mlrun.run

mlrun.run#

class mlrun.run.RunStatuses[source]#

Bases: object

static all()[source]#

error = 'Error'#

failed = 'Failed'#

running = 'Running'#

skipped = 'Skipped'#

static stable_statuses()[source]#

succeeded = 'Succeeded'#

static transient_statuses()[source]#

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 {Kinds of function (runtimes)](../concepts/functions-overview.html).

Parameters:

name – function name, typically best to use hyphen-case
project – project used to namespace the function, defaults to ‘default’
tag – function tag to track multiple versions of the same function, defaults to ‘latest’
filename – path to .py/.ipynb file, defaults to current jupyter notebook
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’
kind – function runtime type string - nuclio, job, etc. (see docstring for all options)
image – base docker image to use for building the function container, defaults to None
code_output – specify ‘.’ to generate python module from the current jupyter notebook
embed_code – indicates whether or not to inject the code directly into the function runtime spec, defaults to True
description – short function description, defaults to ‘’
requirements – list of python packages or pip requirements file path, defaults to None
requirements – a list of python packages
requirements_file – path to a python requirements file
categories – list of categories for mlrun Function Hub, defaults to None
labels – immutable name/value pairs to tag the function with useful metadata, defaults to None
with_doc – indicates whether to document the function parameters, defaults to True
ignored_tags – notebook cells to ignore when converting notebooks to py code (separated by ‘;’)

Returns:

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"})

mlrun.run.download_object(url, target, secrets=None)[source]#: download mlrun dataitem (from path/url to target path)

mlrun.run.function_to_module(code='', workdir=None, secrets=None, silent=False)[source]#

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())

Parameters:

code – path/url to function (.py or .ipynb or .yaml) OR function object
workdir – code workdir
secrets – secrets needed to access the URL (e.g.s3, v3io, ..)
silent – do not raise on errors

Returns:

python module

mlrun.run.get_dataitem(url, secrets=None, db=None) → DataItem[source]#: get mlrun dataitem object (from path/url)

mlrun.run.get_object(url, secrets=None, size=None, offset=0, db=None)[source]#: get mlrun dataitem body (from path/url)

mlrun.run.get_or_create_ctx(name: str, event=None, spec=None, with_env: bool = True, rundb: str = '', project: str = '', upload_artifacts=False, labels: dict | None = None)[source]#

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.

Parameters:

name – run name (will be overridden by context)
event – function (nuclio Event object)
spec – dictionary holding run spec
with_env – look for context in environment vars, default True
rundb – path/url to the metadata and artifact database
project – project to initiate the context in (by default mlrun.mlctx.default_project)
upload_artifacts – when using local context (not as part of a job/run), upload artifacts to the system default artifact path location
labels – dict of the context labels

Returns:

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')

mlrun.run.get_pipeline(run_id, namespace=None, format_: str | PipelinesFormat = PipelinesFormat.summary, project: str | None = None, remote: bool = True)[source]#

Get Pipeline status

Parameters:

run_id – id of pipelines run
namespace – k8s namespace if not default
format – Format of the results. Possible values are: - summary (default value) - Return summary of the object data. - full - Return full pipeline object.
project – the project of the pipeline run
remote – read kfp data from mlrun service (default=True)

Returns:

kfp run dict

mlrun.run.import_function(url='', secrets=None, db='', project=None, new_name=None)[source]#

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://[{source}/]{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")

Parameters:

url – path/url to Function Hub, db or function YAML file
secrets – optional, credentials dict for DB or URL (s3, v3io, …)
db – optional, mlrun api/db path
project – optional, target project for the function
new_name – optional, override the imported function name

Returns:

function object

mlrun.run.import_function_to_dict(url, secrets=None)[source]#: Load function spec from local/remote YAML file

mlrun.run.list_pipelines(full=False, page_token='', page_size=None, sort_by='', filter_='', namespace=None, project='*', format_: PipelinesFormat = PipelinesFormat.metadata_only) → Tuple[int, int | None, List[dict]][source]#

List pipelines

Parameters:

full – Deprecated, use format_ instead. if True will set format_ to full, otherwise format_ will be used
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.
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.
sort_by – Can be format of “field_name”, “field_name asc” or “field_name desc” (Example, “name asc” or “id desc”). Ascending by default.
filter – A url-encoded, JSON-serialized Filter protocol buffer, see: [filter.proto](kubeflow/pipelines blob/master/backend/api/filter.proto).
namespace – Kubernetes namespace if other than default
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.
format – Control what will be returned (full/metadata_only/name_only)

mlrun.run.load_func_code(command='', workdir=None, secrets=None, name='name')[source]#

mlrun.run.new_function(name: str = '', project: str = '', tag: str = '', kind: str = '', command: str = '', image: str = '', args: list | None = None, runtime=None, mode=None, handler: str | None = None, source: str | None = None, requirements: str | List[str] | None = None, kfp=None, requirements_file: str = '')[source]#

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)

Parameters:

name – function name
project – function project (none for ‘default’)
tag – function version tag (none for ‘latest’)
kind – runtime type (local, job, nuclio, spark, mpijob, dask, ..)
command – command/url + args (e.g.: training.py –verbose)
image – container image (start with ‘.’ for default registry)
args – command line arguments (override the ones in command)
runtime – runtime (job, nuclio, spark, dask ..) object/dict store runtime specific details and preferences
mode – runtime mode: * pass - will run the command as is in the container (not wrapped by mlrun), the command can use params substitutions like {xparam} and will be replaced with the value of the xparam param if a command is not specified, then image entrypoint shall be used.
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”
source – valid absolute path or URL to git, zip, or tar file, e.g. git://github.com/mlrun/something.git, http://some/url/file.zip note path source must exist on the image or exist locally when run is local (it is recommended to use ‘function.spec.workdir’ when source is a filepath instead)
requirements – a list of python packages, defaults to None
requirements_file – path to a python requirements file
kfp – reserved, flag indicating running within kubeflow pipeline

Returns:

function object

mlrun.run.wait_for_pipeline_completion(run_id, timeout=3600, expected_statuses: List[str] | None = None, namespace=None, remote=True, project: str | None = None)[source]#

Wait for Pipeline status, timeout in sec

Parameters:

run_id – id of pipelines run
timeout – wait timeout in sec
expected_statuses – list of expected statuses, one of [ Succeeded | Failed | Skipped | Error ], by default [ Succeeded ]
namespace – k8s namespace if not default
remote – read kfp data from mlrun service (default=True)
project – the project of the pipeline

Returns:

kfp run dict

mlrun.run.wait_for_runs_completion(runs: list | ValuesView, sleep=3, timeout=0, silent=False)[source]#

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])

Parameters:

runs – list of run objects (the returned values of function.run())
sleep – time to sleep between checks (in seconds)
timeout – maximum time to wait in seconds (0 for unlimited)
silent – set to True for silent exit on timeout

Returns:

list of completed runs

mlrun.run

Contents

mlrun.run#