Source code for mlrun.run

# 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 importlib.util as imputil
import json
import socket
import time
import uuid
from base64 import b64decode
from copy import deepcopy
from os import environ, makedirs, path
from pathlib import Path
from tempfile import mktemp
from typing import Dict, List, Optional, Tuple, Union

import yaml
from kfp import Client
from nuclio import build_file

import mlrun.api.schemas
import mlrun.errors
import mlrun.utils.helpers

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 .k8s_utils import get_k8s_helper
from .model import BaseMetadata, RunObject, RunTemplate
from .runtimes import (
    DaskCluster,
    HandlerRuntime,
    KubejobRuntime,
    LocalRuntime,
    MpiRuntimeV1,
    MpiRuntimeV1Alpha1,
    RemoteRuntime,
    RemoteSparkRuntime,
    RuntimeKinds,
    ServingRuntime,
    SparkRuntime,
    get_runtime_class,
)
from .runtimes.funcdoc import update_function_entry_points
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_meta,
    parse_versioned_object_uri,
    retry_until_successful,
    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, ): """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 (dict of key: path) :param artifact_path: default artifact output path :return: run object """ if command and isinstance(command, str): sp = command.split() command = sp[0] if len(sp) > 1: args = args or [] args = sp[1:] + args meta = BaseMetadata(name, project=project, tag=tag) command, runtime = _load_func_code(command, workdir, secrets=secrets, name=name) if runtime: handler = handler or get_in(runtime, "spec.default_handler", "") meta = BaseMetadata.from_dict(runtime["metadata"]) meta.name = name or meta.name meta.project = project or meta.project meta.tag = tag or meta.tag fn = new_function(meta.name, command=command, args=args, mode=mode) meta.name = fn.metadata.name fn.metadata = meta if workdir: fn.spec.workdir = str(workdir) return fn.run( task, name=name, handler=handler, params=params, inputs=inputs, artifact_path=artifact_path, )
[docs]def function_to_module(code="", workdir=None, secrets=None): """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, ..) :returns: python module """ command, runtime = _load_func_code(code, workdir, secrets=secrets) if not command: raise ValueError("nothing to run, specify command or function") 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
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.to_dict() else: is_remote = "://" in command data = get_object(command, secrets) runtime = yaml.load(data, Loader=yaml.FullLoader) command = get_in(runtime, "spec.command", "") code = get_in(runtime, "spec.build.functionSourceCode") kind = get_in(runtime, "kind", "") if kind in RuntimeKinds.nuclio_runtimes(): code = get_in(runtime, "spec.base_spec.spec.build.functionSourceCode", code) if code: fpath = mktemp(".py") code = b64decode(code).decode("utf-8") command = fpath with open(fpath, "w") as fp: fp.write(code) elif command and not is_remote: command = path.join(workdir or "", command) if not path.isfile(command): raise OSError(f"command file {command} not found") else: raise RuntimeError(f"cannot run, command={command}") elif command == "": pass elif suffix == ".ipynb": fpath = mktemp(".py") code_to_function(name, filename=command, kind="local", code_output=fpath) command = fpath elif suffix == ".py": if "://" in command: fpath = mktemp(".py") download_object(command, fpath, secrets) command = fpath 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 = "", ): """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) :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 = {} 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"] = ( project or newspec["metadata"].get("project") or mlconf.default_project ) ctx = MLClientCtx.from_dict( newspec, rundb=out, autocommit=autocommit, tmp=tmp, host=socket.gethostname() ) return ctx
[docs]def import_function(url="", secrets=None, db="", project=None): """Create function object from DB or local/remote YAML file Function can be imported from function repositories (mlrun marketplace or local db), or be read from a remote URL (http(s), s3, git, v3io, ..) containing the function YAML special URLs:: function marketplace: hub://{name}[:{tag}] local mlrun db: db://{project-name}/{name}[:{tag}] examples:: function = mlrun.import_function("hub://sklearn_classifier") function = mlrun.import_function("./func.yaml") function = mlrun.import_function("https://raw.githubusercontent.com/org/repo/func.yaml") :param url: path/url to marketplace, 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 :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 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: fpath = mktemp(".py") code = b64decode(code).decode("utf-8") update_in(runtime, "spec.command", fpath) with open(fpath, "w") as fp: fp.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: # todo: regex check for valid name if command and kind not in [RuntimeKinds.remote]: name, _ = path.splitext(path.basename(command)) else: name = "mlrun-" + uuid.uuid4().hex[0:6] 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: if not hasattr(runner, "with_source_archive"): raise ValueError( f"source archive option is not supported for {kind} runtime" ) runner.with_source_archive(source) if requirements: runner.with_requirements(requirements) if handler: runner.spec.default_handler = handler if kind.startswith("nuclio"): runner.spec.function_handler = handler 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, ) -> Union[ MpiRuntimeV1Alpha1, MpiRuntimeV1, RemoteRuntime, ServingRuntime, DaskCluster, KubejobRuntime, LocalRuntime, SparkRuntime, 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 marketplace, 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 :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)) def add_name(origin, name=""): name = filename or (name + ".ipynb") if not origin: return name return f"{origin}:{name}" def update_meta(fn): fn.spec.description = description fn.metadata.project = project or mlconf.default_project fn.metadata.tag = tag fn.metadata.categories = categories fn.metadata.labels = 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 = build_file( filename, name=name, handler=handler or "handler", kind=subkind ) 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 = build_file( filename, name=name, handler=handler or "handler", kind=subkind ) 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 if image: r.spec.image = image r.spec.default_handler = handler if embed_code: update_in(spec, "kind", "Function") r.spec.base_spec = spec else: r.spec.source = filename r.spec.function_handler = handler if not name: raise ValueError("name must be specified") r.metadata.name = name r.spec.build.code_origin = code_origin if requirements: r.with_requirements(requirements) update_meta(r) 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 = build_file(filename, name=name) 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 r.spec.image = image or get_in(spec, "spec.image", "") build = r.spec.build build.code_origin = code_origin build.base_image = get_in(spec, "spec.build.baseImage") build.commands = get_in(spec, "spec.build.commands") build.extra = get_in(spec, "spec.build.extra") if embed_code: build.functionSourceCode = get_in(spec, "spec.build.functionSourceCode") else: if code_output: r.spec.command = code_output else: r.spec.command = filename build.image = get_in(spec, "spec.build.image") build.secret = get_in(spec, "spec.build.secret") if requirements: r.with_requirements(requirements) if r.kind != "local": r.spec.env = get_in(spec, "spec.env") for vol in get_in(spec, "spec.volumes", []): r.spec.volumes.append(vol.get("volume")) r.spec.volume_mounts.append(vol.get("volumeMount")) if with_doc: update_function_entry_points(r, code) r.spec.default_handler = handler update_meta(r) return r
[docs]def run_pipeline( pipeline, arguments=None, project=None, experiment=None, run=None, namespace=None, artifact_path=None, ops=None, url=None, 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 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 ttl in secs (after that the pods will be removed) :returns: kubeflow pipeline id """ remote = not get_k8s_helper(silent=True).is_running_inside_kubernetes_cluster() artifact_path = artifact_path or mlconf.artifact_path 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: mldb = get_run_db(url) if mldb.kind != "http": raise ValueError( "run pipeline require access to remote api-service" ", please set the dbpath url" ) id = mldb.submit_pipeline( pipeline, arguments, experiment=experiment, run=run, namespace=namespace, ops=ops, artifact_path=artifact_path, ) 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_meta(artifact_path, ttl, ops) run_result = client.create_run_from_pipeline_func( pipeline, arguments, run_name=run, experiment_name=experiment, pipeline_conf=conf, ) id = run_result.run_id logger.info(f"Pipeline run id={id}, check UI or DB for progress") return id
[docs]def wait_for_pipeline_completion( run_id, timeout=60 * 60, expected_statuses: List[str] = None, namespace=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 :return: kfp run dict """ if expected_statuses is None: expected_statuses = [RunStatuses.succeeded] namespace = namespace or mlconf.namespace remote = not get_k8s_helper(silent=True).is_running_inside_kubernetes_cluster() logger.debug( f"Waiting for run completion." f" run_id: {run_id}," f" expected_statuses: {expected_statuses}," f" timeout: {timeout}," f" remote: {remote}," f" namespace: {namespace}" ) if remote: mldb = get_run_db() def get_pipeline_if_completed(run_id, namespace=namespace): resp = mldb.get_pipeline(run_id, namespace=namespace) status = resp["run"]["status"] 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() status = resp["run"]["status"] if resp else "unknown" if expected_statuses: if status not in expected_statuses: raise RuntimeError(f"run status {status} not in expected statuses") logger.debug( f"Finished waiting for pipeline completion." f" run_id: {run_id}," f" status: {status}," f" namespace: {namespace}" ) return resp
[docs]def get_pipeline(run_id, namespace=None): """Get Pipeline status :param run_id: id of pipelines run :param namespace: k8s namespace if not default :return: kfp run dict """ namespace = namespace or mlconf.namespace remote = not get_k8s_helper(silent=True).is_running_inside_kubernetes_cluster() if remote: mldb = 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) else: client = Client(namespace=namespace) resp = client.get_run(run_id) if resp: resp = resp.to_dict() 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 = 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): """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