# 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.
import json
import os
from copy import deepcopy
from typing import List, Union
import nuclio
from nuclio import KafkaTrigger
import mlrun
import mlrun.common.schemas
from mlrun.model_monitoring.tracking_policy import TrackingPolicy
from ..datastore import parse_kafka_url
from ..model import ObjectList
from ..secrets import SecretsStore
from ..serving.server import GraphServer, create_graph_server
from ..serving.states import (
RootFlowStep,
RouterStep,
StepKinds,
TaskStep,
graph_root_setter,
new_remote_endpoint,
params_to_step,
)
from ..utils import get_caller_globals, logger, set_paths
from .function import NuclioSpec, RemoteRuntime
from .function_reference import FunctionReference
serving_subkind = "serving_v2"
def new_v2_model_server(
name,
model_class: str,
models: dict = None,
filename="",
protocol="",
image="",
endpoint="",
workers=8,
canary=None,
):
f = ServingRuntime()
if not image:
name, spec, code = nuclio.build_file(
filename, name=name, handler="handler", kind=serving_subkind
)
f.spec.base_spec = spec
f.metadata.name = name
f.spec.default_class = model_class
params = None
if protocol:
params = {"protocol": protocol}
if models:
for name, model_path in models.items():
f.add_model(name, model_path=model_path, parameters=params)
f.with_http(workers, host=endpoint, canary=canary)
if image:
f.from_image(image)
return f
class ServingSpec(NuclioSpec):
_dict_fields = NuclioSpec._dict_fields + [
"graph",
"load_mode",
"graph_initializer",
"function_refs",
"parameters",
"models",
"default_content_type",
"error_stream",
"default_class",
"secret_sources",
"track_models",
"tracking_policy",
]
def __init__(
self,
command=None,
args=None,
image=None,
mode=None,
entry_points=None,
description=None,
replicas=None,
min_replicas=None,
max_replicas=None,
volumes=None,
volume_mounts=None,
env=None,
resources=None,
config=None,
base_spec=None,
no_cache=None,
source=None,
image_pull_policy=None,
function_kind=None,
service_account=None,
readiness_timeout=None,
readiness_timeout_before_failure=None,
models=None,
graph=None,
parameters=None,
default_class=None,
load_mode=None,
build=None,
function_refs=None,
graph_initializer=None,
error_stream=None,
track_models=None,
tracking_policy=None,
secret_sources=None,
default_content_type=None,
node_name=None,
node_selector=None,
affinity=None,
disable_auto_mount=False,
priority_class_name=None,
default_handler=None,
pythonpath=None,
workdir=None,
image_pull_secret=None,
tolerations=None,
preemption_mode=None,
security_context=None,
service_type=None,
add_templated_ingress_host_mode=None,
clone_target_dir=None,
state_thresholds=None,
disable_default_http_trigger=None,
):
super().__init__(
command=command,
args=args,
image=image,
mode=mode,
entry_points=entry_points,
description=description,
replicas=replicas,
min_replicas=min_replicas,
max_replicas=max_replicas,
volumes=volumes,
volume_mounts=volume_mounts,
env=env,
resources=resources,
config=config,
base_spec=base_spec,
no_cache=no_cache,
source=source,
image_pull_policy=image_pull_policy,
function_kind=serving_subkind,
service_account=service_account,
readiness_timeout=readiness_timeout,
readiness_timeout_before_failure=readiness_timeout_before_failure,
build=build,
node_name=node_name,
node_selector=node_selector,
affinity=affinity,
disable_auto_mount=disable_auto_mount,
priority_class_name=priority_class_name,
default_handler=default_handler,
pythonpath=pythonpath,
workdir=workdir,
image_pull_secret=image_pull_secret,
tolerations=tolerations,
preemption_mode=preemption_mode,
security_context=security_context,
service_type=service_type,
add_templated_ingress_host_mode=add_templated_ingress_host_mode,
clone_target_dir=clone_target_dir,
disable_default_http_trigger=disable_default_http_trigger,
)
self.models = models or {}
self._graph = None
self.graph: Union[RouterStep, RootFlowStep] = graph
self.parameters = parameters or {}
self.default_class = default_class
self.load_mode = load_mode
self._function_refs: ObjectList = None
self.function_refs = function_refs or []
self.graph_initializer = graph_initializer
self.error_stream = error_stream
self.track_models = track_models
self.tracking_policy = tracking_policy
self.secret_sources = secret_sources or []
self.default_content_type = default_content_type
@property
def graph(self) -> Union[RouterStep, RootFlowStep]:
"""states graph, holding the serving workflow/DAG topology"""
return self._graph
@graph.setter
def graph(self, graph):
graph_root_setter(self, graph)
@property
def function_refs(self) -> List[FunctionReference]:
"""function references, list of optional child function refs"""
return self._function_refs
@function_refs.setter
def function_refs(self, function_refs: List[FunctionReference]):
self._function_refs = ObjectList.from_list(FunctionReference, function_refs)
[docs]class ServingRuntime(RemoteRuntime):
"""MLRun Serving Runtime"""
kind = "serving"
@property
def spec(self) -> ServingSpec:
return self._spec
@spec.setter
def spec(self, spec):
self._spec = self._verify_dict(spec, "spec", ServingSpec)
[docs] def set_topology(
self,
topology=None,
class_name=None,
engine=None,
exist_ok=False,
**class_args,
) -> Union[RootFlowStep, RouterStep]:
"""set the serving graph topology (router/flow) and root class or params
examples::
# simple model router topology
graph = fn.set_topology("router")
fn.add_model(name, class_name="ClassifierModel", model_path=model_uri)
# async flow topology
graph = fn.set_topology("flow", engine="async")
graph.to("MyClass").to(name="to_json", handler="json.dumps").respond()
topology options are::
router - root router + multiple child route states/models
route is usually determined by the path (route key/name)
can specify special router class and router arguments
flow - workflow (DAG) with a chain of states
flow support "sync" and "async" engines, branches are not allowed in sync mode
when using async mode calling state.respond() will mark the state as the
one which generates the (REST) call response
:param topology: - graph topology, router or flow
:param class_name: - optional for router, router class name/path or router object
:param engine: - optional for flow, sync or async engine (default to async)
:param exist_ok: - allow overriding existing topology
:param class_args: - optional, router/flow class init args
:return graph object (fn.spec.graph)
"""
topology = topology or StepKinds.router
if self.spec.graph and not exist_ok:
raise mlrun.errors.MLRunInvalidArgumentError(
"graph topology is already set, cannot be overwritten"
)
if topology == StepKinds.router:
if class_name and hasattr(class_name, "to_dict"):
_, step = params_to_step(class_name, None)
if step.kind != StepKinds.router:
raise mlrun.errors.MLRunInvalidArgumentError(
"provided class is not a router step, must provide a router class in router topology"
)
else:
step = RouterStep(class_name=class_name, class_args=class_args)
self.spec.graph = step
elif topology == StepKinds.flow:
self.spec.graph = RootFlowStep(engine=engine)
else:
raise mlrun.errors.MLRunInvalidArgumentError(
f"unsupported topology {topology}, use 'router' or 'flow'"
)
return self.spec.graph
[docs] def set_tracking(
self,
stream_path: str = None,
batch: int = None,
sample: int = None,
stream_args: dict = None,
tracking_policy: Union[TrackingPolicy, dict] = None,
):
"""apply on your serving function to monitor a deployed model, including real-time dashboards to detect drift
and analyze performance.
:param stream_path: Path/url of the tracking stream e.g. v3io:///users/mike/mystream
you can use the "dummy://" path for test/simulation.
:param batch: Micro batch size (send micro batches of N records at a time).
:param sample: Sample size (send only one of N records).
:param stream_args: Stream initialization parameters, e.g. shards, retention_in_hours, ..
:param tracking_policy: Tracking policy object or a dictionary that will be converted into a tracking policy
object. By using TrackingPolicy, the user can apply his model monitoring requirements,
such as setting the scheduling policy of the model monitoring batch job or changing
the image of the model monitoring stream.
example::
# initialize a new serving function
serving_fn = mlrun.import_function("hub://v2-model-server", new_name="serving")
# apply model monitoring and set monitoring batch job to run every 3 hours
tracking_policy = {'default_batch_intervals':"0 */3 * * *"}
serving_fn.set_tracking(tracking_policy=tracking_policy)
"""
# Applying model monitoring configurations
self.spec.track_models = True
self.spec.tracking_policy = None
if tracking_policy:
if isinstance(tracking_policy, dict):
# Convert tracking policy dictionary into `model_monitoring.TrackingPolicy` object
self.spec.tracking_policy = TrackingPolicy.from_dict(tracking_policy)
else:
# Tracking_policy is already a `model_monitoring.TrackingPolicy` object
self.spec.tracking_policy = tracking_policy
if stream_path:
self.spec.parameters["log_stream"] = stream_path
if batch:
self.spec.parameters["log_stream_batch"] = batch
if sample:
self.spec.parameters["log_stream_sample"] = sample
if stream_args:
self.spec.parameters["stream_args"] = stream_args
[docs] def add_model(
self,
key: str,
model_path: str = None,
class_name: str = None,
model_url: str = None,
handler: str = None,
router_step: str = None,
child_function: str = None,
**class_args,
):
"""add ml model and/or route to the function.
Example, create a function (from the notebook), add a model class, and deploy::
fn = code_to_function(kind='serving')
fn.add_model('boost', model_path, model_class='MyClass', my_arg=5)
fn.deploy()
only works with router topology, for nested topologies (model under router under flow)
need to add router to flow and use router.add_route()
:param key: model api key (or name:version), will determine the relative url/path
:param model_path: path to mlrun model artifact or model directory file/object path
:param class_name: V2 Model python class name or a model class instance
(can also module.submodule.class and it will be imported automatically)
:param model_url: url of a remote model serving endpoint (cannot be used with model_path)
:param handler: for advanced users!, override default class handler name (do_event)
:param router_step: router step name (to determine which router we add the model to in graphs
with multiple router steps)
:param child_function: child function name, when the model runs in a child function
:param class_args: extra kwargs to pass to the model serving class __init__
(can be read in the model using .get_param(key) method)
"""
graph = self.spec.graph
if not graph:
graph = self.set_topology()
if graph.kind != StepKinds.router:
if router_step:
if router_step not in graph:
raise ValueError(
f"router step {router_step} not present in the graph"
)
graph = graph[router_step]
else:
routers = [
step
for step in graph.steps.values()
if step.kind == StepKinds.router
]
if len(routers) == 0:
raise ValueError(
"graph does not contain any router, add_model can only be "
"used when there is a router step"
)
if len(routers) > 1:
raise ValueError(
f"found {len(routers)} routers, please specify the router_step"
" you would like to add this model to"
)
graph = routers[0]
if class_name and hasattr(class_name, "to_dict"):
if model_path:
class_name.model_path = model_path
key, state = params_to_step(class_name, key)
else:
if not model_path and not model_url:
raise ValueError("model_path or model_url must be provided")
class_name = class_name or self.spec.default_class
if class_name and not isinstance(class_name, str):
raise ValueError(
"class name must be a string (name of module.submodule.name)"
)
if model_path and not class_name:
raise ValueError("model_path must be provided with class_name")
if model_path:
model_path = str(model_path)
if model_url:
state = new_remote_endpoint(model_url, **class_args)
else:
class_args = deepcopy(class_args)
class_args["model_path"] = model_path
state = TaskStep(
class_name, class_args, handler=handler, function=child_function
)
return graph.add_route(key, state)
[docs] def add_child_function(
self, name, url=None, image=None, requirements=None, kind=None
):
"""in a multi-function pipeline add child function
example::
fn.add_child_function('enrich', './enrich.ipynb', 'mlrun/mlrun')
:param name: child function name
:param url: function/code url, support .py, .ipynb, .yaml extensions
:param image: base docker image for the function
:param requirements: py package requirements file path OR list of packages
:param kind: mlrun function/runtime kind
:return function object
"""
function_reference = FunctionReference(
url, image, requirements=requirements, kind=kind or "serving"
)
self._spec.function_refs.update(function_reference, name)
func = function_reference.to_function(self.kind)
return func
def _add_ref_triggers(self):
"""add stream trigger to downstream child functions"""
for function_name, stream in self.spec.graph.get_queue_links().items():
if stream.path:
if function_name not in self._spec.function_refs.keys():
raise ValueError(f"function reference {function_name} not present")
group = stream.options.get("group", f"{function_name}-consumer-group")
child_function = self._spec.function_refs[function_name]
trigger_args = stream.trigger_args or {}
engine = self.spec.graph.engine or "async"
if mlrun.mlconf.is_explicit_ack() and engine == "async":
trigger_args["explicit_ack_mode"] = trigger_args.get(
"explicit_ack_mode", "explicitOnly"
)
extra_attributes = trigger_args.get("extra_attributes", {})
trigger_args["extra_attributes"] = extra_attributes
extra_attributes["worker_allocation_mode"] = extra_attributes.get(
"worker_allocation_mode", "static"
)
if (
stream.path.startswith("kafka://")
or "kafka_bootstrap_servers" in stream.options
):
brokers = stream.options.get("kafka_bootstrap_servers")
if brokers:
brokers = brokers.split(",")
topic, brokers = parse_kafka_url(stream.path, brokers)
trigger = KafkaTrigger(
brokers=brokers,
topics=[topic],
consumer_group=group,
**trigger_args,
)
child_function.function_object.add_trigger("kafka", trigger)
else:
# V3IO doesn't allow hyphens in object names
group = group.replace("-", "_")
child_function.function_object.add_v3io_stream_trigger(
stream.path, group=group, shards=stream.shards, **trigger_args
)
def _deploy_function_refs(self, builder_env: dict = None):
"""set metadata and deploy child functions"""
for function_ref in self._spec.function_refs.values():
logger.info(f"deploy child function {function_ref.name} ...")
function_object = function_ref.function_object
if not function_object:
function_object = function_ref.to_function(self.kind)
function_object.metadata.name = function_ref.fullname(self)
function_object.metadata.project = self.metadata.project
function_object.metadata.tag = self.metadata.tag
function_object.metadata.labels = function_object.metadata.labels or {}
function_object.metadata.labels[
"mlrun/parent-function"
] = self.metadata.name
function_object._is_child_function = True
if not function_object.spec.graph:
# copy the current graph only if the child doesnt have a graph of his own
function_object.set_env("SERVING_CURRENT_FUNCTION", function_ref.name)
function_object.spec.graph = self.spec.graph
function_object.verbose = self.verbose
function_object.spec.secret_sources = self.spec.secret_sources
function_object.deploy(builder_env=builder_env)
[docs] def remove_states(self, keys: list):
"""remove one, multiple, or all states/models from the spec (blank list for all)"""
if self.spec.graph:
self.spec.graph.clear_children(keys)
[docs] def with_secrets(self, kind, source):
"""register a secrets source (file, env or dict)
read secrets from a source provider to be used in workflows, example::
task.with_secrets('file', 'file.txt')
task.with_secrets('inline', {'key': 'val'})
task.with_secrets('env', 'ENV1,ENV2')
task.with_secrets('vault', ['secret1', 'secret2'...])
# If using an empty secrets list [] then all accessible secrets will be available.
task.with_secrets('vault', [])
# To use with Azure key vault, a k8s secret must be created with the following keys:
# kubectl -n <namespace> create secret generic azure-key-vault-secret \\
# --from-literal=tenant_id=<service principal tenant ID> \\
# --from-literal=client_id=<service principal client ID> \\
# --from-literal=secret=<service principal secret key>
task.with_secrets('azure_vault', {
'name': 'my-vault-name',
'k8s_secret': 'azure-key-vault-secret',
# An empty secrets list may be passed ('secrets': []) to access all vault secrets.
'secrets': ['secret1', 'secret2'...]
})
:param kind: secret type (file, inline, env)
:param source: secret data or link (see example)
:returns: The Runtime (function) object
"""
if kind == "vault" and isinstance(source, list):
source = {"project": self.metadata.project, "secrets": source}
self.spec.secret_sources.append({"kind": kind, "source": source})
return self
[docs] def deploy(
self,
project="",
tag="",
verbose=False,
auth_info: mlrun.common.schemas.AuthInfo = None,
builder_env: dict = None,
force_build: bool = False,
):
"""deploy model serving function to a local/remote cluster
:param project: optional, override function specified project name
:param tag: specify unique function tag (a different function service is created for every tag)
:param verbose: verbose logging
:param auth_info: The auth info to use to communicate with the Nuclio dashboard, required only when providing
dashboard
:param builder_env: env vars dict for source archive config/credentials e.g. builder_env={"GIT_TOKEN": token}
:param force_build: set True for force building the image
"""
load_mode = self.spec.load_mode
if load_mode and load_mode not in ["sync", "async"]:
raise ValueError(f"illegal model loading mode {load_mode}")
if not self.spec.graph:
raise ValueError("nothing to deploy, .spec.graph is none, use .add_model()")
if self.spec.graph.kind != StepKinds.router and not getattr(
self, "_is_child_function", None
):
# initialize or create required streams/queues
self.spec.graph.check_and_process_graph()
self.spec.graph.init_queues()
functions_in_steps = self.spec.graph.list_child_functions()
child_functions = list(self._spec.function_refs.keys())
for function in functions_in_steps:
if function not in child_functions:
raise mlrun.errors.MLRunInvalidArgumentError(
f"function {function} is used in steps and is not defined, "
"use the .add_child_function() to specify child function attributes"
)
# Handle secret processing before handling child functions, since secrets are transferred to them
if self.spec.secret_sources:
# Before passing to remote builder, secrets values must be retrieved (for example from ENV)
# and stored as inline secrets. Otherwise, they will not be available to the builder.
self._secrets = SecretsStore.from_list(self.spec.secret_sources)
self.spec.secret_sources = self._secrets.to_serial()
if self._spec.function_refs:
# ensure the function is available to the UI while deploying the child functions
self.save(versioned=False)
# deploy child functions
self._add_ref_triggers()
self._deploy_function_refs()
logger.info(f"deploy root function {self.metadata.name} ...")
return super().deploy(
project,
tag,
verbose,
auth_info,
builder_env=builder_env,
force_build=force_build,
)
def _get_runtime_env(self):
env = super()._get_runtime_env()
function_name_uri_map = {f.name: f.uri(self) for f in self.spec.function_refs}
serving_spec = {
"function_uri": self._function_uri(),
"version": "v2",
"parameters": self.spec.parameters,
"graph": self.spec.graph.to_dict() if self.spec.graph else {},
"load_mode": self.spec.load_mode,
"functions": function_name_uri_map,
"graph_initializer": self.spec.graph_initializer,
"error_stream": self.spec.error_stream,
"track_models": self.spec.track_models,
"tracking_policy": self.spec.tracking_policy.to_dict()
if self.spec.tracking_policy
else None,
"default_content_type": self.spec.default_content_type,
}
if self.spec.secret_sources:
self._secrets = SecretsStore.from_list(self.spec.secret_sources)
serving_spec["secret_sources"] = self._secrets.to_serial()
env["SERVING_SPEC_ENV"] = json.dumps(serving_spec)
return env
[docs] def to_mock_server(
self,
namespace=None,
current_function="*",
track_models=False,
workdir=None,
**kwargs,
) -> GraphServer:
"""create mock server object for local testing/emulation
:param namespace: one or list of namespaces/modules to search the steps classes/functions in
:param log_level: log level (error | info | debug)
:param current_function: specify if you want to simulate a child function, * for all functions
:param track_models: allow model tracking (disabled by default in the mock server)
:param workdir: working directory to locate the source code (if not the current one)
"""
# set the namespaces/modules to look for the steps code in
namespace = namespace or []
if not isinstance(namespace, list):
namespace = [namespace]
module = mlrun.run.function_to_module(self, silent=True, workdir=workdir)
if module:
namespace.append(module)
namespace.append(get_caller_globals())
if workdir:
old_workdir = os.getcwd()
workdir = os.path.realpath(workdir)
set_paths(workdir)
os.chdir(workdir)
server = create_graph_server(
parameters=self.spec.parameters,
load_mode=self.spec.load_mode,
graph=self.spec.graph,
verbose=self.verbose,
current_function=current_function,
graph_initializer=self.spec.graph_initializer,
track_models=track_models and self.spec.track_models,
function_uri=self._function_uri(),
secret_sources=self.spec.secret_sources,
default_content_type=self.spec.default_content_type,
**kwargs,
)
server.init_states(
context=None,
namespace=namespace,
logger=logger,
is_mock=True,
)
if workdir:
os.chdir(old_workdir)
server.init_object(namespace)
return server
[docs] def plot(self, filename=None, format=None, source=None, **kw):
"""plot/save graph using graphviz
example::
serving_fn = mlrun.new_function("serving", image="mlrun/mlrun", kind="serving")
serving_fn.add_model('my-classifier',model_path=model_path,
class_name='mlrun.frameworks.sklearn.SklearnModelServer')
serving_fn.plot(rankdir="LR")
:param filename: target filepath for the image (None for the notebook)
:param format: The output format used for rendering (``'pdf'``, ``'png'``, etc.)
:param source: source step to add to the graph
:param kw: kwargs passed to graphviz, e.g. rankdir="LR" (see: https://graphviz.org/doc/info/attrs.html)
:return: graphviz graph object
"""
return self.spec.graph.plot(filename, format=format, source=source, **kw)
def _set_as_mock(self, enable):
if not enable:
self._mock_server = None
return
logger.info(
"Deploying serving function MOCK (for simulation)...\n"
"Turn off the mock (mock=False) and make sure Nuclio is installed for real deployment to Nuclio"
)
self._mock_server = self.to_mock_server()
