# Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ["GraphServer", "create_graph_server", "GraphContext", "MockEvent"]
import asyncio
import base64
import copy
import json
import os
import socket
import traceback
import uuid
from datetime import datetime, timezone
from typing import Any, Optional, Union
import pandas as pd
import storey
from nuclio import Context as NuclioContext
from nuclio.request import Logger as NuclioLogger
import mlrun
import mlrun.common.constants
import mlrun.common.helpers
import mlrun.common.schemas
import mlrun.model_monitoring
import mlrun.utils
from mlrun.config import config
from mlrun.errors import err_to_str
from mlrun.secrets import SecretsStore
from ..common.helpers import parse_versioned_object_uri
from ..common.schemas.model_monitoring.constants import FileTargetKind
from ..common.schemas.serving import MAX_BATCH_JOB_DURATION
from ..datastore import DataItem, get_stream_pusher
from ..datastore.store_resources import ResourceCache
from ..errors import MLRunInvalidArgumentError
from ..execution import MLClientCtx
from ..model import ModelObj
from ..utils import get_caller_globals
from .states import (
FlowStep,
MonitoredStep,
RootFlowStep,
RouterStep,
get_function,
graph_root_setter,
)
from .utils import event_id_key, event_path_key
DUMMY_STREAM = "dummy://"
class _StreamContext:
"""Handles the stream context for the events stream process. Includes the configuration for the output stream
that will be used for pushing the events from the nuclio model serving function"""
def __init__(self, enabled: bool, parameters: dict, function_uri: str):
"""
Initialize _StreamContext object.
:param enabled: A boolean indication for applying the stream context
:param parameters: Dictionary of optional parameters, such as `log_stream` and `stream_args`. Note that these
parameters might be relevant to the output source such as `kafka_brokers` if
the output source is from type Kafka.
:param function_uri: Full value of the function uri, usually it's <project-name>/<function-name>
"""
self.enabled = False
self.hostname = socket.gethostname()
self.function_uri = function_uri
self.output_stream = None
stream_uri = None
log_stream = parameters.get(FileTargetKind.LOG_STREAM, "")
if (enabled or log_stream) and function_uri:
self.enabled = True
project, _, _, _ = parse_versioned_object_uri(
function_uri, config.active_project
)
stream_args = parameters.get("stream_args", {})
if log_stream == DUMMY_STREAM:
# Dummy stream used for testing, see tests/serving/test_serving.py
stream_uri = DUMMY_STREAM
elif not stream_args.get("mock"): # if not a mock: `context.is_mock = True`
stream_uri = mlrun.model_monitoring.get_stream_path(project=project)
if log_stream:
# Update the stream path to the log stream value
stream_uri = log_stream.format(project=project)
self.output_stream = get_stream_pusher(stream_uri, **stream_args)
else:
# Get the output stream from the profile
self.output_stream = mlrun.model_monitoring.helpers.get_output_stream(
project=project, mock=stream_args.get("mock", False)
)
[docs]
class GraphServer(ModelObj):
kind = "server"
def __init__(
self,
graph=None,
parameters=None,
load_mode=None,
function_uri=None,
verbose=False,
version=None,
functions=None,
graph_initializer=None,
error_stream=None,
track_models=None,
secret_sources=None,
default_content_type=None,
function_name=None,
function_tag=None,
project=None,
model_endpoint_creation_task_name=None,
):
self._graph = None
self.graph: Union[RouterStep, RootFlowStep] = graph
self.function_uri = function_uri
self.parameters = parameters or {}
self.verbose = verbose
self.load_mode = load_mode or "sync"
self.version = version or "v2"
self.context = None
self._current_function = None
self.functions = functions or {}
self.graph_initializer = graph_initializer
self.error_stream = error_stream
self.track_models = track_models
self._error_stream_object = None
self.secret_sources = secret_sources
self._secrets = SecretsStore.from_list(secret_sources)
self._db_conn = None
self.resource_cache = None
self.default_content_type = default_content_type
self.http_trigger = True
self.function_name = function_name
self.function_tag = function_tag
self.project = project
self.model_endpoint_creation_task_name = model_endpoint_creation_task_name
[docs]
def set_current_function(self, function):
"""set which child function this server is currently running on"""
self._current_function = function
@property
def graph(self) -> Union[RootFlowStep, RouterStep]:
return self._graph
@graph.setter
def graph(self, graph):
graph_root_setter(self, graph)
[docs]
def set_error_stream(self, error_stream):
"""set/initialize the error notification stream"""
self.error_stream = error_stream
if error_stream:
self._error_stream_object = get_stream_pusher(error_stream)
else:
self._error_stream_object = None
def _get_db(self):
return mlrun.get_run_db(secrets=self._secrets)
[docs]
def init_states(
self,
context,
namespace,
resource_cache: ResourceCache = None,
logger=None,
is_mock=False,
monitoring_mock=False,
):
"""for internal use, initialize all steps (recursively)"""
if self.secret_sources:
self._secrets = SecretsStore.from_list(self.secret_sources)
if self.error_stream:
self._error_stream_object = get_stream_pusher(self.error_stream)
self.resource_cache = resource_cache or ResourceCache()
context = GraphContext(server=self, nuclio_context=context, logger=logger)
context.is_mock = is_mock
context.monitoring_mock = monitoring_mock
context.root = self.graph
context.stream = _StreamContext(
self.track_models, self.parameters, self.function_uri
)
context.current_function = self._current_function
context.get_store_resource = self.resource_cache.resource_getter(
self._get_db(), self._secrets
)
context.get_table = self.resource_cache.get_table
context.verbose = self.verbose
self.context = context
if self.graph_initializer:
if callable(self.graph_initializer):
handler = self.graph_initializer
else:
handler = get_function(self.graph_initializer, namespace or [])
handler(self)
context.root = self.graph
[docs]
def init_object(self, namespace):
self.graph.init_object(self.context, namespace, self.load_mode, reset=True)
[docs]
def test(
self,
path: str = "/",
body: Optional[Union[str, bytes, dict]] = None,
method: str = "",
headers: Optional[str] = None,
content_type: Optional[str] = None,
silent: bool = False,
get_body: bool = True,
event_id: Optional[str] = None,
trigger: "MockTrigger" = None,
offset=None,
time=None,
):
"""invoke a test event into the server to simulate/test server behavior
example::
server = create_graph_server()
server.add_model("my", class_name=MyModelClass, model_path="{path}", z=100)
print(server.test("my/infer", testdata))
:param path: api path, e.g. (/{router.url_prefix}/{model-name}/..) path
:param body: message body (dict or json str/bytes)
:param method: optional, GET, POST, ..
:param headers: optional, request headers, ..
:param content_type: optional, http mime type
:param silent: don't raise on error responses (when not 20X)
:param get_body: return the body as py object (vs serialize response into json)
:param event_id: specify the unique event ID (by default a random value will be generated)
:param trigger: nuclio trigger info or mlrun.serving.server.MockTrigger class (holds kind and name)
:param offset: trigger offset (for streams)
:param time: event time Datetime or str, default to now()
"""
if not self.graph:
raise MLRunInvalidArgumentError(
"no models or steps were set, use function.set_topology() and add steps"
)
if not method:
method = "POST" if body else "GET"
event = MockEvent(
body=body,
path=path,
method=method,
headers=headers,
content_type=content_type,
event_id=event_id,
trigger=trigger,
offset=offset,
time=time,
)
resp = self.run(event, get_body=get_body)
if hasattr(resp, "status_code") and resp.status_code >= 300 and not silent:
raise RuntimeError(f"failed ({resp.status_code}): {resp.body}")
return resp
[docs]
def run(self, event, context=None, get_body=False, extra_args=None):
server_context = self.context
context = context or server_context
event.content_type = event.content_type or self.default_content_type or ""
if event.headers:
if event_id_key in event.headers:
event.id = event.headers.get(event_id_key)
if event_path_key in event.headers:
event.path = event.headers.get(event_path_key)
if isinstance(event.body, (str, bytes)) and (
not event.content_type or event.content_type in ["json", "application/json"]
):
# assume it is json and try to load
try:
body = json.loads(event.body)
event.body = body
except (json.decoder.JSONDecodeError, UnicodeDecodeError) as exc:
if event.content_type in ["json", "application/json"]:
# if its json type and didnt load, raise exception
message = f"failed to json decode event, {err_to_str(exc)}"
context.logger.error(message)
server_context.push_error(event, message, source="_handler")
return context.Response(
body=message, content_type="text/plain", status_code=400
)
try:
response = self.graph.run(event, **(extra_args or {}))
except Exception as exc:
message = f"{exc.__class__.__name__}: {err_to_str(exc)}"
if server_context.verbose:
message += "\n" + str(traceback.format_exc())
context.logger.error(f"run error, {traceback.format_exc()}")
server_context.push_error(event, message, source="_handler")
return context.Response(
body=message, content_type="text/plain", status_code=400
)
if asyncio.iscoroutine(response):
return self._process_async_response(context, response, get_body)
else:
return self._process_response(context, response, get_body)
async def _process_async_response(self, context, response, get_body):
return self._process_response(context, await response, get_body)
def _process_response(self, context, response, get_body):
body = response.body
if (
isinstance(context, MLClientCtx)
or isinstance(body, context.Response)
or get_body
):
return body
if body and not isinstance(body, (str, bytes)):
body = json.dumps(body)
return context.Response(
body=body, content_type="application/json", status_code=200
)
return body
[docs]
def wait_for_completion(self):
"""wait for async operation to complete"""
return self.graph.wait_for_completion()
def add_error_raiser_step(
graph: RootFlowStep, monitored_steps: dict[str, MonitoredStep]
) -> RootFlowStep:
monitored_steps_raisers = {}
user_steps = list(graph.steps.values())
for monitored_step in monitored_steps.values():
error_step = graph.add_step(
class_name="mlrun.serving.states.ModelRunnerErrorRaiser",
name=f"{monitored_step.name}_error_raise",
after=monitored_step.name,
full_event=True,
raise_exception=monitored_step.raise_exception,
models_names=list(monitored_step.class_args["models"].keys()),
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
if monitored_step.responder:
monitored_step.responder = False
error_step.respond()
monitored_steps_raisers[monitored_step.name] = error_step.name
error_step.on_error = monitored_step.on_error
if monitored_steps_raisers:
for step in user_steps:
if step.after:
if isinstance(step.after, list):
for i in range(len(step.after)):
if step.after[i] in monitored_steps_raisers:
step.after[i] = monitored_steps_raisers[step.after[i]]
else:
if (
isinstance(step.after, str)
and step.after in monitored_steps_raisers
):
step.after = monitored_steps_raisers[step.after]
return graph
def add_monitoring_general_steps(
project: str,
graph: RootFlowStep,
context,
serving_spec,
pause_until_background_task_completion: bool,
) -> tuple[RootFlowStep, FlowStep]:
"""
Adding the monitoring flow connection steps, this steps allow the graph to reconstruct the serving event enrich it
and push it to the model monitoring stream
system_steps structure -
"background_task_status_step" --> "filter_none" --> "monitoring_pre_processor_step" --> "flatten_events"
--> "sampling_step" --> "filter_none_sampling" --> "model_monitoring_stream"
"""
background_task_status_step = None
if pause_until_background_task_completion:
background_task_status_step = graph.add_step(
"mlrun.serving.system_steps.BackgroundTaskStatus",
"background_task_status_step",
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
monitor_flow_step = graph.add_step(
"storey.Filter",
"filter_none",
_fn="(event is not None)",
after="background_task_status_step" if background_task_status_step else None,
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
if background_task_status_step:
monitor_flow_step = background_task_status_step
graph.add_step(
"mlrun.serving.system_steps.MonitoringPreProcessor",
"monitoring_pre_processor_step",
after="filter_none",
full_event=True,
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
# flatten the events
graph.add_step(
"storey.FlatMap",
"flatten_events",
_fn="(event)",
after="monitoring_pre_processor_step",
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
graph.add_step(
"mlrun.serving.system_steps.SamplingStep",
"sampling_step",
after="flatten_events",
sampling_percentage=float(
serving_spec.get("parameters", {}).get("sampling_percentage", 100.0)
if isinstance(serving_spec, dict)
else getattr(serving_spec, "parameters", {}).get(
"sampling_percentage", 100.0
),
),
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
graph.add_step(
"storey.Filter",
"filter_none_sampling",
_fn="(event is not None)",
after="sampling_step",
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
if getattr(context, "is_mock", False):
graph.add_step(
"mlrun.serving.system_steps.MockStreamPusher",
"model_monitoring_stream",
after="filter_none_sampling",
model_endpoint_creation_strategy=mlrun.common.schemas.ModelEndpointCreationStrategy.SKIP,
)
else:
stream_uri = mlrun.model_monitoring.get_stream_path(
project=project,
function_name=mlrun.common.schemas.MonitoringFunctionNames.STREAM,
)
context.logger.info_with(
"Creating Model Monitoring stream target using uri:", uri=stream_uri
)
graph.add_step(
">>",
"model_monitoring_stream",
path=stream_uri,
sharding_func=mlrun.common.schemas.model_monitoring.constants.StreamProcessingEvent.ENDPOINT_ID,
after="filter_none_sampling",
)
return graph, monitor_flow_step
def add_system_steps_to_graph(
project: str,
graph: RootFlowStep,
track_models: bool,
context,
serving_spec,
pause_until_background_task_completion: bool = True,
) -> RootFlowStep:
if not (isinstance(graph, RootFlowStep) and graph.include_monitored_step()):
return graph
monitored_steps = graph.get_monitored_steps()
graph = add_error_raiser_step(graph, monitored_steps)
if track_models:
background_task_status_step = None
graph, monitor_flow_step = add_monitoring_general_steps(
project,
graph,
context,
serving_spec,
pause_until_background_task_completion,
)
if background_task_status_step:
monitor_flow_step = background_task_status_step
# Connect each model runner to the monitoring step:
for step_name, step in monitored_steps.items():
if monitor_flow_step.after:
if isinstance(monitor_flow_step.after, list):
monitor_flow_step.after.append(step_name)
elif isinstance(monitor_flow_step.after, str):
monitor_flow_step.after = [monitor_flow_step.after, step_name]
else:
monitor_flow_step.after = [
step_name,
]
context.logger.info_with(
"Server graph after adding system steps",
graph=str(graph.steps),
)
return graph
def v2_serving_init(context, namespace=None):
"""hook for nuclio init_context()"""
context.logger.info("Initializing server from spec")
spec = mlrun.utils.get_serving_spec()
server = GraphServer.from_dict(spec)
server.graph = add_system_steps_to_graph(
server.project,
copy.deepcopy(server.graph),
spec.get("track_models"),
context,
spec,
)
if config.log_level.lower() == "debug":
server.verbose = True
if hasattr(context, "trigger"):
server.http_trigger = getattr(context.trigger, "kind", "http") == "http"
context.logger.info_with(
"Setting current function",
current_function=os.getenv("SERVING_CURRENT_FUNCTION", ""),
)
server.set_current_function(os.getenv("SERVING_CURRENT_FUNCTION", ""))
context.logger.info_with(
"Initializing states", namespace=namespace or get_caller_globals()
)
kwargs = {}
if hasattr(context, "is_mock"):
kwargs["is_mock"] = context.is_mock
server.init_states(
context,
namespace or get_caller_globals(),
**kwargs,
)
context.logger.info("Initializing graph steps")
server.init_object(namespace or get_caller_globals())
# set the handler hook to point to our handler
setattr(context, "mlrun_handler", v2_serving_handler)
setattr(context, "_server", server)
context.logger.info_with("Serving was initialized", verbose=server.verbose)
if server.verbose:
context.logger.info(server.to_yaml())
_set_callbacks(server, context)
async def async_execute_graph(
context: MLClientCtx,
data: DataItem,
timestamp_column: Optional[str],
batching: bool,
batch_size: Optional[int],
read_as_lists: bool,
nest_under_inputs: bool,
) -> list[Any]:
spec = mlrun.utils.get_serving_spec()
namespace = {}
code = os.getenv("MLRUN_EXEC_CODE")
if code:
code = base64.b64decode(code).decode("utf-8")
exec(code, namespace)
else:
# TODO: find another way to get the local file path, or ensure that MLRUN_EXEC_CODE
# gets set in local flow and not just in the remote pod
source_filename = spec.get("filename", None)
if source_filename:
with open(source_filename) as f:
exec(f.read(), namespace)
server = GraphServer.from_dict(spec)
if server.model_endpoint_creation_task_name:
context.logger.info(
f"Waiting for model endpoint creation task '{server.model_endpoint_creation_task_name}'..."
)
background_task = (
mlrun.get_run_db().wait_for_background_task_to_reach_terminal_state(
project=server.project,
name=server.model_endpoint_creation_task_name,
)
)
task_state = background_task.status.state
if task_state == mlrun.common.schemas.BackgroundTaskState.failed:
raise mlrun.errors.MLRunRuntimeError(
"Aborting job due to model endpoint creation background task failure"
)
elif task_state != mlrun.common.schemas.BackgroundTaskState.succeeded:
# this shouldn't happen, but we need to know if it does
raise mlrun.errors.MLRunRuntimeError(
"Aborting job because the model endpoint creation background task did not succeed "
f"(status='{task_state}')"
)
df = data.as_df()
if df.empty:
context.logger.warn("Job terminated due to empty inputs (0 rows)")
return []
track_models = spec.get("track_models")
if track_models and timestamp_column:
context.logger.info(f"Sorting dataframe by {timestamp_column}")
df[timestamp_column] = pd.to_datetime( # in case it's a string
df[timestamp_column]
)
df.sort_values(by=timestamp_column, inplace=True)
if len(df) > 1:
start_time = df[timestamp_column].iloc[0]
end_time = df[timestamp_column].iloc[-1]
time_range = end_time - start_time
start_time = start_time.isoformat()
end_time = end_time.isoformat()
# TODO: tie this to the controller's base period
if time_range > pd.Timedelta(MAX_BATCH_JOB_DURATION):
raise mlrun.errors.MLRunRuntimeError(
f"Dataframe time range is too long: {time_range}. "
"Please disable tracking or reduce the input dataset's time range below the defined limit "
f"of {MAX_BATCH_JOB_DURATION}."
)
else:
start_time = end_time = df["timestamp"].iloc[0].isoformat()
else:
# end time will be set from clock time when the batch completes
start_time = datetime.now(tz=timezone.utc).isoformat()
server.graph = add_system_steps_to_graph(
server.project,
copy.deepcopy(server.graph),
track_models,
context,
spec,
pause_until_background_task_completion=False, # we've already awaited it
)
if config.log_level.lower() == "debug":
server.verbose = True
context.logger.info_with("Initializing states", namespace=namespace)
kwargs = {}
if hasattr(context, "is_mock"):
kwargs["is_mock"] = context.is_mock
server.init_states(
context=None, # this context is expected to be a nuclio context, which we don't have in this flow
namespace=namespace,
**kwargs,
)
context.logger.info("Initializing graph steps")
server.init_object(namespace)
context.logger.info_with("Graph was initialized", verbose=server.verbose)
if server.verbose:
context.logger.info(server.to_yaml())
async def run(body):
event = storey.Event(id=index, body=body)
if timestamp_column:
if batching:
# we use the first row in the batch to determine the timestamp for the whole batch
body = body[0]
if not isinstance(body, dict):
raise mlrun.errors.MLRunRuntimeError(
f"When timestamp_column=True, event body must be a dict – got {type(body).__name__} instead"
)
if timestamp_column not in body:
raise mlrun.errors.MLRunRuntimeError(
f"Event body '{body}' did not contain timestamp column '{timestamp_column}'"
)
event._original_timestamp = body[timestamp_column]
return await server.run(event, context)
if batching and not batch_size:
batch_size = len(df)
batch = []
tasks = []
for index, row in df.iterrows():
data = row.to_list() if read_as_lists else row.to_dict()
if nest_under_inputs:
data = {"inputs": data}
if batching:
batch.append(data)
if len(batch) == batch_size:
tasks.append(asyncio.create_task(run(batch)))
batch = []
else:
tasks.append(asyncio.create_task(run(data)))
if batch:
tasks.append(asyncio.create_task(run(batch)))
responses = await asyncio.gather(*tasks)
termination_result = server.wait_for_completion()
if asyncio.iscoroutine(termination_result):
await termination_result
model_endpoint_uids = spec.get("model_endpoint_uids", [])
# needed for output_stream to be created
server = GraphServer.from_dict(spec)
server.init_states(None, namespace)
batch_completion_time = datetime.now(tz=timezone.utc).isoformat()
if not timestamp_column:
end_time = batch_completion_time
mm_stream_record = dict(
kind="batch_complete",
project=context.project,
first_timestamp=start_time,
last_timestamp=end_time,
batch_completion_time=batch_completion_time,
)
output_stream = server.context.stream.output_stream
for mep_uid in spec.get("model_endpoint_uids", []):
mm_stream_record["endpoint_id"] = mep_uid
output_stream.push(mm_stream_record, partition_key=mep_uid)
context.logger.info(
f"Job completed processing {len(df)} rows",
timestamp_column=timestamp_column,
model_endpoint_uids=model_endpoint_uids,
)
return responses
def execute_graph(
context: MLClientCtx,
data: DataItem,
timestamp_column: Optional[str] = None,
batching: bool = False,
batch_size: Optional[int] = None,
read_as_lists: bool = False,
nest_under_inputs: bool = False,
) -> (list[Any], Any):
"""
Execute graph as a job, from start to finish.
:param context: The job's execution client context.
:param data: The input data to the job, to be pushed into the graph row by row, or in batches.
:param timestamp_column: The name of the column that will be used as the timestamp for model monitoring purposes.
when timestamp_column is used in conjunction with batching, the first timestamp will be used for the entire
batch.
:param batching: Whether to push one or more batches into the graph rather than row by row.
:param batch_size: The number of rows to push per batch. If not set, and batching=True, the entire dataset will
be pushed into the graph in one batch.
:param read_as_lists: Whether to read each row as a list instead of a dictionary.
:param nest_under_inputs: Whether to wrap each row with {"inputs": ...}.
:return: A list of responses.
"""
return asyncio.run(
async_execute_graph(
context,
data,
timestamp_column,
batching,
batch_size,
read_as_lists,
nest_under_inputs,
)
)
def _set_callbacks(server, context):
if not server.graph.supports_termination() or not hasattr(context, "platform"):
return
if hasattr(context.platform, "set_termination_callback"):
context.logger.info(
"Setting termination callback to terminate graph on worker shutdown"
)
async def termination_callback():
context.logger.info("Termination callback called")
maybe_coroutine = server.wait_for_completion()
if asyncio.iscoroutine(maybe_coroutine):
await maybe_coroutine
context.logger.info("Termination of async flow is completed")
context.platform.set_termination_callback(termination_callback)
if hasattr(context.platform, "set_drain_callback"):
context.logger.info(
"Setting drain callback to terminate and restart the graph on a drain event (such as rebalancing)"
)
async def drain_callback():
context.logger.info("Drain callback called")
maybe_coroutine = server.wait_for_completion()
if asyncio.iscoroutine(maybe_coroutine):
await maybe_coroutine
context.logger.info(
"Termination of async flow is completed. Rerunning async flow."
)
# Rerun the flow without reconstructing it
server.graph._run_async_flow()
context.logger.info("Async flow restarted")
context.platform.set_drain_callback(drain_callback)
def v2_serving_handler(context, event, get_body=False):
"""hook for nuclio handler()"""
if context._server.http_trigger:
# Workaround for a Nuclio bug where it sometimes passes b'' instead of None due to dirty memory
if event.body == b"":
event.body = None
# original path is saved in stream_path so it can be used by explicit ack, but path is reset to / as a
# workaround for NUC-178
# nuclio 1.12.12 added the topic attribute, and we must use it as part of the fix for NUC-233
# TODO: Remove fallback on event.path once support for nuclio<1.12.12 is dropped
event.stream_path = getattr(event, "topic", event.path)
if hasattr(event, "trigger") and event.trigger.kind in (
"kafka",
"kafka-cluster",
"v3ioStream",
"v3io-stream",
"rabbit-mq",
"rabbitMq",
):
event.path = "/"
return context._server.run(event, context, get_body)
[docs]
def create_graph_server(
parameters=None,
load_mode=None,
graph=None,
verbose=False,
current_function=None,
**kwargs,
) -> GraphServer:
"""create graph server host/emulator for local or test runs
Usage example::
server = create_graph_server(graph=RouterStep(), parameters={})
server.init(None, globals())
server.graph.add_route("my", class_name=MyModelClass, model_path="{path}", z=100)
print(server.test("/v2/models/my/infer", testdata))
"""
parameters = parameters or {}
server = GraphServer(graph, parameters, load_mode, verbose=verbose, **kwargs)
server.set_current_function(
current_function or os.getenv("SERVING_CURRENT_FUNCTION", "")
)
return server
class MockTrigger:
"""mock nuclio event trigger"""
def __init__(self, kind="", name=""):
self.kind = kind
self.name = name
class MockEvent:
"""mock basic nuclio event object"""
def __init__(
self,
body=None,
content_type=None,
headers=None,
method=None,
path=None,
event_id=None,
trigger: MockTrigger = None,
offset=None,
time=None,
):
self.id = event_id or uuid.uuid4().hex
self.key = ""
self.body = body
# optional
self.headers = headers or {}
self.method = method
self.path = path or "/"
self.content_type = content_type
self.error = None
self.trigger = trigger or MockTrigger()
self.offset = offset or 0
def __str__(self):
error = f", error={self.error}" if self.error else ""
return f"Event(id={self.id}, body={self.body}, method={self.method}, path={self.path}{error})"
class Response:
def __init__(self, headers=None, body=None, content_type=None, status_code=200):
self.headers = headers or {}
self.body = body
self.status_code = status_code
self.content_type = content_type or "text/plain"
def __repr__(self):
cls = self.__class__.__name__
items = self.__dict__.items()
args = (f"{key}={repr(value)}" for key, value in items)
args_str = ", ".join(args)
return f"{cls}({args_str})"
[docs]
class GraphContext:
"""Graph context object"""
def __init__(
self,
level="info", # Unused argument
logger=None,
server=None,
nuclio_context: Optional[NuclioContext] = None,
) -> None:
self.state = None
self.logger = logger
self.worker_id = 0
self.Response = Response
self.verbose = False
self.stream = None
self.root = None
self.executor: Optional[storey.flow.RunnableExecutor] = None
if nuclio_context:
self.logger: NuclioLogger = nuclio_context.logger
self.Response = nuclio_context.Response
if hasattr(nuclio_context, "trigger") and hasattr(
nuclio_context.trigger, "kind"
):
self.trigger = nuclio_context.trigger.kind
self.worker_id = nuclio_context.worker_id
if hasattr(nuclio_context, "platform"):
self.platform = nuclio_context.platform
elif not logger:
self.logger: mlrun.utils.Logger = mlrun.utils.logger
self._server = server
self.current_function = None
self.get_store_resource = None
self.get_table = None
self.is_mock = False
self.monitoring_mock = False
self._project_obj = None
@property
def server(self):
return self._server
@property
def project_obj(self):
if not self._project_obj:
self._project_obj = mlrun.get_run_db().get_project(name=self.project)
return self._project_obj
@property
def project(self) -> str:
"""current project name (for the current function)"""
project, _, _, _ = mlrun.common.helpers.parse_versioned_object_uri(
self._server.function_uri
)
return project
[docs]
def push_error(self, event, message, source=None, **kwargs):
if self.verbose:
self.logger.error(
f"got error from {source} state:\n{event.body}\n{message}"
)
if self._server and self._server._error_stream_object:
try:
message = format_error(
self._server, self, source, event, message, kwargs
)
self._server._error_stream_object.push(message)
except Exception as ex:
message = traceback.format_exc()
self.logger.error(f"failed to write to error stream: {ex}\n{message}")
[docs]
def get_param(self, key: str, default=None):
if self._server and self._server.parameters:
return self._server.parameters.get(key, default)
return default
[docs]
def get_secret(self, key: str):
if self._server and self._server._secrets:
return self._server._secrets.get(key)
return None
[docs]
def get_remote_endpoint(self, name, external=True):
"""return the remote nuclio/serving function http(s) endpoint given its name
:param name: the function name/uri in the form [project/]function-name[:tag]
:param external: return the external url (returns the external url by default)
"""
if "://" in name:
return name
project, uri, tag, _ = mlrun.common.helpers.parse_versioned_object_uri(
self._server.function_uri
)
if name.startswith("."):
name = f"{uri}-{name[1:]}"
else:
project, name, tag, _ = mlrun.common.helpers.parse_versioned_object_uri(
name, project
)
(
state,
fullname,
_,
_,
_,
function_status,
) = mlrun.runtimes.nuclio.function.get_nuclio_deploy_status(name, project, tag)
if state in ["error", "unhealthy"]:
raise ValueError(
f"Nuclio function {fullname} is in error state, cannot be accessed"
)
key = "externalInvocationUrls" if external else "internalInvocationUrls"
urls = function_status.get(key)
if not urls:
raise ValueError(f"cannot read {key} for nuclio function {fullname}")
return f"http://{urls[0]}"
def format_error(server, context, source, event, message, args):
return {
"function_uri": server.function_uri,
"worker": context.worker_id,
"host": socket.gethostname(),
"source": source,
"event": {"id": event.id, "body": event.body},
"message": message,
"args": args,
}
