mlrun.projects#

class mlrun.projects.MlrunProject(name=None, description=None, params=None, functions=None, workflows=None, artifacts=None, artifact_path=None, conda=None, metadata=None, spec=None, default_requirements: Optional[Union[str, List[str]]] = None)[source]#

Bases: mlrun.model.ModelObj

property artifact_path: str#

build_config(image: Optional[str] = None, set_as_default: bool = False, with_mlrun: Optional[bool] = None, base_image: Optional[str] = None, commands: Optional[list] = None, secret_name: Optional[str] = None, requirements: Optional[Union[str, List[str]]] = None, overwrite_build_params: bool = False, requirements_file: Optional[str] = None)[source]#

specify builder configuration for the project

Parameters

image – target image name/path. If not specified the project’s existing default_image name will be used. If not set, the mlconf.default_project_image_name value will be used
set_as_default – set image to be the project’s default image (default False)
with_mlrun – add the current mlrun package to the container build
base_image – base image name/path
commands – list of docker build (RUN) commands e.g. [‘pip install pandas’]
secret_name – k8s secret for accessing the docker registry
requirements – a list of packages to install on the built image
requirements_file – requirements file to install on the built image
overwrite_build_params –
overwrite existing build configuration (default False)
- False: the new params are merged with the existing (currently merge is applied to requirements and commands)
- True: the existing params are replaced by the new ones

build_function(function: Union[str, mlrun.runtimes.base.BaseRuntime], with_mlrun: Optional[bool] = None, skip_deployed: bool = False, image: Optional[str] = None, base_image: Optional[str] = None, commands: Optional[list] = None, secret_name: Optional[str] = None, requirements: Optional[Union[str, List[str]]] = None, mlrun_version_specifier: Optional[str] = None, builder_env: Optional[dict] = None, overwrite_build_params: bool = False, requirements_file: Optional[str] = None) → Union[mlrun.projects.operations.BuildStatus, kfp.dsl._container_op.ContainerOp][source]#

deploy ML function, build container with its dependencies

Parameters

function – name of the function (in the project) or function object
with_mlrun – add the current mlrun package to the container build
skip_deployed – skip the build if we already have an image for the function
image – target image name/path
base_image – base image name/path (commands and source code will be added to it)
commands – list of docker build (RUN) commands e.g. [‘pip install pandas’]
secret_name – k8s secret for accessing the docker registry
requirements – list of python packages, defaults to None
requirements_file – pip requirements file path, defaults to None
mlrun_version_specifier – which mlrun package version to include (if not current)
builder_env – Kaniko builder pod env vars dict (for config/credentials) e.g. builder_env={“GIT_TOKEN”: token}, does not work yet in KFP
overwrite_build_params – overwrite the function build parameters with the provided ones, or attempt to add to existing parameters

build_image(image: Optional[str] = None, set_as_default: bool = True, with_mlrun: Optional[bool] = None, skip_deployed: bool = False, base_image: Optional[str] = None, commands: Optional[list] = None, secret_name: Optional[str] = None, requirements: Optional[Union[str, List[str]]] = None, mlrun_version_specifier: Optional[str] = None, builder_env: Optional[dict] = None, overwrite_build_params: bool = False, requirements_file: Optional[str] = None) → Union[mlrun.projects.operations.BuildStatus, kfp.dsl._container_op.ContainerOp][source]#

Builder docker image for the project, based on the project’s build config. Parameters allow to override the build config.

Parameters

image – target image name/path. If not specified the project’s existing default_image name will be used. If not set, the mlconf.default_project_image_name value will be used
set_as_default – set image to be the project’s default image (default False)
with_mlrun – add the current mlrun package to the container build
skip_deployed – skip the build if we already have the image specified built
base_image – base image name/path (commands and source code will be added to it)
commands – list of docker build (RUN) commands e.g. [‘pip install pandas’]
secret_name – k8s secret for accessing the docker registry
requirements – list of python packages, defaults to None
requirements_file – pip requirements file path, defaults to None
mlrun_version_specifier – which mlrun package version to include (if not current)
builder_env – Kaniko builder pod env vars dict (for config/credentials) e.g. builder_env={“GIT_TOKEN”: token}, does not work yet in KFP
overwrite_build_params –
overwrite existing build configuration (default False)
- False: the new params are merged with the existing (currently merge is applied to requirements and commands)
- True: the existing params are replaced by the new ones

clear_context()[source]#: delete all files and clear the context dir

property context: str#

create_remote(url, name='origin', branch=None)[source]#

create remote for the project git

Parameters

url – remote git url
name – name for the remote (default is ‘origin’)
branch – Git branch to use as source

property default_image: str#

deploy_function(function: Union[str, mlrun.runtimes.base.BaseRuntime], dashboard: str = '', models: Optional[list] = None, env: Optional[dict] = None, tag: Optional[str] = None, verbose: Optional[bool] = None, builder_env: Optional[dict] = None, mock: Optional[bool] = None) → Union[mlrun.projects.operations.DeployStatus, kfp.dsl._container_op.ContainerOp][source]#

deploy real-time (nuclio based) functions

Parameters

function – name of the function (in the project) or function object
dashboard – DEPRECATED. Keep empty to allow auto-detection by MLRun API.
models – list of model items
env – dict of extra environment variables
tag – extra version tag
verbose – add verbose prints/logs
builder_env – env vars dict for source archive config/credentials e.g. builder_env={“GIT_TOKEN”: token}
mock – deploy mock server vs a real Nuclio function (for local simulations)

property description: str#

export(filepath=None, include_files: Optional[str] = None)[source]#

save the project object into a yaml file or zip archive (default to project.yaml)

By default the project object is exported to a yaml file, when the filepath suffix is ‘.zip’ the project context dir (code files) are also copied into the zip, the archive path can include DataItem urls (for remote object storage, e.g. s3://<bucket>/<path>).

Parameters

filepath – path to store project .yaml or .zip (with the project dir content)
include_files – glob filter string for selecting files to include in the zip archive

get_artifact(key, tag=None, iter=None)[source]#

Return an artifact object

Parameters

key – artifact key
tag – version tag
iter – iteration number (for hyper-param tasks)

Returns

Artifact object

get_artifact_uri(key: str, category: str = 'artifact', tag: Optional[str] = None) → str[source]#

return the project artifact uri (store://..) from the artifact key

example:

uri = project.get_artifact_uri("my_model", category="model", tag="prod")

Parameters

key – artifact key/name
category – artifact category (artifact, model, feature-vector, ..)
tag – artifact version tag, default to latest version

get_function(key, sync=False, enrich=False, ignore_cache=False, copy_function=True) → mlrun.runtimes.base.BaseRuntime[source]#

get function object by name

Parameters

key – name of key for search
sync – will reload/reinit the function from the project spec
enrich – add project info/config/source info to the function object
ignore_cache – read the function object from the DB (ignore the local cache)
copy_function – return a copy of the function object

Returns

function object

get_function_names() → List[str][source]#: get a list of all the project function names

get_function_objects() → Dict[str, mlrun.runtimes.base.BaseRuntime][source]#: “get a virtual dict with all the project functions ready for use in a pipeline

get_item_absolute_path(url: str, check_path_in_context: bool = False) → Tuple[str, bool][source]#

Get the absolute path of the artifact or function file :param url: remote url, absolute path or relative path :param check_path_in_context: if True, will check if the path exists when in the context

(temporary parameter to allow for backwards compatibility)

Returns: absolute path / url, whether the path is in the project context

get_param(key: str, default=None)[source]#: get project param by key

get_run_status(run, timeout=None, expected_statuses=None, notifiers: Optional[mlrun.utils.notifications.notification_pusher.CustomNotificationPusher] = None)[source]#

get_secret(key: str)[source]#: get a key based secret e.g. DB password from the context secrets can be specified when invoking a run through files, env, ..

get_store_resource(uri)[source]#: get store resource object by uri

import_artifact(item_path: str, new_key=None, artifact_path=None, tag=None)[source]#

Import an artifact object/package from .yaml, .json, or .zip file

Parameters

item_path – dataitem url or file path to the file/package
new_key – overwrite the artifact key/name
artifact_path – target artifact path (when not using the default)
tag – artifact tag to set

Returns

artifact object

kind = 'project'#

list_artifacts(name=None, tag=None, labels: Optional[Union[Dict[str, str], List[str]]] = None, since=None, until=None, iter: Optional[int] = None, best_iteration: bool = False, kind: Optional[str] = None, category: Optional[Union[str, mlrun.common.schemas.artifact.ArtifactCategories]] = None) → mlrun.lists.ArtifactList[source]#

List artifacts filtered by various parameters.

The returned result is an ArtifactList (list of dict), use .to_objects() to convert it to a list of RunObjects, .show() to view graphically in Jupyter, and .to_df() to convert to a DataFrame.

Examples:

# Get latest version of all artifacts in project
latest_artifacts = project.list_artifacts('', tag='latest')
# check different artifact versions for a specific artifact, return as objects list
result_versions = project.list_artifacts('results', tag='*').to_objects()

Parameters

name – Name of artifacts to retrieve. Name is used as a like query, and is not case-sensitive. This means that querying for name may return artifacts named my_Name_1 or surname.
tag – Return artifacts assigned this tag.
labels – Return artifacts that have these labels. Labels can either be a dictionary {“label”: “value”} or a list of “label=value” (match label key and value) or “label” (match just label key) strings.
since – Not in use in HTTPRunDB.
until – Not in use in HTTPRunDB.
iter – Return artifacts from a specific iteration (where iter=0 means the root iteration). If None (default) return artifacts from all iterations.
best_iteration – Returns the artifact which belongs to the best iteration of a given run, in the case of artifacts generated from a hyper-param run. If only a single iteration exists, will return the artifact from that iteration. If using best_iter, the iter parameter must not be used.
kind – Return artifacts of the requested kind.
category – Return artifacts of the requested category.

list_functions(name=None, tag=None, labels=None)[source]#

Retrieve a list of functions, filtered by specific criteria.

example:

functions = project.list_functions(tag="latest")

Parameters

name – Return only functions with a specific name.
tag – Return function versions with specific tags.
labels – Return functions that have specific labels assigned to them.

Returns

List of function objects.

list_models(name=None, tag=None, labels: Optional[Union[Dict[str, str], List[str]]] = None, since=None, until=None, iter: Optional[int] = None, best_iteration: bool = False)[source]#

List models in project, filtered by various parameters.

Examples:

# Get latest version of all models in project
latest_models = project.list_models('', tag='latest')

Parameters

name – Name of artifacts to retrieve. Name is used as a like query, and is not case-sensitive. This means that querying for name may return artifacts named my_Name_1 or surname.
tag – Return artifacts assigned this tag.
labels – Return artifacts that have these labels. Labels can either be a dictionary {“label”: “value”} or a list of “label=value” (match label key and value) or “label” (match just label key) strings.
since – Not in use in HTTPRunDB.
until – Not in use in HTTPRunDB.
iter – Return artifacts from a specific iteration (where iter=0 means the root iteration). If None (default) return artifacts from all iterations.
best_iteration – Returns the artifact which belongs to the best iteration of a given run, in the case of artifacts generated from a hyper-param run. If only a single iteration exists, will return the artifact from that iteration. If using best_iter, the iter parameter must not be used.

list_runs(name=None, uid=None, labels=None, state=None, sort=True, last=0, iter=False, start_time_from: Optional[datetime.datetime] = None, start_time_to: Optional[datetime.datetime] = None, last_update_time_from: Optional[datetime.datetime] = None, last_update_time_to: Optional[datetime.datetime] = None, **kwargs) → mlrun.lists.RunList[source]#

Retrieve a list of runs, filtered by various options.

The returned result is a `` (list of dict), use .to_objects() to convert it to a list of RunObjects, .show() to view graphically in Jupyter, .to_df() to convert to a DataFrame, and compare() to generate comparison table and PCP plot.

Example:

# return a list of runs matching the name and label and compare
runs = project.list_runs(name='download', labels='owner=admin')
runs.compare()
# If running in Jupyter, can use the .show() function to display the results
project.list_runs(name='').show()

Parameters

name – Name of the run to retrieve.
uid – Unique ID of the run.
project – Project that the runs belongs to.
labels – List runs that have a specific label assigned. Currently only a single label filter can be applied, otherwise result will be empty.
state – List only runs whose state is specified.
sort – Whether to sort the result according to their start time. Otherwise, results will be returned by their internal order in the DB (order will not be guaranteed).
last – Deprecated - currently not used.
iter – If True return runs from all iterations. Otherwise, return only runs whose iter is 0.
start_time_from – Filter by run start time in [start_time_from, start_time_to].
start_time_to – Filter by run start time in [start_time_from, start_time_to].
last_update_time_from – Filter by run last update time in (last_update_time_from, last_update_time_to).
last_update_time_to – Filter by run last update time in (last_update_time_from, last_update_time_to).

log_artifact(item, body=None, tag='', local_path='', artifact_path=None, format=None, upload=None, labels=None, target_path=None, **kwargs)[source]#

log an output artifact and optionally upload it to datastore

example:

project.log_artifact(
    "some-data",
    body=b"abc is 123",
    local_path="model.txt",
    labels={"framework": "xgboost"},
)

Parameters

item – artifact key or artifact class ()
body – will use the body as the artifact content
local_path – path to the local file we upload, will also be use as the destination subpath (under “artifact_path”)
artifact_path – target artifact path (when not using the default) to define a subpath under the default location use: artifact_path=context.artifact_subpath(‘data’)
format – artifact file format: csv, png, ..
tag – version tag
target_path – absolute target path (instead of using artifact_path + local_path)
upload – upload to datastore (default is True)
labels – a set of key/value labels to tag the artifact with

Returns

artifact object

log_dataset(key, df, tag='', local_path=None, artifact_path=None, upload=None, labels=None, format='', preview=None, stats=None, target_path='', extra_data=None, label_column: Optional[str] = None, **kwargs) → mlrun.artifacts.dataset.DatasetArtifact[source]#

log a dataset artifact and optionally upload it to datastore

example:

raw_data = {
    "first_name": ["Jason", "Molly", "Tina", "Jake", "Amy"],
    "last_name": ["Miller", "Jacobson", "Ali", "Milner", "Cooze"],
    "age": [42, 52, 36, 24, 73],
    "testScore": [25, 94, 57, 62, 70],
}
df = pd.DataFrame(raw_data, columns=["first_name", "last_name", "age", "testScore"])
project.log_dataset("mydf", df=df, stats=True)

Parameters

key – artifact key
df – dataframe object
label_column – name of the label column (the one holding the target (y) values)
local_path – path to the local dataframe file that exists locally. The given file extension will be used to save the dataframe to a file If the file exists, it will be uploaded to the datastore instead of the given df.
artifact_path – target artifact path (when not using the default). to define a subpath under the default location use: artifact_path=context.artifact_subpath(‘data’)
tag – version tag
format – optional, format to use (csv, parquet, pq, tsdb, kv)
target_path – absolute target path (instead of using artifact_path + local_path)
preview – number of lines to store as preview in the artifact metadata
stats – calculate and store dataset stats in the artifact metadata
extra_data – key/value list of extra files/charts to link with this dataset
upload – upload to datastore (default is True)
labels – a set of key/value labels to tag the artifact with

Returns

artifact object

log_model(key, body=None, framework='', tag='', model_dir=None, model_file=None, algorithm=None, metrics=None, parameters=None, artifact_path=None, upload=None, labels=None, inputs: Optional[List[mlrun.features.Feature]] = None, outputs: Optional[List[mlrun.features.Feature]] = None, feature_vector: Optional[str] = None, feature_weights: Optional[list] = None, training_set=None, label_column=None, extra_data=None, **kwargs)[source]#

log a model artifact and optionally upload it to datastore

example:

project.log_model("model", body=dumps(model),
                  model_file="model.pkl",
                  metrics=context.results,
                  training_set=training_df,
                  label_column='label',
                  feature_vector=feature_vector_uri,
                  labels={"app": "fraud"})

Parameters

key – artifact key or artifact class ()
body – will use the body as the artifact content
model_file – path to the local model file we upload (see also model_dir) or to a model file data url (e.g. http://host/path/model.pkl)
model_dir – path to the local dir holding the model file and extra files
artifact_path – target artifact path (when not using the default) to define a subpath under the default location use: artifact_path=context.artifact_subpath(‘data’)
framework – name of the ML framework
algorithm – training algorithm name
tag – version tag
metrics – key/value dict of model metrics
parameters – key/value dict of model parameters
inputs – ordered list of model input features (name, type, ..)
outputs – ordered list of model output/result elements (name, type, ..)
upload – upload to datastore (default is True)
labels – a set of key/value labels to tag the artifact with
feature_vector – feature store feature vector uri (store://feature-vectors/<project>/<name>[:tag])
feature_weights – list of feature weights, one per input column
training_set – training set dataframe, used to infer inputs & outputs
label_column – which columns in the training set are the label (target) columns
extra_data – key/value list of extra files/charts to link with this dataset value can be absolute path | relative path (to model dir) | bytes | artifact object

Returns

artifact object

property metadata: mlrun.projects.project.ProjectMetadata#

property mountdir: str#

property name: str#: Project name, this is a property of the project metadata

property notifiers#

property params: str#

pull(branch=None, remote=None)[source]#

pull/update sources from git or tar into the context dir

Parameters

branch – git branch, if not the current one
remote – git remote, if other than origin

push(branch, message=None, update=True, remote=None, add: Optional[list] = None)[source]#

update spec and push updates to remote git repo

Parameters

branch – target git branch
message – git commit message
update – update files (git add update=True)
remote – git remote, default to origin
add – list of files to add

register_artifacts()[source]#: register the artifacts in the MLRun DB (under this project)

reload(sync=False, context=None) → mlrun.projects.project.MlrunProject[source]#

reload the project and function objects from the project yaml/specs

Parameters

sync – set to True to load functions objects
context – context directory (where the yaml and code exist)

Returns

project object

remove_function(name)[source]#

remove a function from a project

Parameters: name – name of the function (under the project)

run(name: Optional[str] = None, workflow_path: Optional[str] = None, arguments: Optional[Dict[str, Any]] = None, artifact_path: Optional[str] = None, workflow_handler: Optional[Union[str, Callable]] = None, namespace: Optional[str] = None, sync: bool = False, watch: bool = False, dirty: bool = False, ttl: Optional[int] = None, engine: Optional[str] = None, local: Optional[bool] = None, schedule: Optional[Union[str, mlrun.common.schemas.schedule.ScheduleCronTrigger, bool]] = None, timeout: Optional[int] = None, overwrite: bool = False, source: Optional[str] = None, cleanup_ttl: Optional[int] = None) → mlrun.projects.pipelines._PipelineRunStatus[source]#

run a workflow using kubeflow pipelines

Parameters

name – name of the workflow
workflow_path – url to a workflow file, if not a project workflow
arguments – kubeflow pipelines arguments (parameters)
artifact_path – target path/url for workflow artifacts, the string ‘{{workflow.uid}}’ will be replaced by workflow id
workflow_handler – workflow function handler (for running workflow function directly)
namespace – kubernetes namespace if other than default
sync – force functions sync before run
watch – wait for pipeline completion
dirty – allow running the workflow when the git repo is dirty
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)
engine – workflow engine running the workflow. supported values are ‘kfp’ (default), ‘local’ or ‘remote’. for setting engine for remote running use ‘remote:local’ or ‘remote:kfp’.
local – run local pipeline with local functions (set local=True in function.run())
schedule – ScheduleCronTrigger class instance or a standard crontab expression string (which will be converted to the class using its from_crontab constructor), see this link for help: https://apscheduler.readthedocs.io/en/3.x/modules/triggers/cron.html#module-apscheduler.triggers.cron for using the pre-defined workflow’s schedule, set schedule=True
timeout – timeout in seconds to wait for pipeline completion (watch will be activated)
overwrite – (deprecated) replacing the schedule of the same workflow (under the same name) if exists with the new one.
source – remote source to use instead of the actual project.spec.source (used when engine is remote). for other engines the source is to validate that the code is up-to-date
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

run id

run_function(function: Union[str, mlrun.runtimes.base.BaseRuntime], handler: Optional[str] = None, name: str = '', params: Optional[dict] = None, hyperparams: Optional[dict] = None, hyper_param_options: Optional[mlrun.model.HyperParamOptions] = None, inputs: Optional[dict] = None, outputs: Optional[List[str]] = None, workdir: str = '', labels: Optional[dict] = None, base_task: Optional[mlrun.model.RunTemplate] = None, watch: bool = True, local: Optional[bool] = None, verbose: Optional[bool] = None, selector: Optional[str] = None, auto_build: Optional[bool] = None, schedule: Optional[Union[str, mlrun.common.schemas.schedule.ScheduleCronTrigger]] = None, artifact_path: Optional[str] = None, notifications: Optional[List[mlrun.model.Notification]] = None, returns: Optional[List[Union[str, Dict[str, str]]]] = None) → Union[mlrun.model.RunObject, kfp.dsl._container_op.ContainerOp][source]#

Run a local or remote task as part of a local/kubeflow pipeline

example (use with project):

# create a project with two functions (local and from hub)
project = mlrun.new_project(project_name, "./proj")
project.set_function("mycode.py", "myfunc", image="mlrun/mlrun")
project.set_function("hub://auto-trainer", "train")

# run functions (refer to them by name)
run1 = project.run_function("myfunc", params={"x": 7})
run2 = project.run_function("train", params={"label_columns": LABELS},
                                     inputs={"dataset":run1.outputs["data"]})

Parameters

function – name of the function (in the project) or function object
handler – name of the function handler
name – execution name
params – input parameters (dict)
hyperparams – hyper parameters
selector – selection criteria for hyper params e.g. “max.accuracy”
hyper_param_options – hyper param options (selector, early stop, strategy, ..) see: HyperParamOptions
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>”.
outputs – list of outputs which can pass in the workflow
workdir – default input artifacts path
labels – labels to tag the job/run with ({key:val, ..})
base_task – task object to use as base
watch – watch/follow run log, True by default
local – run the function locally vs on the runtime/cluster
verbose – add verbose prints/logs
auto_build – when set to True and the function require build it will be built on the first function run, use only if you dont plan on changing the build config between runs
schedule – ScheduleCronTrigger class instance or a standard crontab expression string (which will be converted to the class using its from_crontab constructor), see this link for help: https://apscheduler.readthedocs.io/en/3.x/modules/triggers/cron.html#module-apscheduler.triggers.cron
artifact_path – path to store artifacts, when running in a workflow this will be set automatically
notifications – list of notifications to push when the run is completed
returns –
List of log hints - 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 log hint 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”.

Returns

MLRun RunObject or KubeFlow containerOp

save(filepath=None, store=True)[source]#

export project to yaml file and save project in database

Store: if True, allow updating in case project already exists

save_to_db(store=True)[source]#

save project to database

Store: if True, allow updating in case project already exists

save_workflow(name, target, artifact_path=None, ttl=None)[source]#

create and save a workflow as a yaml or archive file

Parameters

name – workflow name
target – target file path (can end with .yaml or .zip)
artifact_path – target path/url for workflow artifacts, the string ‘{{workflow.uid}}’ will be replaced by workflow id
ttl – pipeline ttl (time to live) in secs (after that the pods will be removed)

set_artifact(key, artifact: Optional[Union[str, dict, mlrun.artifacts.base.Artifact]] = None, target_path: Optional[str] = None, tag: Optional[str] = None)[source]#

add/set an artifact in the project spec (will be registered on load)

example:

# register a simple file artifact
project.set_artifact('data', target_path=data_url)
# register a model artifact
project.set_artifact('model', ModelArtifact(model_file="model.pkl"), target_path=model_dir_url)

# register a path to artifact package (will be imported on project load)
# to generate such package use `artifact.export(target_path)`
project.set_artifact('model', 'https://mystuff.com/models/mymodel.zip')

Parameters

key – artifact key/name
artifact – mlrun Artifact object/dict (or its subclasses) or path to artifact file to import (yaml/json/zip), relative paths are relative to the context path
target_path – absolute target path url (point to the artifact content location)
tag – artifact tag

set_default_image(default_image: str)[source]#

Set the default image to be used for running runtimes (functions) in this project. This image will be used if an image was not provided for a runtime. In case the default image is replaced, functions already registered with the project that used the previous default image will have their image replaced on next execution.

Parameters: default_image – Default image to use

set_function(func: Optional[Union[str, mlrun.runtimes.base.BaseRuntime]] = None, name: str = '', kind: str = '', image: Optional[str] = None, handler=None, with_repo: Optional[bool] = None, tag: Optional[str] = None, requirements: Optional[Union[str, List[str]]] = None) → mlrun.runtimes.base.BaseRuntime[source]#

update or add a function object to the project

function can be provided as an object (func) or a .py/.ipynb/.yaml url support url prefixes:

object (s3://, v3io://, ..)
MLRun DB e.g. db://project/func:ver
functions hub/market: e.g. hub://auto-trainer:master

examples:

proj.set_function(func_object)
proj.set_function('./src/mycode.py', 'ingest',
                  image='myrepo/ing:latest', with_repo=True)
proj.set_function('http://.../mynb.ipynb', 'train')
proj.set_function('./func.yaml')
proj.set_function('hub://get_toy_data', 'getdata')

# set function requirements

# by providing a list of packages
proj.set_function('my.py', requirements=["requests", "pandas"])

# by providing a path to a pip requirements file
proj.set_function('my.py', requirements="requirements.txt")

Parameters

func – function object or spec/code url, None refers to current Notebook
name – name of the function (under the project)
kind – runtime kind e.g. job, nuclio, spark, dask, mpijob default: job
image – docker image to be used, can also be specified in the function object/yaml
handler – default function handler to invoke (can only be set with .py/.ipynb files)
with_repo – add (clone) the current repo to the build source
tag – function version tag (none for ‘latest’, can only be set with .py/.ipynb files)
requirements – list of python packages or pip requirements file path

Returns

project object

set_model_monitoring_credentials(access_key: Optional[str] = None, endpoint_store_connection: Optional[str] = None, stream_path: Optional[str] = None)[source]#

Set the credentials that will be used by the project’s model monitoring infrastructure functions.

Parameters

access_key – Model Monitoring access key for managing user permissions
endpoint_store_connection – Endpoint store connection string
stream_path – Path to the model monitoring stream

set_secrets(secrets: Optional[dict] = None, file_path: Optional[str] = None, provider: Optional[Union[str, mlrun.common.schemas.secret.SecretProviderName]] = None)[source]#

set project secrets from dict or secrets env file when using a secrets file it should have lines in the form KEY=VALUE, comment line start with “#” V3IO paths/credentials and MLrun service API address are dropped from the secrets

example secrets file:

# this is an env file
AWS_ACCESS_KEY_ID-XXXX
AWS_SECRET_ACCESS_KEY=YYYY

usage:

# read env vars from dict or file and set as project secrets
project.set_secrets({"SECRET1": "value"})
project.set_secrets(file_path="secrets.env")

Parameters

secrets – dict with secrets key/value
file_path – path to secrets file
provider – MLRun secrets provider

set_source(source: str = '', pull_at_runtime: bool = False, workdir: Optional[str] = None)[source]#

set the project source code path(can be git/tar/zip archive)

Parameters

source – valid absolute path or URL to git, zip, or tar file, (or None for current) 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 ‘workdir’ when source is a filepath instead)
pull_at_runtime – load the archive into the container at job runtime vs on build/deploy
workdir – workdir path relative to the context dir or absolute

set_workflow(name, workflow_path: str, embed=False, engine=None, args_schema: Optional[List[mlrun.model.EntrypointParam]] = None, handler=None, schedule: Optional[Union[str, mlrun.common.schemas.schedule.ScheduleCronTrigger]] = None, ttl=None, **args)[source]#

add or update a workflow, specify a name and the code path

Parameters

name – name of the workflow
workflow_path – url/path for the workflow file
embed – add the workflow code into the project.yaml
engine – workflow processing engine (“kfp” or “local”)
args_schema – list of arg schema definitions (:py:class`~mlrun.model.EntrypointParam`)
handler – workflow function handler
schedule – ScheduleCronTrigger class instance or a standard crontab expression string (which will be converted to the class using its from_crontab constructor), see this link for help: https://apscheduler.readthedocs.io/en/3.x/modules/triggers/cron.html#module-apscheduler.triggers.cron
ttl – pipeline ttl in secs (after that the pods will be removed)
args – argument values (key=value, ..)

property source: str#

property spec: mlrun.projects.project.ProjectSpec#

property status: mlrun.projects.project.ProjectStatus#

sync_functions(names: Optional[list] = None, always=True, save=False)[source]#: reload function objects from specs and files

with_secrets(kind, source, prefix='')[source]#

read secrets from a source provider to be used in workflows, example:

proj.with_secrets('file', 'file.txt')
proj.with_secrets('inline', {'key': 'val'})
proj.with_secrets('env', 'ENV1,ENV2', prefix='PFX_')

Vault secret source has several options:

proj.with_secrets('vault', {'user': <user name>, 'secrets': ['secret1', 'secret2' ...]})
proj.with_secrets('vault', {'project': <proj.name>, 'secrets': ['secret1', 'secret2' ...]})
proj.with_secrets('vault', ['secret1', 'secret2' ...])

The 2nd option uses the current project name as context. Can also use empty secret list:

proj.with_secrets('vault', [])

This will enable access to all secrets in vault registered to the current project.

Parameters

kind – secret type (file, inline, env, vault)
source – secret data or link (see example)
prefix – add a prefix to the keys in this source

Returns

project object

property workflows: list#

class mlrun.projects.ProjectMetadata(name=None, created=None, labels=None, annotations=None)[source]#

Bases: mlrun.model.ModelObj

property name: str#: Project name

static validate_project_name(name: str, raise_on_failure: bool = True) → bool[source]#

class mlrun.projects.ProjectSpec(description=None, params=None, functions=None, workflows=None, artifacts=None, artifact_path=None, conda=None, source=None, subpath=None, origin_url=None, goals=None, load_source_on_run=None, default_requirements: Optional[Union[str, List[str]]] = None, desired_state='online', owner=None, disable_auto_mount=None, workdir=None, default_image=None, build=None)[source]#

Bases: mlrun.model.ModelObj

property artifacts: list#: list of artifacts used in this project

property build: mlrun.model.ImageBuilder#

property functions: list#: list of function object/specs used in this project

get_code_path()[source]#: Get the path to the code root/workdir

property mountdir: str#: specify to mount the context dir inside the function container use ‘.’ to use the same path as in the client e.g. Jupyter

remove_artifact(key)[source]#

remove_function(name)[source]#

remove_workflow(name)[source]#

set_artifact(key, artifact)[source]#

set_function(name, function_object, function_dict)[source]#

set_workflow(name, workflow)[source]#

property source: str#: source url or git repo

property workflows: List[dict]#

list of workflows specs dicts used in this project

Type: returns

class mlrun.projects.ProjectStatus(state=None)[source]#: Bases: mlrun.model.ModelObj

mlrun.projects.build_function(function: Union[str, mlrun.runtimes.base.BaseRuntime], with_mlrun: Optional[bool] = None, skip_deployed: bool = False, image=None, base_image=None, commands: Optional[list] = None, secret_name=None, requirements: Optional[Union[str, List[str]]] = None, requirements_file: Optional[str] = None, mlrun_version_specifier=None, builder_env: Optional[dict] = None, project_object=None, overwrite_build_params: bool = False) → Union[mlrun.projects.operations.BuildStatus, kfp.dsl._container_op.ContainerOp][source]#

deploy ML function, build container with its dependencies

Parameters

function – name of the function (in the project) or function object
with_mlrun – add the current mlrun package to the container build
skip_deployed – skip the build if we already have an image for the function
image – target image name/path
base_image – base image name/path (commands and source code will be added to it)
commands – list of docker build (RUN) commands e.g. [‘pip install pandas’]
secret_name – k8s secret for accessing the docker registry
requirements – list of python packages, defaults to None
requirements_file – pip requirements file path, defaults to None
mlrun_version_specifier – which mlrun package version to include (if not current)
builder_env – Kaniko builder pod env vars dict (for config/credentials) e.g. builder_env={“GIT_TOKEN”: token}, does not work yet in KFP
project_object – override the project object to use, will default to the project set in the runtime context.
builder_env – Kaniko builder pod env vars dict (for config/credentials) e.g. builder_env={“GIT_TOKEN”: token}, does not work yet in KFP
overwrite_build_params – overwrite the function build parameters with the provided ones, or attempt to add to existing parameters

mlrun.projects.deploy_function(function: Union[str, mlrun.runtimes.base.BaseRuntime], dashboard: str = '', models: Optional[list] = None, env: Optional[dict] = None, tag: Optional[str] = None, verbose: Optional[bool] = None, builder_env: Optional[dict] = None, project_object=None, mock: Optional[bool] = None) → Union[mlrun.projects.operations.DeployStatus, kfp.dsl._container_op.ContainerOp][source]#

deploy real-time (nuclio based) functions

Parameters

function – name of the function (in the project) or function object
dashboard – DEPRECATED. Keep empty to allow auto-detection by MLRun API.
models – list of model items
env – dict of extra environment variables
tag – extra version tag
verbose – add verbose prints/logs
builder_env – env vars dict for source archive config/credentials e.g. builder_env={“GIT_TOKEN”: token}
mock – deploy mock server vs a real Nuclio function (for local simulations)
project_object – override the project object to use, will default to the project set in the runtime context.

mlrun.projects.get_or_create_project(name: str, context: str = './', url: Optional[str] = None, secrets: Optional[dict] = None, init_git=False, subpath: Optional[str] = None, clone: bool = False, user_project: bool = False, from_template: Optional[str] = None, save: bool = True) → mlrun.projects.project.MlrunProject[source]#

Load a project from MLRun DB, or create/import if doesnt exist

example:

# load project from the DB (if exist) or the source repo
project = get_or_create_project("myproj", "./", "git://github.com/mlrun/demo-xgb-project.git")
project.pull("development")  # pull the latest code from git
project.run("main", arguments={'data': data_url})  # run the workflow "main"

Parameters

name – project name
context – project local directory path (default value = “./”)
url – name (in DB) or git or tar.gz or .zip sources archive path e.g.: git://github.com/mlrun/demo-xgb-project.git http://mysite/archived-project.zip
secrets – key:secret dict or SecretsStore used to download sources
init_git – if True, will excute git init on the context dir
subpath – project subpath (within the archive/context)
clone – if True, always clone (delete any existing content)
user_project – add the current username to the project name (for db:// prefixes)
from_template – path to project YAML file that will be used as from_template (for new projects)
save – whether to save the created project in the DB

Returns

project object

mlrun.projects.load_project(context: str = './', url: Optional[str] = None, name: Optional[str] = None, secrets: Optional[dict] = None, init_git: bool = False, subpath: Optional[str] = None, clone: bool = False, user_project: bool = False, save: bool = True, sync_functions: bool = False) → mlrun.projects.project.MlrunProject[source]#

Load an MLRun project from git or tar or dir

example:

# Load the project and run the 'main' workflow.
# When using git as the url source the context directory must be an empty or
# non-existent folder as the git repo will be cloned there
project = load_project("./demo_proj", "git://github.com/mlrun/project-demo.git")
project.run("main", arguments={'data': data_url})

Parameters

context – project local directory path (default value = “./”)
url – name (in DB) or git or tar.gz or .zip sources archive path e.g.: git://github.com/mlrun/demo-xgb-project.git http://mysite/archived-project.zip <project-name> The git project should include the project yaml file. If the project yaml file is in a sub-directory, must specify the sub-directory.
name – project name
secrets – key:secret dict or SecretsStore used to download sources
init_git – if True, will git init the context dir
subpath – project subpath (within the archive)
clone – if True, always clone (delete any existing content)
user_project – add the current user name to the project name (for db:// prefixes)
save – whether to save the created project and artifact in the DB
sync_functions – sync the project’s functions into the project object (will be saved to the DB if save=True)

Returns

project object

mlrun.projects.new_project(name, context: str = './', init_git: bool = False, user_project: bool = False, remote: Optional[str] = None, from_template: Optional[str] = None, secrets: Optional[dict] = None, description: Optional[str] = None, subpath: Optional[str] = None, save: bool = True, overwrite: bool = False) → mlrun.projects.project.MlrunProject[source]#

Create a new MLRun project, optionally load it from a yaml/zip/git template

example:

# create a project with local and hub functions, a workflow, and an artifact
project = mlrun.new_project("myproj", "./", init_git=True, description="my new project")
project.set_function('prep_data.py', 'prep-data', image='mlrun/mlrun', handler='prep_data')
project.set_function('hub://auto-trainer', 'train')
project.set_artifact('data', Artifact(target_path=data_url))
project.set_workflow('main', "./myflow.py")
project.save()

# run the "main" workflow (watch=True to wait for run completion)
project.run("main", watch=True)

example (load from template):

# create a new project from a zip template (can also use yaml/git templates)
# initialize a local git, and register the git remote path
project = mlrun.new_project("myproj", "./", init_git=True,
                            remote="git://github.com/mlrun/project-demo.git",
                            from_template="http://mysite/proj.zip")
project.run("main", watch=True)

Parameters

name – project name
context – project local directory path (default value = “./”)
init_git – if True, will git init the context dir
user_project – add the current user name to the provided project name (making it unique per user)
remote – remote Git url
from_template – path to project YAML/zip file that will be used as a template
secrets – key:secret dict or SecretsStore used to download sources
description – text describing the project
subpath – project subpath (relative to the context dir)
save – whether to save the created project in the DB
overwrite – overwrite project using ‘cascade’ deletion strategy (deletes project resources) if project with name exists

Returns

project object

mlrun.projects.run_function(function: Union[str, mlrun.runtimes.base.BaseRuntime], handler: Optional[str] = None, name: str = '', params: Optional[dict] = None, hyperparams: Optional[dict] = None, hyper_param_options: Optional[mlrun.model.HyperParamOptions] = None, inputs: Optional[dict] = None, outputs: Optional[List[str]] = None, workdir: str = '', labels: Optional[dict] = None, base_task: Optional[mlrun.model.RunTemplate] = None, watch: bool = True, local: Optional[bool] = None, verbose: Optional[bool] = None, selector: Optional[str] = None, project_object=None, auto_build: Optional[bool] = None, schedule: Optional[Union[str, mlrun.common.schemas.schedule.ScheduleCronTrigger]] = None, artifact_path: Optional[str] = None, notifications: Optional[List[mlrun.model.Notification]] = None, returns: Optional[List[Union[str, Dict[str, str]]]] = None) → Union[mlrun.model.RunObject, kfp.dsl._container_op.ContainerOp][source]#

Run a local or remote task as part of a local/kubeflow pipeline

run_function() allow you to execute a function locally, on a remote cluster, or as part of an automated workflow function can be specified as an object or by name (str), when the function is specified by name it is looked up in the current project eliminating the need to redefine/edit functions.

when functions run as part of a workflow/pipeline (project.run()) some attributes can be set at the run level, e.g. local=True will run all the functions locally, setting artifact_path will direct all outputs to the same path. project runs provide additional notifications/reporting and exception handling. inside a Kubeflow pipeline (KFP) run_function() generates KFP “ContainerOps” which are used to form a DAG some behavior may differ between regular runs and deferred KFP runs.

example (use with function object):

LABELS = "is_error"
MODEL_CLASS = "sklearn.ensemble.RandomForestClassifier"
DATA_PATH = "s3://bigdata/data.parquet"
function = mlrun.import_function("hub://auto-trainer")
run1 = run_function(function, params={"label_columns": LABELS, "model_class": MODEL_CLASS},
                              inputs={"dataset": DATA_PATH})

example (use with project):

# create a project with two functions (local and from hub)
project = mlrun.new_project(project_name, "./proj)
project.set_function("mycode.py", "myfunc", image="mlrun/mlrun")
project.set_function("hub://auto-trainer", "train")

# run functions (refer to them by name)
run1 = run_function("myfunc", params={"x": 7})
run2 = run_function("train", params={"label_columns": LABELS, "model_class": MODEL_CLASS},
                             inputs={"dataset": run1.outputs["data"]})

example (use in pipeline):

@dsl.pipeline(name="test pipeline", description="test")
def my_pipe(url=""):
    run1 = run_function("loaddata", params={"url": url})
    run2 = run_function("train", params={"label_columns": LABELS, "model_class": MODEL_CLASS},
                                 inputs={"dataset": run1.outputs["data"]})

project.run(workflow_handler=my_pipe, arguments={"param1": 7})

Parameters

function – name of the function (in the project) or function object
handler – name of the function handler
name – execution name
params – input parameters (dict)
hyperparams – hyper parameters
selector – selection criteria for hyper params e.g. “max.accuracy”
hyper_param_options – hyper param options (selector, early stop, strategy, ..) see: HyperParamOptions
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>”.
outputs – list of outputs which can pass in the workflow
workdir – default input artifacts path
labels – labels to tag the job/run with ({key:val, ..})
base_task – task object to use as base
watch – watch/follow run log, True by default
local – run the function locally vs on the runtime/cluster
verbose – add verbose prints/logs
project_object – override the project object to use, will default to the project set in the runtime context.
auto_build – when set to True and the function require build it will be built on the first function run, use only if you dont plan on changing the build config between runs
schedule – ScheduleCronTrigger class instance or a standard crontab expression string (which will be converted to the class using its from_crontab constructor), see this link for help: https://apscheduler.readthedocs.io/en/3.x/modules/triggers/cron.html#module-apscheduler.triggers.cron
artifact_path – path to store artifacts, when running in a workflow this will be set automatically
notifications – list of notifications to push when the run is completed
returns –
List of log hints - 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 log hint 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”.

Returns

MLRun RunObject or KubeFlow containerOp