# Copyright 2018 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 functools
import importlib
import importlib.util as imputil
import inspect
import json
import os
import pathlib
import re
import socket
import tempfile
import time
import uuid
import warnings
from base64 import b64decode
from collections import OrderedDict
from copy import deepcopy
from os import environ, makedirs, path
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Type, Union
import nuclio
import yaml
from deprecated import deprecated
from kfp import Client
import mlrun.api.schemas
import mlrun.errors
import mlrun.utils.helpers
from mlrun.kfpops import format_summary_from_kfp_run, show_kfp_run
from .config import config as mlconf
from .datastore import store_manager
from .db import get_or_set_dburl, get_run_db
from .execution import MLClientCtx
from .model import BaseMetadata, RunObject, RunTemplate
from .runtimes import (
DaskCluster,
HandlerRuntime,
KubejobRuntime,
LocalRuntime,
MpiRuntimeV1,
MpiRuntimeV1Alpha1,
RemoteRuntime,
RemoteSparkRuntime,
RuntimeKinds,
ServingRuntime,
Spark2Runtime,
Spark3Runtime,
get_runtime_class,
)
from .runtimes.funcdoc import update_function_entry_points
from .runtimes.package.context_handler import ArtifactType, ContextHandler
from .runtimes.serving import serving_subkind
from .runtimes.utils import add_code_metadata, global_context
from .utils import (
extend_hub_uri_if_needed,
get_in,
logger,
new_pipe_metadata,
parse_versioned_object_uri,
retry_until_successful,
run_keys,
update_in,
)
[docs]class RunStatuses(object):
succeeded = "Succeeded"
failed = "Failed"
skipped = "Skipped"
error = "Error"
running = "Running"
[docs] @staticmethod
def all():
return [
RunStatuses.succeeded,
RunStatuses.failed,
RunStatuses.skipped,
RunStatuses.error,
RunStatuses.running,
]
[docs] @staticmethod
def stable_statuses():
return [
RunStatuses.succeeded,
RunStatuses.failed,
RunStatuses.skipped,
RunStatuses.error,
]
[docs] @staticmethod
def transient_statuses():
return [
status
for status in RunStatuses.all()
if status not in RunStatuses.stable_statuses()
]
[docs]def run_local(
task=None,
command="",
name: str = "",
args: list = None,
workdir=None,
project: str = "",
tag: str = "",
secrets=None,
handler=None,
params: dict = None,
inputs: dict = None,
artifact_path: str = "",
mode: str = None,
allow_empty_resources=None,
returns: list = None,
):
"""Run a task on function/code (.py, .ipynb or .yaml) locally,
example::
# define a task
task = new_task(params={'p1': 8}, out_path=out_path)
# run
run = run_local(spec, command='src/training.py', workdir='src')
or specify base task parameters (handler, params, ..) in the call::
run = run_local(handler=my_function, params={'x': 5})
:param task: task template object or dict (see RunTemplate)
:param command: command/url/function
:param name: ad hook function name
:param args: command line arguments (override the ones in command)
:param workdir: working dir to exec in
:param project: function project (none for 'default')
:param tag: function version tag (none for 'latest')
:param secrets: secrets dict if the function source is remote (s3, v3io, ..)
:param handler: pointer or name of a function handler
:param params: input parameters (dict)
:param inputs: Input objects to pass to the handler. Type hints can be given so the input will be parsed
during runtime from `mlrun.DataItem` to the given type hint. The type hint can be given
in the key field of the dictionary after a colon, e.g: "<key> : <type_hint>".
:param artifact_path: default artifact output path
:param mode: Runtime mode for more details head to `mlrun.new_function`
:param allow_empty_resources: Allow passing non materialized set/vector as input to jobs
(allows to have function which don't depend on having targets,
e.g a function which accepts a feature vector uri and generate
the offline vector e.g. parquet_ for it if it doesn't exist)
:param returns: List of configurations for how to log the returning values from the handler's run (as artifacts or
results). The list's length must be equal to the amount of returning objects. A configuration may
be given as:
* A string of the key to use to log the returning value as result or as an artifact. To specify
The artifact type, it is possible to pass a string in the following structure:
"<key> : <type>". Available artifact types can be seen in `mlrun.ArtifactType`. If no artifact
type is specified, the object's default artifact type will be used.
* A dictionary of configurations to use when logging. Further info per object type and artifact
type can be given there. The artifact key must appear in the dictionary as "key": "the_key".
:return: run object
"""
function_name = name
if command and isinstance(command, str):
sp = command.split()
command = sp[0]
if len(sp) > 1:
args = args or []
args = sp[1:] + args
function_name = function_name or pathlib.Path(command).stem
meta = BaseMetadata(function_name, project=project, tag=tag)
command, runtime = load_func_code(command, workdir, secrets=secrets, name=name)
if runtime:
if task:
handler = handler or task.spec.handler
handler = handler or runtime.spec.default_handler or ""
meta = runtime.metadata.copy()
meta.project = project or meta.project
meta.tag = tag or meta.tag
# if the handler has module prefix force "local" (vs "handler") runtime
kind = "local" if isinstance(handler, str) and "." in handler else ""
fn = new_function(meta.name, command=command, args=args, mode=mode, kind=kind)
fn.metadata = meta
setattr(fn, "_is_run_local", True)
if workdir:
fn.spec.workdir = str(workdir)
fn.spec.allow_empty_resources = allow_empty_resources
if runtime:
# copy the code/base-spec to the local function (for the UI and code logging)
fn.spec.description = runtime.spec.description
fn.spec.build = runtime.spec.build
return fn.run(
task,
name=name,
handler=handler,
params=params,
inputs=inputs,
returns=returns,
artifact_path=artifact_path,
)
[docs]def function_to_module(code="", workdir=None, secrets=None, silent=False):
"""Load code, notebook or mlrun function as .py module
this function can import a local/remote py file or notebook
or load an mlrun function object as a module, you can use this
from your code, notebook, or another function (for common libs)
Note: the function may have package requirements which must be satisfied
example::
mod = mlrun.function_to_module('./examples/training.py')
task = mlrun.new_task(inputs={'infile.txt': '../examples/infile.txt'})
context = mlrun.get_or_create_ctx('myfunc', spec=task)
mod.my_job(context, p1=1, p2='x')
print(context.to_yaml())
fn = mlrun.import_function('hub://open_archive')
mod = mlrun.function_to_module(fn)
data = mlrun.run.get_dataitem("https://fpsignals-public.s3.amazonaws.com/catsndogs.tar.gz")
context = mlrun.get_or_create_ctx('myfunc')
mod.open_archive(context, archive_url=data)
print(context.to_yaml())
:param code: path/url to function (.py or .ipynb or .yaml)
OR function object
:param workdir: code workdir
:param secrets: secrets needed to access the URL (e.g.s3, v3io, ..)
:param silent: do not raise on errors
:returns: python module
"""
command, _ = load_func_code(code, workdir, secrets=secrets)
if not command:
if silent:
return None
raise ValueError("nothing to run, specify command or function")
command = os.path.join(workdir or "", command)
path = Path(command)
mod_name = path.name
if path.suffix:
mod_name = mod_name[: -len(path.suffix)]
spec = imputil.spec_from_file_location(mod_name, command)
if spec is None:
raise OSError(f"cannot import from {command!r}")
mod = imputil.module_from_spec(spec)
spec.loader.exec_module(mod)
return mod
[docs]def load_func_code(command="", workdir=None, secrets=None, name="name"):
is_obj = hasattr(command, "to_dict")
suffix = "" if is_obj else Path(command).suffix
runtime = None
if is_obj or suffix == ".yaml":
is_remote = False
if is_obj:
runtime = command
else:
is_remote = "://" in command
data = get_object(command, secrets)
runtime = yaml.load(data, Loader=yaml.FullLoader)
runtime = new_function(runtime=runtime)
command = runtime.spec.command or ""
code = runtime.spec.build.functionSourceCode
origin_filename = runtime.spec.build.origin_filename
kind = runtime.kind or ""
if kind in RuntimeKinds.nuclio_runtimes():
code = get_in(runtime.spec.base_spec, "spec.build.functionSourceCode", code)
if code:
if (
origin_filename
and origin_filename.endswith(".py")
and path.isfile(origin_filename)
):
command = origin_filename
else:
suffix = ".py"
if origin_filename:
suffix = f"-{pathlib.Path(origin_filename).stem}.py"
with tempfile.NamedTemporaryFile(
suffix=suffix, mode="w", delete=False
) as temp_file:
code = b64decode(code).decode("utf-8")
command = temp_file.name
temp_file.write(code)
elif command and not is_remote:
file_path = path.join(workdir or "", command)
if not path.isfile(file_path):
raise OSError(f"command file {file_path} not found")
else:
logger.warn("run command, file or code were not specified")
elif command == "":
pass
elif suffix == ".ipynb":
temp_file = tempfile.NamedTemporaryFile(suffix=".py", delete=False)
code_to_function(
name, filename=command, kind="local", code_output=temp_file.name
)
command = temp_file.name
elif suffix == ".py":
if "://" in command:
temp_file = tempfile.NamedTemporaryFile(suffix=".py", delete=False)
download_object(command, temp_file.name, secrets)
command = temp_file.name
else:
raise ValueError(f"unsupported suffix: {suffix}")
return command, runtime
[docs]def get_or_create_ctx(
name: str,
event=None,
spec=None,
with_env: bool = True,
rundb: str = "",
project: str = "",
upload_artifacts=False,
):
"""called from within the user program to obtain a run context
the run context is an interface for receiving parameters, data and logging
run results, the run context is read from the event, spec, or environment
(in that order), user can also work without a context (local defaults mode)
all results are automatically stored in the "rundb" or artifact store,
the path to the rundb can be specified in the call or obtained from env.
:param name: run name (will be overridden by context)
:param event: function (nuclio Event object)
:param spec: dictionary holding run spec
:param with_env: look for context in environment vars, default True
:param rundb: path/url to the metadata and artifact database
:param project: project to initiate the context in (by default mlrun.mlctx.default_project)
:param upload_artifacts: when using local context (not as part of a job/run), upload artifacts to the
system default artifact path location
:return: execution context
Examples::
# load MLRUN runtime context (will be set by the runtime framework e.g. KubeFlow)
context = get_or_create_ctx('train')
# get parameters from the runtime context (or use defaults)
p1 = context.get_param('p1', 1)
p2 = context.get_param('p2', 'a-string')
# access input metadata, values, files, and secrets (passwords)
print(f'Run: {context.name} (uid={context.uid})')
print(f'Params: p1={p1}, p2={p2}')
print(f'accesskey = {context.get_secret("ACCESS_KEY")}')
input_str = context.get_input('infile.txt').get()
print(f'file: {input_str}')
# RUN some useful code e.g. ML training, data prep, etc.
# log scalar result values (job result metrics)
context.log_result('accuracy', p1 * 2)
context.log_result('loss', p1 * 3)
context.set_label('framework', 'sklearn')
# log various types of artifacts (file, web page, table), will be versioned and visible in the UI
context.log_artifact('model.txt', body=b'abc is 123', labels={'framework': 'xgboost'})
context.log_artifact('results.html', body=b'<b> Some HTML <b>', viewer='web-app')
"""
if global_context.get() and not spec and not event:
return global_context.get()
newspec = {}
config = environ.get("MLRUN_EXEC_CONFIG")
if event:
newspec = event.body
elif spec:
newspec = deepcopy(spec)
elif with_env and config:
newspec = config
if isinstance(newspec, (RunObject, RunTemplate)):
newspec = newspec.to_dict()
if newspec and not isinstance(newspec, dict):
newspec = json.loads(newspec)
if not newspec:
newspec = {}
if upload_artifacts:
artifact_path = mlrun.utils.helpers.fill_artifact_path_template(
mlconf.artifact_path, project or mlconf.default_project
)
update_in(newspec, ["spec", run_keys.output_path], artifact_path)
newspec.setdefault("metadata", {})
update_in(newspec, "metadata.name", name, replace=False)
autocommit = False
tmp = environ.get("MLRUN_META_TMPFILE")
out = rundb or mlconf.dbpath or environ.get("MLRUN_DBPATH")
if out:
autocommit = True
logger.info(f"logging run results to: {out}")
newspec["metadata"]["project"] = (
newspec["metadata"].get("project") or project or mlconf.default_project
)
ctx = MLClientCtx.from_dict(
newspec, rundb=out, autocommit=autocommit, tmp=tmp, host=socket.gethostname()
)
global_context.set(ctx)
return ctx
[docs]def import_function(url="", secrets=None, db="", project=None, new_name=None):
"""Create function object from DB or local/remote YAML file
Functions can be imported from function repositories (mlrun Function Hub (formerly Marketplace) or local db),
or be read from a remote URL (http(s), s3, git, v3io, ..) containing the function YAML
special URLs::
function hub: hub://{name}[:{tag}]
local mlrun db: db://{project-name}/{name}[:{tag}]
examples::
function = mlrun.import_function("hub://auto_trainer")
function = mlrun.import_function("./func.yaml")
function = mlrun.import_function("https://raw.githubusercontent.com/org/repo/func.yaml")
:param url: path/url to Function Hub, db or function YAML file
:param secrets: optional, credentials dict for DB or URL (s3, v3io, ...)
:param db: optional, mlrun api/db path
:param project: optional, target project for the function
:param new_name: optional, override the imported function name
:returns: function object
"""
is_hub_uri = False
if url.startswith("db://"):
url = url[5:]
_project, name, tag, hash_key = parse_versioned_object_uri(url)
db = get_run_db(db or get_or_set_dburl(), secrets=secrets)
runtime = db.get_function(name, _project, tag, hash_key)
if not runtime:
raise KeyError(f"function {name}:{tag} not found in the DB")
else:
url, is_hub_uri = extend_hub_uri_if_needed(url)
runtime = import_function_to_dict(url, secrets)
function = new_function(runtime=runtime)
project = project or mlrun.mlconf.default_project
# When we're importing from the hub we want to assign to a target project, otherwise any store on it will
# simply default to the default project
if project and is_hub_uri:
function.metadata.project = project
if new_name:
function.metadata.name = mlrun.utils.helpers.normalize_name(new_name)
return function
[docs]def import_function_to_dict(url, secrets=None):
"""Load function spec from local/remote YAML file"""
obj = get_object(url, secrets)
runtime = yaml.load(obj, Loader=yaml.FullLoader)
remote = "://" in url
code = get_in(runtime, "spec.build.functionSourceCode")
update_in(runtime, "metadata.build.code_origin", url)
cmd = code_file = get_in(runtime, "spec.command", "")
if " " in cmd:
code_file = cmd[: cmd.find(" ")]
if runtime["kind"] in ["", "local"]:
if code:
with tempfile.NamedTemporaryFile(
suffix=".py", mode="w", delete=False
) as temp_file:
code = b64decode(code).decode("utf-8")
update_in(runtime, "spec.command", temp_file.name)
temp_file.write(code)
elif remote and cmd:
if cmd.startswith("/"):
raise ValueError("exec path (spec.command) must be relative")
url = url[: url.rfind("/") + 1] + code_file
code = get_object(url, secrets)
dir = path.dirname(code_file)
if dir:
makedirs(dir, exist_ok=True)
with open(code_file, "wb") as fp:
fp.write(code)
elif cmd:
if not path.isfile(code_file):
# look for the file in a relative path to the yaml
slash = url.rfind("/")
if slash >= 0 and path.isfile(url[: url.rfind("/") + 1] + code_file):
raise ValueError(
f"exec file spec.command={code_file} is relative, change working dir"
)
raise ValueError(f"no file in exec path (spec.command={code_file})")
else:
raise ValueError("command or code not specified in function spec")
return runtime
[docs]def new_function(
name: str = "",
project: str = "",
tag: str = "",
kind: str = "",
command: str = "",
image: str = "",
args: list = None,
runtime=None,
mode=None,
handler: str = None,
source: str = None,
requirements: Union[str, List[str]] = None,
kfp=None,
):
"""Create a new ML function from base properties
example::
# define a container based function (the `training.py` must exist in the container workdir)
f = new_function(command='training.py -x {x}', image='myrepo/image:latest', kind='job')
f.run(params={"x": 5})
# define a container based function which reads its source from a git archive
f = new_function(command='training.py -x {x}', image='myrepo/image:latest', kind='job',
source='git://github.com/mlrun/something.git')
f.run(params={"x": 5})
# define a local handler function (execute a local function handler)
f = new_function().run(task, handler=myfunction)
:param name: function name
:param project: function project (none for 'default')
:param tag: function version tag (none for 'latest')
:param kind: runtime type (local, job, nuclio, spark, mpijob, dask, ..)
:param command: command/url + args (e.g.: training.py --verbose)
:param image: container image (start with '.' for default registry)
:param args: command line arguments (override the ones in command)
:param runtime: runtime (job, nuclio, spark, dask ..) object/dict
store runtime specific details and preferences
:param mode: runtime mode, "args" mode will push params into command template, example:
command=`mycode.py --x {xparam}` will substitute the `{xparam}` with the value of the xparam param
"pass" mode will run the command as is in the container (not wrapped by mlrun), the command can use
`{}` for parameters like in the "args" mode
:param handler: The default function handler to call for the job or nuclio function, in batch functions
(job, mpijob, ..) the handler can also be specified in the `.run()` command, when not specified
the entire file will be executed (as main).
for nuclio functions the handler is in the form of module:function, defaults to "main:handler"
:param source: valid path to git, zip, or tar file, e.g. `git://github.com/mlrun/something.git`,
`http://some/url/file.zip`
:param requirements: list of python packages or pip requirements file path, defaults to None
:param kfp: reserved, flag indicating running within kubeflow pipeline
:return: function object
"""
# don't override given dict
if runtime and isinstance(runtime, dict):
runtime = deepcopy(runtime)
kind, runtime = _process_runtime(command, runtime, kind)
command = get_in(runtime, "spec.command", command)
name = name or get_in(runtime, "metadata.name", "")
if not kind and not command:
runner = HandlerRuntime()
else:
if kind in ["", "local"] and command:
runner = LocalRuntime.from_dict(runtime)
elif kind in RuntimeKinds.all():
runner = get_runtime_class(kind).from_dict(runtime)
else:
supported_runtimes = ",".join(RuntimeKinds.all())
raise Exception(
f"unsupported runtime ({kind}) or missing command, supported runtimes: {supported_runtimes}"
)
if not name:
if command and kind not in [RuntimeKinds.remote]:
name, _ = path.splitext(path.basename(command))
else:
name = "mlrun-" + uuid.uuid4().hex[0:6]
# make sure function name is valid
name = mlrun.utils.helpers.normalize_name(name)
runner.metadata.name = name
runner.metadata.project = (
runner.metadata.project or project or mlconf.default_project
)
if tag:
runner.metadata.tag = tag
if image:
if kind in ["", "handler", "local"]:
raise ValueError(
"image should only be set with containerized "
"runtimes (job, mpijob, spark, ..), set kind=.."
)
runner.spec.image = image
if args:
runner.spec.args = args
runner.kfp = kfp
if mode:
runner.spec.mode = mode
if source:
runner.spec.build.source = source
if handler:
if kind == RuntimeKinds.serving:
raise mlrun.errors.MLRunInvalidArgumentError(
"cannot set the handler for serving runtime"
)
elif kind in RuntimeKinds.nuclio_runtimes():
runner.spec.function_handler = handler
else:
runner.spec.default_handler = handler
if requirements:
runner.with_requirements(requirements)
runner.verify_base_image()
return runner
def _process_runtime(command, runtime, kind):
if runtime and hasattr(runtime, "to_dict"):
runtime = runtime.to_dict()
if runtime and isinstance(runtime, dict):
kind = kind or runtime.get("kind", "")
command = command or get_in(runtime, "spec.command", "")
if "://" in command and command.startswith("http"):
kind = kind or RuntimeKinds.remote
if not runtime:
runtime = {}
update_in(runtime, "spec.command", command)
runtime["kind"] = kind
if kind != RuntimeKinds.remote:
if command:
update_in(runtime, "spec.command", command)
else:
update_in(runtime, "spec.function_kind", "mlrun")
return kind, runtime
[docs]def code_to_function(
name: str = "",
project: str = "",
tag: str = "",
filename: str = "",
handler: str = "",
kind: str = "",
image: str = None,
code_output: str = "",
embed_code: bool = True,
description: str = "",
requirements: Union[str, List[str]] = None,
categories: List[str] = None,
labels: Dict[str, str] = None,
with_doc: bool = True,
ignored_tags=None,
) -> Union[
MpiRuntimeV1Alpha1,
MpiRuntimeV1,
RemoteRuntime,
ServingRuntime,
DaskCluster,
KubejobRuntime,
LocalRuntime,
Spark2Runtime,
Spark3Runtime,
RemoteSparkRuntime,
]:
"""Convenience function to insert code and configure an mlrun runtime.
Easiest way to construct a runtime type object. Provides the most often
used configuration options for all runtimes as parameters.
Instantiated runtimes are considered 'functions' in mlrun, but they are
anything from nuclio functions to generic kubernetes pods to spark jobs.
Functions are meant to be focused, and as such limited in scope and size.
Typically a function can be expressed in a single python module with
added support from custom docker images and commands for the environment.
The returned runtime object can be further configured if more
customization is required.
One of the most important parameters is 'kind'. This is what is used to
specify the chosen runtimes. The options are:
- local: execute a local python or shell script
- job: insert the code into a Kubernetes pod and execute it
- nuclio: insert the code into a real-time serverless nuclio function
- serving: insert code into orchestrated nuclio function(s) forming a DAG
- dask: run the specified python code / script as Dask Distributed job
- mpijob: run distributed Horovod jobs over the MPI job operator
- spark: run distributed Spark job using Spark Kubernetes Operator
- remote-spark: run distributed Spark job on remote Spark service
Learn more about function runtimes here:
https://docs.mlrun.org/en/latest/runtimes/functions.html#function-runtimes
:param name: function name, typically best to use hyphen-case
:param project: project used to namespace the function, defaults to 'default'
:param tag: function tag to track multiple versions of the same function, defaults to 'latest'
:param filename: path to .py/.ipynb file, defaults to current jupyter notebook
:param handler: The default function handler to call for the job or nuclio function, in batch functions
(job, mpijob, ..) the handler can also be specified in the `.run()` command, when not specified
the entire file will be executed (as main).
for nuclio functions the handler is in the form of module:function, defaults to 'main:handler'
:param kind: function runtime type string - nuclio, job, etc. (see docstring for all options)
:param image: base docker image to use for building the function container, defaults to None
:param code_output: specify '.' to generate python module from the current jupyter notebook
:param embed_code: indicates whether or not to inject the code directly into the function runtime spec,
defaults to True
:param description: short function description, defaults to ''
:param requirements: list of python packages or pip requirements file path, defaults to None
:param categories: list of categories for mlrun Function Hub, defaults to None
:param labels: immutable name/value pairs to tag the function with useful metadata, defaults to None
:param with_doc: indicates whether to document the function parameters, defaults to True
:param ignored_tags: notebook cells to ignore when converting notebooks to py code (separated by ';')
:return:
pre-configured function object from a mlrun runtime class
example::
import mlrun
# create job function object from notebook code and add doc/metadata
fn = mlrun.code_to_function("file_utils", kind="job",
handler="open_archive", image="mlrun/mlrun",
description = "this function opens a zip archive into a local/mounted folder",
categories = ["fileutils"],
labels = {"author": "me"})
example::
import mlrun
from pathlib import Path
# create file
Path("mover.py").touch()
# create nuclio function object from python module call mover.py
fn = mlrun.code_to_function("nuclio-mover", kind="nuclio",
filename="mover.py", image="python:3.7",
description = "this function moves files from one system to another",
requirements = ["pandas"],
labels = {"author": "me"})
"""
filebase, _ = path.splitext(path.basename(filename))
ignored_tags = ignored_tags or mlconf.ignored_notebook_tags
def add_name(origin, name=""):
name = filename or (name + ".ipynb")
if not origin:
return name
return f"{origin}:{name}"
def update_common(fn, spec):
fn.spec.image = image or get_in(spec, "spec.image", "")
fn.spec.build.base_image = get_in(spec, "spec.build.baseImage")
fn.spec.build.commands = get_in(spec, "spec.build.commands")
fn.spec.build.secret = get_in(spec, "spec.build.secret")
if requirements:
fn.with_requirements(requirements)
if embed_code:
fn.spec.build.functionSourceCode = get_in(
spec, "spec.build.functionSourceCode"
)
if fn.kind != "local":
fn.spec.env = get_in(spec, "spec.env")
for vol in get_in(spec, "spec.volumes", []):
fn.spec.volumes.append(vol.get("volume"))
fn.spec.volume_mounts.append(vol.get("volumeMount"))
fn.spec.description = description
fn.metadata.project = project or mlconf.default_project
fn.metadata.tag = tag
fn.metadata.categories = categories
fn.metadata.labels = labels or fn.metadata.labels
def resolve_nuclio_subkind(kind):
is_nuclio = kind.startswith("nuclio")
subkind = kind[kind.find(":") + 1 :] if is_nuclio and ":" in kind else None
if kind == RuntimeKinds.serving:
is_nuclio = True
subkind = serving_subkind
return is_nuclio, subkind
if (
not embed_code
and not code_output
and (not filename or filename.endswith(".ipynb"))
):
raise ValueError(
"a valid code file must be specified "
"when not using the embed_code option"
)
is_nuclio, subkind = resolve_nuclio_subkind(kind)
code_origin = add_name(add_code_metadata(filename), name)
name, spec, code = nuclio.build_file(
filename,
name=name,
handler=handler or "handler",
kind=subkind,
ignored_tags=ignored_tags,
)
spec_kind = get_in(spec, "kind", "")
if not kind and spec_kind not in ["", "Function"]:
kind = spec_kind.lower()
# if its a nuclio subkind, redo nb parsing
is_nuclio, subkind = resolve_nuclio_subkind(kind)
if is_nuclio:
name, spec, code = nuclio.build_file(
filename,
name=name,
handler=handler or "handler",
kind=subkind,
ignored_tags=ignored_tags,
)
if code_output:
if code_output == ".":
code_output = name + ".py"
if filename == "" or filename.endswith(".ipynb"):
with open(code_output, "w") as fp:
fp.write(code)
else:
raise ValueError("code_output option is only used with notebooks")
if is_nuclio:
if subkind == serving_subkind:
r = ServingRuntime()
else:
r = RemoteRuntime()
r.spec.function_kind = subkind
# default_handler is only used in :mlrun subkind, determine the handler to invoke in function.run()
r.spec.default_handler = handler if subkind == "mlrun" else ""
r.spec.function_handler = (
handler if handler and ":" in handler else get_in(spec, "spec.handler")
)
if not embed_code:
r.spec.source = filename
nuclio_runtime = get_in(spec, "spec.runtime")
if nuclio_runtime and not nuclio_runtime.startswith("py"):
r.spec.nuclio_runtime = nuclio_runtime
if not name:
raise ValueError("name must be specified")
r.metadata.name = name
r.spec.build.code_origin = code_origin
r.spec.build.origin_filename = filename or (name + ".ipynb")
update_common(r, spec)
return r
if kind is None or kind in ["", "Function"]:
raise ValueError("please specify the function kind")
elif kind in RuntimeKinds.all():
r = get_runtime_class(kind)()
else:
raise ValueError(f"unsupported runtime ({kind})")
name, spec, code = nuclio.build_file(filename, name=name, ignored_tags=ignored_tags)
if not name:
raise ValueError("name must be specified")
h = get_in(spec, "spec.handler", "").split(":")
r.handler = h[0] if len(h) <= 1 else h[1]
r.metadata = get_in(spec, "spec.metadata")
r.metadata.name = name
build = r.spec.build
build.code_origin = code_origin
build.origin_filename = filename or (name + ".ipynb")
build.extra = get_in(spec, "spec.build.extra")
if not embed_code:
if code_output:
r.spec.command = code_output
else:
r.spec.command = filename
build.image = get_in(spec, "spec.build.image")
update_common(r, spec)
r.verify_base_image()
if with_doc:
update_function_entry_points(r, code)
r.spec.default_handler = handler
return r
[docs]@deprecated(
version="1.3.0",
reason="'run_pipeline' will be removed in 1.5.0, use 'project.run' instead",
category=FutureWarning,
)
def run_pipeline(
pipeline,
arguments=None,
project=None,
experiment=None,
run=None,
namespace=None,
artifact_path=None,
ops=None,
url=None,
# TODO: deprecated, remove in 1.5.0
ttl=None,
remote: bool = True,
cleanup_ttl=None,
):
"""
remote KubeFlow pipeline execution
Submit a workflow task to KFP via mlrun API service
:param pipeline: KFP pipeline function or path to .yaml/.zip pipeline file
:param arguments: pipeline arguments
:param project: name of project
:param experiment: experiment name
:param run: optional, run name
:param namespace: Kubernetes namespace (if not using default)
:param url: optional, url to mlrun API service
:param artifact_path: target location/url for mlrun artifacts
:param ops: additional operators (.apply() to all pipeline functions)
:param ttl: pipeline cleanup ttl in secs (time to wait after workflow completion, at which point the
workflow and all its resources are deleted) (deprecated, use cleanup_ttl instead)
:param remote: read kfp data from mlrun service (default=True). Run pipeline from local kfp data (remote=False)
is deprecated. Should not be used
:param cleanup_ttl:
pipeline cleanup ttl in secs (time to wait after workflow completion, at which point the
workflow and all its resources are deleted)
:returns: kubeflow pipeline id
"""
if ttl:
warnings.warn(
"'ttl' is deprecated, use 'cleanup_ttl' instead. "
"This will be removed in 1.5.0",
# TODO: Remove this in 1.5.0
FutureWarning,
)
artifact_path = artifact_path or mlconf.artifact_path
project = project or mlconf.default_project
artifact_path = mlrun.utils.helpers.fill_artifact_path_template(
artifact_path, project or mlconf.default_project
)
if artifact_path and "{{run.uid}}" in artifact_path:
artifact_path.replace("{{run.uid}}", "{{workflow.uid}}")
if not artifact_path:
raise ValueError("artifact path was not specified")
namespace = namespace or mlconf.namespace
arguments = arguments or {}
if remote or url:
from .projects.pipelines import WorkflowSpec, pipeline_context
clear_pipeline_context = False
# if pipeline_context.workflow isn't set it means the `run_pipeline` method was called directly
# so to make sure the pipeline and functions inside are being run in the KFP pipeline we set the pipeline
# context with KFP engine
if not pipeline_context.workflow:
workflow_spec = WorkflowSpec(engine="kfp")
pipeline_context.set(pipeline_context.project, workflow=workflow_spec)
clear_pipeline_context = True
pipeline_run_id = _run_pipeline(
pipeline=pipeline,
arguments=arguments,
project=project,
experiment=experiment,
run=run,
namespace=namespace,
artifact_path=artifact_path,
ops=ops,
url=url,
cleanup_ttl=cleanup_ttl or ttl,
)
if clear_pipeline_context:
pipeline_context.clear()
# this shouldn't be used, keeping for backwards compatibility until the entire method is deprecated
else:
client = Client(namespace=namespace)
if isinstance(pipeline, str):
experiment = client.create_experiment(name=experiment)
run_result = client.run_pipeline(
experiment.id, run, pipeline, params=arguments
)
else:
conf = new_pipe_metadata(
artifact_path=artifact_path, cleanup_ttl=ttl, op_transformers=ops
)
run_result = client.create_run_from_pipeline_func(
pipeline,
arguments,
run_name=run,
experiment_name=experiment,
pipeline_conf=conf,
)
pipeline_run_id = run_result.run_id
logger.info(f"Pipeline run id={id}, check UI for progress")
return pipeline_run_id
def _run_pipeline(
pipeline,
arguments=None,
project=None,
experiment=None,
run=None,
namespace=None,
artifact_path=None,
ops=None,
url=None,
cleanup_ttl=None,
):
"""remote KubeFlow pipeline execution
Submit a workflow task to KFP via mlrun API service
:param pipeline: KFP pipeline function or path to .yaml/.zip pipeline file
:param arguments: pipeline arguments
:param project: name of project
:param experiment: experiment name
:param run: optional, run name
:param namespace: Kubernetes namespace (if not using default)
:param url: optional, url to mlrun API service
:param artifact_path: target location/url for mlrun artifacts
:param ops: additional operators (.apply() to all pipeline functions)
:param cleanup_ttl:
pipeline cleanup ttl in secs (time to wait after workflow completion, at which point the
workflow and all its resources are deleted)
:returns: kubeflow pipeline id
"""
mldb = mlrun.db.get_run_db(url)
if mldb.kind != "http":
raise ValueError(
"run pipeline require access to remote api-service"
", please set the dbpath url"
)
pipeline_run_id = mldb.submit_pipeline(
project,
pipeline,
arguments,
experiment=experiment,
run=run,
namespace=namespace,
ops=ops,
artifact_path=artifact_path,
cleanup_ttl=cleanup_ttl,
)
logger.info(f"Pipeline run id={pipeline_run_id}, check UI for progress")
return pipeline_run_id
[docs]def wait_for_pipeline_completion(
run_id,
timeout=60 * 60,
expected_statuses: List[str] = None,
namespace=None,
remote=True,
project: str = None,
):
"""Wait for Pipeline status, timeout in sec
:param run_id: id of pipelines run
:param timeout: wait timeout in sec
:param expected_statuses: list of expected statuses, one of [ Succeeded | Failed | Skipped | Error ], by default
[ Succeeded ]
:param namespace: k8s namespace if not default
:param remote: read kfp data from mlrun service (default=True)
:param project: the project of the pipeline
:return: kfp run dict
"""
if expected_statuses is None:
expected_statuses = [RunStatuses.succeeded]
namespace = namespace or mlconf.namespace
logger.debug(
f"Waiting for run completion."
f" run_id: {run_id},"
f" project: {project},"
f" expected_statuses: {expected_statuses},"
f" timeout: {timeout},"
f" remote: {remote},"
f" namespace: {namespace}"
)
if remote:
mldb = mlrun.db.get_run_db()
def get_pipeline_if_completed(run_id, namespace=namespace):
resp = mldb.get_pipeline(run_id, namespace=namespace, project=project)
status = resp["run"]["status"]
show_kfp_run(resp, clear_output=True)
if status not in RunStatuses.stable_statuses():
# TODO: think of nicer liveness indication and make it re-usable
# log '.' each retry as a liveness indication
logger.debug(".")
raise RuntimeError("pipeline run has not completed yet")
return resp
if mldb.kind != "http":
raise ValueError(
"get pipeline require access to remote api-service"
", please set the dbpath url"
)
resp = retry_until_successful(
10,
timeout,
logger,
False,
get_pipeline_if_completed,
run_id,
namespace=namespace,
)
else:
client = Client(namespace=namespace)
resp = client.wait_for_run_completion(run_id, timeout)
if resp:
resp = resp.to_dict()
resp = format_summary_from_kfp_run(resp)
show_kfp_run(resp)
status = resp["run"]["status"] if resp else "unknown"
message = resp["run"].get("message", "")
if expected_statuses:
if status not in expected_statuses:
raise RuntimeError(
f"Pipeline run status {status}{', ' + message if message else ''}"
)
logger.debug(
f"Finished waiting for pipeline completion."
f" run_id: {run_id},"
f" status: {status},"
f" message: {message},"
f" namespace: {namespace}"
)
return resp
[docs]def get_pipeline(
run_id,
namespace=None,
format_: Union[
str, mlrun.api.schemas.PipelinesFormat
] = mlrun.api.schemas.PipelinesFormat.summary,
project: str = None,
remote: bool = True,
):
"""Get Pipeline status
:param run_id: id of pipelines run
:param namespace: k8s namespace if not default
:param format_: Format of the results. Possible values are:
- ``summary`` (default value) - Return summary of the object data.
- ``full`` - Return full pipeline object.
:param project: the project of the pipeline run
:param remote: read kfp data from mlrun service (default=True)
:return: kfp run dict
"""
namespace = namespace or mlconf.namespace
if remote:
mldb = mlrun.db.get_run_db()
if mldb.kind != "http":
raise ValueError(
"get pipeline require access to remote api-service"
", please set the dbpath url"
)
resp = mldb.get_pipeline(
run_id, namespace=namespace, format_=format_, project=project
)
else:
client = Client(namespace=namespace)
resp = client.get_run(run_id)
if resp:
resp = resp.to_dict()
if (
not format_
or format_ == mlrun.api.schemas.PipelinesFormat.summary.value
):
resp = format_summary_from_kfp_run(resp)
show_kfp_run(resp)
return resp
[docs]def list_pipelines(
full=False,
page_token="",
page_size=None,
sort_by="",
filter_="",
namespace=None,
project="*",
format_: mlrun.api.schemas.PipelinesFormat = mlrun.api.schemas.PipelinesFormat.metadata_only,
) -> Tuple[int, Optional[int], List[dict]]:
"""List pipelines
:param full: Deprecated, use `format_` instead. if True will set `format_` to full, otherwise `format_` will
be used
:param page_token: A page token to request the next page of results. The token is acquired from the nextPageToken
field of the response from the previous call or can be omitted when fetching the first page.
:param page_size: The number of pipelines to be listed per page. If there are more pipelines than this number, the
response message will contain a nextPageToken field you can use to fetch the next page.
:param sort_by: Can be format of "field_name", "field_name asc" or "field_name desc" (Example, "name asc"
or "id desc"). Ascending by default.
:param filter_: A url-encoded, JSON-serialized Filter protocol buffer, see:
[filter.proto](https://github.com/kubeflow/pipelines/ blob/master/backend/api/filter.proto).
:param namespace: Kubernetes namespace if other than default
:param project: Can be used to retrieve only specific project pipelines. "*" for all projects. Note that
filtering by project can't be used together with pagination, sorting, or custom filter.
:param format_: Control what will be returned (full/metadata_only/name_only)
"""
if full:
format_ = mlrun.api.schemas.PipelinesFormat.full
run_db = mlrun.db.get_run_db()
pipelines = run_db.list_pipelines(
project, namespace, sort_by, page_token, filter_, format_, page_size
)
return pipelines.total_size, pipelines.next_page_token, pipelines.runs
[docs]def get_object(url, secrets=None, size=None, offset=0, db=None):
"""get mlrun dataitem body (from path/url)"""
stores = store_manager.set(secrets, db=db)
return stores.object(url=url).get(size, offset)
[docs]def get_dataitem(url, secrets=None, db=None) -> mlrun.datastore.DataItem:
"""get mlrun dataitem object (from path/url)"""
stores = store_manager.set(secrets, db=db)
return stores.object(url=url)
[docs]def download_object(url, target, secrets=None):
"""download mlrun dataitem (from path/url to target path)"""
stores = store_manager.set(secrets)
stores.object(url=url).download(target_path=target)
[docs]def wait_for_runs_completion(runs: list, sleep=3, timeout=0, silent=False):
"""wait for multiple runs to complete
Note: need to use `watch=False` in `.run()` so the run will not wait for completion
example::
# run two training functions in parallel and wait for the results
inputs = {'dataset': cleaned_data}
run1 = train.run(name='train_lr', inputs=inputs, watch=False,
params={'model_pkg_class': 'sklearn.linear_model.LogisticRegression',
'label_column': 'label'})
run2 = train.run(name='train_lr', inputs=inputs, watch=False,
params={'model_pkg_class': 'sklearn.ensemble.RandomForestClassifier',
'label_column': 'label'})
completed = wait_for_runs_completion([run1, run2])
:param runs: list of run objects (the returned values of function.run())
:param sleep: time to sleep between checks (in seconds)
:param timeout: maximum time to wait in seconds (0 for unlimited)
:param silent: set to True for silent exit on timeout
:return: list of completed runs
"""
completed = []
total_time = 0
while True:
running = []
for run in runs:
state = run.state()
if state in mlrun.runtimes.constants.RunStates.terminal_states():
completed.append(run)
else:
running.append(run)
if len(running) == 0:
break
time.sleep(sleep)
total_time += sleep
if timeout and total_time > timeout:
if silent:
break
raise mlrun.errors.MLRunTimeoutError(
"some runs did not reach terminal state on time"
)
runs = running
return completed
def _parse_type_hint(type_hint: Union[Type, str]) -> Type:
"""
Parse a given type hint from string to its actual hinted type class object. The string must be one of the following:
* Python builtin type - one of ``tuple``, ``list``, ``set``, ``dict`` and ``bytearray``.
* Full module import path. An alias is not allowed (if ``import pandas as pd`` is used, the type hint cannot be
``pd.DataFrame`` but ``pandas.DataFrame``).
The type class on its own (like `DataFrame`) cannot be used as the scope of the decorator is not the same as the
handler itself, hence modules and objects that were imported in the handler's scope are not available. This is the
same reason import aliases cannot be used as well.
If the provided type hint is not a string, it will simply be returned as is.
**Notice**: This method should only run on client side as it dependent on user requirements.
:param type_hint: The type hint to parse.
:return: The hinted type.
:raise MLRunInvalidArgumentError: In case the type hint is not following the 2 options mentioned above.
"""
if not isinstance(type_hint, str):
return type_hint
# TODO: Remove once Packager is implemented (it will support typing hints)
# If a typing hint is provided, we return a dummy Union type so the parser will skip the data item:
if type_hint.startswith("typing."):
return Union[int, str]
# Validate the type hint is a valid module path:
if not bool(
re.fullmatch(r"([a-zA-Z_][a-zA-Z0-9_]*\.)*[a-zA-Z_][a-zA-Z0-9_]*", type_hint)
):
raise mlrun.errors.MLRunInvalidArgumentError(
f"Invalid type hint. An input type hint must be a valid python class name or its module import path. For "
f"example: 'list', 'pandas.DataFrame', 'numpy.ndarray', 'sklearn.linear_model.LinearRegression'. Type hint "
f"given: '{type_hint}'."
)
# Look for a builtin type (rest of the builtin types like `int`, `str`, `float` should be treated as results, hence
# not given as an input to an MLRun function, but as a parameter):
builtin_types = {
tuple.__name__: tuple,
list.__name__: list,
set.__name__: set,
dict.__name__: dict,
bytearray.__name__: bytearray,
}
if type_hint in builtin_types:
return builtin_types[type_hint]
# If it's not a builtin, its should have a full module path:
if "." not in type_hint:
raise mlrun.errors.MLRunInvalidArgumentError(
f"MLRun tried to get the type hint '{type_hint}' but it can't as it is not a valid builtin Python type "
f"(one of {', '.join(list(builtin_types.keys()))}). Pay attention using only the type as string is not "
f"allowed as the handler's scope is different then MLRun's. To properly give a type hint, please specify "
f"the full module path. For example: do not use `DataFrame`, use `pandas.DataFrame`."
)
# Import the module to receive the hinted type:
try:
# Get the module path and the type class (If we'll wish to support inner classes, the `rsplit` won't work):
module_path, type_hint = type_hint.rsplit(".", 1)
# Replace alias if needed (alias assumed to be imported already, hence we look in globals):
# For example:
# If in handler scope there was `import A.B.C as abc` and user gave a type hint "abc.Something" then:
# `module_path[0]` will be equal to "abc". Then, because it is an alias, it will appear in the globals, so we'll
# replace the alias with the full module name in order to import the module.
module_path = module_path.split(".")
if module_path[0] in globals():
module_path[0] = globals()[module_path[0]].__name__
module_path = ".".join(module_path)
# Import the module:
module = importlib.import_module(module_path)
# Get the class type from the module:
type_hint = getattr(module, type_hint)
except ModuleNotFoundError as module_not_found_error:
# May be raised from `importlib.import_module` in case the module does not exist.
raise mlrun.errors.MLRunInvalidArgumentError(
f"MLRun tried to get the type hint '{type_hint}' but the module '{module_path}' cannot be imported. "
f"Keep in mind that using alias in the module path (meaning: import module as alias) is not allowed. "
f"If the module path is correct, please make sure the module package is installed in the python "
f"interpreter."
) from module_not_found_error
except AttributeError as attribute_error:
# May be raised from `getattr(module, type_hint)` in case the class type cannot be imported directly from the
# imported module.
raise mlrun.errors.MLRunInvalidArgumentError(
f"MLRun tried to get the type hint '{type_hint}' from the module '{module.__name__}' but it seems it "
f"doesn't exist. Make sure the class can be imported from the module with the exact module path you "
f"passed. Notice inner classes (a class inside of a class) are not supported."
) from attribute_error
return type_hint
def _parse_log_hint(
log_hint: Union[Dict[str, str], str, None]
) -> Union[Dict[str, str], None]:
"""
Parse a given log hint from string to a logging configuration dictionary. The string will be read as the artifact
key ('key' in the dictionary) and if the string have a single colon, the following structure is assumed:
"<artifact_key> : <artifact_type>". The artifact type must be on of the values of `ArtifactType`'s enum.
If a logging configuration dictionary is received, it will be validated to have a key field and valid artifact type
value.
None will be returned as None.
:param log_hint: The log hint to parse.
:return: The hinted logging configuration.
:raise MLRunInvalidArgumentError: In case the log hint is not following the string structure, the artifact type is
not valid or the dictionary is missing the key field.
"""
# Check for None value:
if log_hint is None:
return None
# If the log hint was provided as a string, construct a dictionary out of it:
if isinstance(log_hint, str):
# Check if only key is given:
if ":" not in log_hint:
log_hint = {"key": log_hint}
# Check for valid "<key> : <artifact type>" pattern:
else:
if log_hint.count(":") > 1:
raise mlrun.errors.MLRunInvalidArgumentError(
f"Incorrect log hint pattern. Output keys can have only a single ':' in them to specify the "
f"desired artifact type the returned value will be logged as: '<artifact_key> : <artifact_type>', "
f"but given: {log_hint}"
)
# Split into key and type:
key, artifact_type = log_hint.replace(" ", "").split(":")
log_hint = {"key": key, "artifact_type": artifact_type}
# TODO: Replace with constants keys once mlrun.package is implemented.
# Validate the log hint dictionary has the mandatory key:
if "key" not in log_hint:
raise mlrun.errors.MLRunInvalidArgumentError(
f"An output log hint dictionary must include the 'key' - the artifact key (it's name). The following "
f"log hint is missing the key: {log_hint}."
)
# Validate the artifact type is valid:
if "artifact_type" in log_hint:
valid_artifact_types = [t.value for t in ArtifactType.__members__.values()]
if log_hint["artifact_type"] not in valid_artifact_types:
raise mlrun.errors.MLRunInvalidArgumentError(
f"The following artifact type '{log_hint['artifact_type']}' is not a valid `ArtifactType`. "
f"Please select one of the following: {','.join(valid_artifact_types)}"
)
return log_hint
[docs]def handler(
labels: Dict[str, str] = None,
outputs: List[Union[str, Dict[str, str]]] = None,
inputs: Union[bool, Dict[str, Union[str, Type]]] = True,
):
"""
MLRun's handler is a decorator to wrap a function and enable setting labels, automatic `mlrun.DataItem` parsing and
outputs logging.
:param labels: Labels to add to the run. Expecting a dictionary with the labels names as keys. Default: None.
:param outputs: Logging configurations for the function's returned values. Expecting a list of tuples and None
values:
* str - A string in the format of '{key}:{artifact_type}'. If a string was given without ':' it will
indicate the key and the artifact type will be according to the returned value type. The artifact
types can be one of: "dataset", "directory", "file", "object", "plot" and "result".
* Dict[str, str] - A dictionary of logging configuration. the key 'key' is mandatory for the logged
artifact key.
* None - Do not log the output.
The list length must be equal to the total amount of returned values from the function. Default is
None - meaning no outputs will be logged.
:param inputs: Parsing configurations for the arguments passed as inputs via the `run` method of an MLRun function.
Can be passed as a boolean value or a dictionary:
* True - Parse all found inputs to the assigned type hint in the function's signature. If there is no
type hint assigned, the value will remain an `mlrun.DataItem`.
* False - Do not parse inputs, leaving the inputs as `mlrun.DataItem`.
* Dict[str, Union[Type, str]] - A dictionary with argument name as key and the expected type to parse
the `mlrun.DataItem` to. The expected type can be a string as well, idicating the full module path.
**Notice**: Type hints from the `typing` module (e.g. `typing.Optional`, `typing.Union`,
`typing.List` etc.) are currently not supported but will be in the future.
Default: True.
Example::
import mlrun
@mlrun.handler(outputs=["my_array", None, "my_multiplier"])
def my_handler(array: np.ndarray, m: int):
array = array * m
m += 1
return array, "I won't be logged", m
>>> mlrun_function = mlrun.code_to_function("my_code.py", kind="job")
>>> run_object = mlrun_function.run(
... handler="my_handler",
... inputs={"array": "store://my_array_Artifact"},
... params={"m": 2}
... )
>>> run_object.outputs
{'my_multiplier': 3, 'my_array': 'store://...'}
"""
def decorator(func: Callable):
def wrapper(*args: tuple, **kwargs: dict):
nonlocal labels
nonlocal outputs
nonlocal inputs
# Set default `inputs` - inspect the full signature and add the user's input on top of it:
if inputs:
# Get the available parameters type hints from the function's signature:
func_signature = inspect.signature(func)
parameters = OrderedDict(
{
parameter.name: parameter.annotation
for parameter in func_signature.parameters.values()
}
)
# If user input is given, add it on top of the collected defaults (from signature), strings type hints
# will be parsed to their actual types:
if isinstance(inputs, dict):
parameters.update(
{
parameter_name: _parse_type_hint(type_hint=type_hint)
for parameter_name, type_hint in inputs.items()
}
)
inputs = parameters
# Create a context handler and look for a context:
context_handler = ContextHandler()
context_handler.look_for_context(args=args, kwargs=kwargs)
# If an MLRun context is found, parse arguments pre-run (kwargs are parsed inplace):
if context_handler.is_context_available() and inputs:
args = context_handler.parse_inputs(
args=args, kwargs=kwargs, type_hints=inputs
)
# Call the original function and get the returning values:
func_outputs = func(*args, **kwargs)
# If an MLRun context is found, set the given labels and log the returning values to MLRun via the context:
if context_handler.is_context_available():
if labels:
context_handler.set_labels(labels=labels)
if outputs:
context_handler.log_outputs(
outputs=func_outputs
if isinstance(func_outputs, tuple)
else [func_outputs],
log_hints=[
_parse_log_hint(log_hint=log_hint) for log_hint in outputs
],
)
return # Do not return any values as the returning values were logged to MLRun.
return func_outputs
# Make sure to pass the wrapped function's signature (argument list, type hints and doc strings) to the wrapper:
wrapper = functools.wraps(func)(wrapper)
return wrapper
return decorator
