc
self._stop_event.wait()
else:
stop_event.wait()
except KeyboardInterrupt:
pass
@property
def protocol(self) -> GatewayProtocolType:
"""Return the protocol of this Flow
:return: the protocol of this Flow
"""
v = self._common_kwargs.get('protocol', GatewayProtocolType.GRPC)
if isinstance(v, str):
v = GatewayProtocolType.from_string(v)
return v
@protocol.setter
def protocol(self, value: Union[str, GatewayProtocolType]):
"""Set the protocol of this Flow, can only be set before the Flow has been started
:param value: the protocol to set
"""
# Flow is running already, protocol cant be changed anymore
if self._build_level >= FlowBuildLevel.RUNNING:
raise RuntimeError('Protocol can not be changed after the Flow has started')
if isinstance(value, str):
self._common_kwargs['protocol'] = GatewayProtocolType.from_string(value)
elif isinstance(value, GatewayProtocolType):
self._common_kwargs['protocol'] = value
else:
raise TypeError(f'{value} must be either `str` or `GatewayProtocolType`')
# Flow is build to graph already
if self._build_level >= FlowBuildLevel.GRAPH:
self[GATEWAY_NAME].args.protocol = self._common_kwargs['protocol']
def __getitem__(self, item):
if isinstance(item, str):
return self._deployment_nodes[item]
elif isinstance(item, int):
return list(self._deployment_nodes.values())[item]
else:
raise TypeError(f'{typename(item)} is not supported')
@property
def workspace(self) -> str:
"""Return the workspace path of the flow.
.. # noqa: DAR201"""
if self.args.workspace is not None:
return os.path.abspath(self.args.workspace)
else:
return None
@workspace.setter
def workspace(self, value: str):
"""set workspace dir for flow & all deployments
:param value: workspace to be set
"""
self.args.workspace = value
for k, p in self:
p.args.workspace = value
p.update_pod_args()
@property
def workspace_id(self) -> Dict[str, str]:
"""Get all Deployments' ``workspace_id`` values in a dict
.. # noqa: DAR201"""
return {
k: p.args.workspace_id for k, p in self if hasattr(p.args, 'workspace_id')
}
@workspace_id.setter
def workspace_id(self, value: str):
"""Set all Deployments' ``workspace_id`` to ``value``
:param value: a hexadecimal UUID string
"""
uuid.UUID(value)
for k, p in self:
if hasattr(p.args, 'workspace_id'):
p.args.workspace_id = value
args = getattr(p, 'pod_args', getattr(p, 'shards_args', None))
if args is None:
raise ValueError(
f'could not find "pod_args" or "shards_args" on {p}'
)
values = None
if isinstance(args, dict):
values = args.values()
elif isinstance(args, list):
values = args
for v in values:
if v and isinstance(v, argparse.Namespace):
v.workspace_id = value
if v and isinstance(v, List):
for i in v:
i.workspace_id = value
@property
def env(self) -> Optional[Dict]:
"""Get all envs to be set in the Flow
:return: envs as dict
"""
return self.args.env
@env.setter
def env(self, value: Dict[str, str]):
"""set env vars for flow & all deployments.
This can be used by jinad to set envs for Flow and all child objects
:param value: value to be set
"""
self.args.env = value
for k, v in self:
v.args.env = value
v.update_pod_args()
@overload
def expose_endpoint(self, exec_endpoint: str, path: Optional[str] = None):
"""Expose an Executor's endpoint (defined by `@requests(on=...)`) to HTTP endpoint for easier access.
After expose, you can send data request directly to `http://hostname:port/endpoint`.
:param exec_endpoint: the endpoint string, by convention starts with `/`
:param path: the HTTP endpoint string, when not given, it is `exec_endpoint`
"""
...
@overload
def expose_endpoint(
self,
exec_endpoint: str,
*,
path: Optional[str] = None,
status_code: int = 200,
tags: Optional[List[str]] = None,
summary: Optional[str] = None,
description: Optional[str] = None,
response_description: str = 'Successful Response',
deprecated: Optional[bool] = None,
methods: Optional[List[str]] = None,
operation_id: Optional[str] = None,
response_model_by_alias: bool = True,
response_model_exclude_unset: bool = False,
response_model_exclude_defaults: bool = False,
response_model_exclude_none: bool = False,
include_in_schema: bool = True,
name: Optional[str] = None,
):
"""Expose an Executor's endpoint (defined by `@requests(on=...)`) to HTTP endpoint for easier access.
After expose, you can send data request directly to `http://hostname:port/endpoint`.
Use this method to specify your HTTP endpoint with richer semantic and schema.
:param exec_endpoint: the endpoint string, by convention starts with `/`
# noqa: DAR101
"""
...
[docs] @allowed_levels([FlowBuildLevel.EMPTY])
def expose_endpoint(self, exec_endpoint: str, **kwargs):
"""Expose an Executor's endpoint (defined by `@requests(on=...)`) to HTTP endpoint for easier access.
After expose, you can send data request directly to `http://hostname:port/endpoint`.
:param exec_endpoint: the endpoint string, by convention starts with `/`
# noqa: DAR101
# noqa: DAR102
"""
self._endpoints_mapping[exec_endpoint] = kwargs
[docs] def to_kubernetes_yaml(
self,
output_base_path: str,
k8s_namespace: Optional[str] = None,
include_gateway: bool = True,
):
"""
Converts the Flow into a set of yaml deployments to deploy in Kubernetes.
If you don't want to rebuild image on Jina Hub,
you can set `JINA_HUB_NO_IMAGE_REBUILD` environment variable.
:param output_base_path: The base path where to dump all the yaml files
:param k8s_namespace: The name of the k8s namespace to set for the configurations. If None, the name of the Flow will be used.
:param include_gateway: Defines if the gateway deployment should be included, defaults to True
"""
import yaml
if self._build_level.value < FlowBuildLevel.GRAPH.value:
self.build(copy_flow=False)
from jina.orchestrate.deployments.config.k8s import K8sDeploymentConfig
k8s_namespace = k8s_namespace or self.args.name or 'default'
for node, v in self._deployment_nodes.items():
if v.external or (node == 'gateway' and not include_gateway):
continue
if node == 'gateway' and v.args.default_port:
from jina.serve.networking import GrpcConnectionPool
v.args.port = GrpcConnectionPool.K8S_PORT
v.first_pod_args.port = GrpcConnectionPool.K8S_PORT
v.args.port_monitoring = GrpcConnectionPool.K8S_PORT_MONITORING
v.first_pod_args.port_monitoring = (
GrpcConnectionPool.K8S_PORT_MONITORING
)
v.args.default_port = False
deployment_base = os.path.join(output_base_path, node)
k8s_deployment = K8sDeploymentConfig(
args=v.args,
k8s_namespace=k8s_namespace,
k8s_deployments_addresses=self._get_k8s_deployments_addresses(
k8s_namespace
)
if node == 'gateway'
else None,
)
configs = k8s_deployment.to_kubernetes_yaml()
for name, k8s_objects in configs:
filename = os.path.join(deployment_base, f'{name}.yml')
os.makedirs(deployment_base, exist_ok=True)
with open(filename, 'w+') as fp:
for i, k8s_object in enumerate(k8s_objects):
yaml.dump(k8s_object, fp)
if i < len(k8s_objects) - 1:
fp.write('---\n')
self.logger.info(
f'K8s yaml files have been created under [b]{output_base_path}[/]. You can use it by running [b]kubectl apply -R -f {output_base_path}[/]'
)
to_k8s_yaml = to_kubernetes_yaml
[docs] def to_docker_compose_yaml(
self,
output_path: Optional[str] = None,
network_name: Optional[str] = None,
include_gateway: bool = True,
):
"""
Converts the Flow into a yaml file to run with `docker-compose up`
:param output_path: The output path for the yaml file
:param network_name: The name of the network that will be used by the deployment name
:param include_gateway: Defines if the gateway deployment should be included, defaults to True
"""
import yaml
if self._build_level.value < FlowBuildLevel.GRAPH.value:
self.build(copy_flow=False)
output_path = output_path or 'docker-compose.yml'
network_name = network_name or 'jina-network'
from jina.orchestrate.deployments.config.docker_compose import (
DockerComposeConfig,
)
docker_compose_dict = {
'version': '3.3',
'networks': {network_name: {'driver': 'bridge'}},
}
services = {}
for node, v in self._deployment_nodes.items():
if v.external or (node == 'gateway' and not include_gateway):
continue
docker_compose_deployment = DockerComposeConfig(
args=v.args,
deployments_addresses=self._get_docker_compose_deployments_addresses(),
)
service_configs = docker_compose_deployment.to_docker_compose_config()
for service_name, service in service_configs:
service['networks'] = [network_name]
services[service_name] = service
docker_compose_dict['services'] = services
with open(output_path, 'w+') as fp:
yaml.dump(docker_compose_dict, fp, sort_keys=False)
command = (
'docker-compose up'
if output_path is None
else f'docker-compose -f {output_path} up'
)
self.logger.info(
f'Docker compose file has been created under [b]{output_path}[/b]. You can use it by running [b]{command}[/b]'
)
@property
def client_args(self) -> argparse.Namespace:
"""Get Client settings.
# noqa: DAR201
"""
if 'port' in self._common_kwargs:
kwargs = copy.deepcopy(self._common_kwargs)
kwargs['port'] = self._common_kwargs['port']
return ArgNamespace.kwargs2namespace(kwargs, set_client_cli_parser())
@property
def gateway_args(self) -> argparse.Namespace:
"""Get Gateway settings.
# noqa: DAR201
"""
return ArgNamespace.kwargs2namespace(self._common_kwargs, set_gateway_parser())
def _update_network_interface(self, **kwargs):
"""Update the network interface of this Flow (affects Gateway & Client)
:param kwargs: new network settings
"""
self._common_kwargs.update(kwargs)
def __getattribute__(self, item):
obj = super().__getattribute__(item)
if (
item == 'load_config' and inspect.ismethod(obj) and obj.__self__ is Flow
): # check if obj load config call from an instance and not the Class
warnings.warn(
"Calling `load_config` from a Flow instance will override all of the instance's initial parameters. We recommend to use `Flow.load_config(...)` instead"
)
return obj
