mlrun.runtimes

class mlrun.runtimes.BaseRuntime(metadata=None, spec=None)

Bases: mlrun.model.ModelObj

as_step(runspec: Optional[mlrun.model.RunObject] = None, handler=None, name: str = '', project: str = '', params: Optional[dict] = None, hyperparams=None, selector='', hyper_param_options: Optional[mlrun.model.HyperParamOptions] = None, inputs: Optional[dict] = None, outputs: Optional[dict] = None, workdir: str = '', artifact_path: str = '', image: str = '', labels: Optional[dict] = None, use_db=True, verbose=None, scrape_metrics=False, returns: Optional[List[Union[str, Dict[str, str]]]] = None, auto_build: bool = False)

Run a local or remote task.

Parameters

• runspec – run template object or dict (see RunTemplate)

• handler – name of the function handler

• name – execution name

• project – project name

• params – input parameters (dict)

• hyperparams – hyper parameters

• selector – selection criteria for hyper params

• 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

• artifact_path – default artifact output path (replace out_path)

• workdir – default input artifacts path

• image – container image to use

• labels – labels to tag the job/run with ({key:val, ..})

• use_db – save function spec in the db (vs the workflow file)

• verbose – add verbose prints/logs

• scrape_metrics – whether to add the mlrun/scrape-metrics label to this run’s resources

• returns –

  List of configurations for how to log the returning values from the handler’s run (as artifacts or results). The list’s length must be equal to the amount of returning objects. A configuration may be given as:

  • A string of the key to use to log the returning value as result or as an artifact. To specify The artifact type, it is possible to pass a string in the following structure: “<key> : <type>”. Available artifact types can be seen in mlrun.ArtifactType. If no artifact type is specified, the object’s default artifact type will be used.

  • A dictionary of configurations to use when logging. Further info per object type and artifact type can be given there. The artifact key must appear in the dictionary as “key”: “the_key”.

• 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

Returns

KubeFlow containerOp

clean_build_params()

doc()

export(target='', format='.yaml', secrets=None, strip=True)

save function spec to a local/remote path (default to./function.yaml)

Parameters

• target – target path/url

• format – .yaml (default) or .json

• secrets – optional secrets dict/object for target path (e.g. s3)

• strip – strip status data

Returns

self

full_image_path(image=None, client_version: Optional[str] = None, client_python_version: Optional[str] = None)

is_deployed()

kind = 'base'

property metadata: mlrun.model.BaseMetadata

prepare_image_for_deploy()

if a function has a ‘spec.image’ it is considered to be deployed, but because we allow the user to set ‘spec.image’ for usability purposes, we need to check whether this is a built image or it requires to be built on top.

run(runspec: Optional[Union[mlrun.model.RunTemplate, mlrun.model.RunObject, dict]] = None, handler: Optional[Union[str, Callable]] = None, name: Optional[str] = '', project: Optional[str] = '', params: Optional[dict] = None, inputs: Optional[Dict[str, str]] = None, out_path: Optional[str] = '', workdir: Optional[str] = '', artifact_path: Optional[str] = '', watch: Optional[bool] = True, schedule: Optional[Union[str, mlrun.common.schemas.schedule.ScheduleCronTrigger]] = None, hyperparams: Optional[Dict[str, list]] = None, hyper_param_options: Optional[mlrun.model.HyperParamOptions] = None, verbose: Optional[bool] = None, scrape_metrics: Optional[bool] = None, local: Optional[bool] = False, local_code_path: Optional[str] = None, auto_build: Optional[bool] = None, param_file_secrets: Optional[Dict[str, str]] = None, notifications: Optional[List[mlrun.model.Notification]] = None, returns: Optional[List[Union[str, Dict[str, str]]]] = None) → mlrun.model.RunObject

Run a local or remote task.

Parameters

• runspec – The run spec to generate the RunObject from. Can be RunTemplate | RunObject | dict.

• handler – Pointer or name of a function handler.

• name – Execution name.

• project – Project name.

• params – Input parameters (dict).

• 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>”.

• out_path – Default artifact output path.

• artifact_path – Default artifact output path (will replace out_path).

• workdir – Default input artifacts path.

• watch – Watch/follow run log.

• 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

• hyperparams – Dict of param name and list of values to be enumerated e.g. {“p1”: [1,2,3]} the default strategy is grid search, can specify strategy (grid, list, random) and other options in the hyper_param_options parameter.

• hyper_param_options – Dict or HyperParamOptions struct of hyperparameter options.

• verbose – Add verbose prints/logs.

• scrape_metrics – Whether to add the mlrun/scrape-metrics label to this run’s resources.

• local – Run the function locally vs on the runtime/cluster.

• local_code_path – Path of the code for local runs & debug.

• auto_build – When set to True and the function require build it will be built on the first function run, use only if you don’t plan on changing the build config between runs.

• param_file_secrets – Dictionary of secrets to be used only for accessing the hyper-param parameter file. These secrets are only used locally and will not be stored anywhere

• 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

Run context object (RunObject) with run metadata, results and status

save(tag='', versioned=False, refresh=False) → str

set_db_connection(conn)

set_label(key, value)

property spec: mlrun.runtimes.base.FunctionSpec

property status: mlrun.runtimes.base.FunctionStatus

store_run(runobj: mlrun.model.RunObject)

to_dict(fields=None, exclude=None, strip=False)

convert the object to a python dictionary

try_auto_mount_based_on_config()

property uri

validate_and_enrich_service_account(allowed_service_account, default_service_account)

verify_base_image()

with_code(from_file='', body=None, with_doc=True)

Update the function code This function eliminates the need to build container images every time we edit the code

Parameters

• from_file – blank for current notebook, or path to .py/.ipynb file

• body – will use the body as the function code

• with_doc – update the document of the function parameters

Returns

function object

with_commands(commands: List[str], overwrite: bool = False, verify_base_image: bool = False, prepare_image_for_deploy: bool = True)

add commands to build spec.

Parameters

• commands – list of commands to run during build

• overwrite – overwrite existing commands

• verify_base_image – verify that the base image is configured (deprecated, use prepare_image_for_deploy)

• prepare_image_for_deploy – prepare the image/base_image spec for deployment

Returns

function object

with_requirements(requirements: Union[str, List[str]], overwrite: bool = False, verify_base_image: bool = False, prepare_image_for_deploy: bool = True, requirements_file: str = '')

add package requirements from file or list to build spec.

Parameters

• requirements – a list of python packages

• requirements_file – a local python requirements file path

• overwrite – overwrite existing requirements

• verify_base_image – verify that the base image is configured (deprecated, use prepare_image_for_deploy)

• prepare_image_for_deploy – prepare the image/base_image spec for deployment

Returns

function object

class mlrun.runtimes.DaskCluster(spec=None, metadata=None)

Bases: mlrun.runtimes.kubejob.KubejobRuntime

property client

close(running=True)

cluster()

deploy(watch=True, with_mlrun=None, skip_deployed=False, is_kfp=False, mlrun_version_specifier=None, builder_env: Optional[dict] = None, show_on_failure: bool = False)

deploy function, build container with dependencies

Parameters

• watch – wait for the deploy to complete (and print build logs)

• 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

• is_kfp – deploy as part of a kfp pipeline

• 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}

• show_on_failure – show logs only in case of build failure

:return True if the function is ready (deployed)

get_status()

gpus(gpus, gpu_type='nvidia.com/gpu')

property initialized

is_deployed()

check if the function is deployed (has a valid container)

kind = 'dask'

property spec: mlrun.runtimes.daskjob.DaskSpec

property status: mlrun.runtimes.daskjob.DaskStatus

with_limits(mem=None, cpu=None, gpus=None, gpu_type='nvidia.com/gpu', patch: bool = False)

set pod cpu/memory/gpu limits by default it overrides the whole limits section, if you wish to patch specific resources use patch=True.

with_requests(mem=None, cpu=None, patch: bool = False)

set requested (desired) pod cpu/memory resources by default it overrides the whole requests section, if you wish to patch specific resources use patch=True.

with_scheduler_limits(mem: Optional[str] = None, cpu: Optional[str] = None, gpus: Optional[int] = None, gpu_type: str = 'nvidia.com/gpu', patch: bool = False)

set scheduler pod resources limits by default it overrides the whole limits section, if you wish to patch specific resources use patch=True.

with_scheduler_requests(mem: Optional[str] = None, cpu: Optional[str] = None, patch: bool = False)

set scheduler pod resources requests by default it overrides the whole requests section, if you wish to patch specific resources use patch=True.

with_worker_limits(mem: Optional[str] = None, cpu: Optional[str] = None, gpus: Optional[int] = None, gpu_type: str = 'nvidia.com/gpu', patch: bool = False)

set worker pod resources limits by default it overrides the whole limits section, if you wish to patch specific resources use patch=True.

with_worker_requests(mem: Optional[str] = None, cpu: Optional[str] = None, patch: bool = False)

set worker pod resources requests by default it overrides the whole requests section, if you wish to patch specific resources use patch=True.

class mlrun.runtimes.HandlerRuntime(metadata=None, spec=None)

Bases: mlrun.runtimes.base.BaseRuntime, mlrun.runtimes.local.ParallelRunner

kind = 'handler'

class mlrun.runtimes.KubejobRuntime(spec=None, metadata=None)

Bases: mlrun.runtimes.pod.KubeResource

build_config(image='', base_image=None, commands: Optional[list] = None, secret=None, source=None, extra=None, load_source_on_run=None, with_mlrun=None, auto_build=None, requirements=None, overwrite=False, verify_base_image=False, prepare_image_for_deploy=True, requirements_file=None)

specify builder configuration for the deploy operation

Parameters

• image – target image name/path

• base_image – base image name/path

• commands – list of docker build (RUN) commands e.g. [‘pip install pandas’]

• secret – k8s secret for accessing the docker registry

• source – source git/tar archive to load code from in to the context/workdir e.g. git://github.com/mlrun/something.git#development

• extra – extra Dockerfile lines

• load_source_on_run – load the archive code into the container at runtime vs at build time

• with_mlrun – add the current mlrun package to the container build

• 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

• requirements – a list of packages to install

• requirements_file – requirements file to install

• overwrite –

  overwrite existing build configuration

  • 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

• verify_base_image – verify that the base image is configured (deprecated, use prepare_image_for_deploy)

• prepare_image_for_deploy – prepare the image/base_image spec for deployment

deploy(watch=True, with_mlrun=None, skip_deployed=False, is_kfp=False, mlrun_version_specifier=None, builder_env: Optional[dict] = None, show_on_failure: bool = False) → bool

deploy function, build container with dependencies

Parameters

• watch – wait for the deploy to complete (and print build logs)

• 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

• is_kfp – deploy as part of a kfp pipeline

• 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}

• show_on_failure – show logs only in case of build failure

:return True if the function is ready (deployed)

deploy_step(image=None, base_image=None, commands: Optional[list] = None, secret_name='', with_mlrun=True, skip_deployed=False)

is_deployed()

check if the function is deployed (has a valid container)

kind = 'job'

with_source_archive(source, workdir=None, handler=None, pull_at_runtime=True, target_dir=None)

load the code from git/tar/zip archive at runtime or build

Parameters

• 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 ‘workdir’ when source is a filepath instead)

• handler – default function handler

• workdir – working dir relative to the archive root (e.g. ‘./subdir’) or absolute to the image root

• pull_at_runtime – load the archive into the container at job runtime vs on build/deploy

• target_dir – target dir on runtime pod or repo clone / archive extraction

class mlrun.runtimes.LocalRuntime(metadata=None, spec=None)

Bases: mlrun.runtimes.base.BaseRuntime, mlrun.runtimes.local.ParallelRunner

is_deployed()

kind = 'local'

to_job(image='')

with_source_archive(source, workdir=None, handler=None, target_dir=None)

load the code from git/tar/zip archive at runtime or build

Parameters

• source – valid path to git, zip, or tar file, e.g. git://github.com/mlrun/something.git http://some/url/file.zip

• handler – default function handler

• workdir – working dir relative to the archive root (e.g. ‘./subdir’) or absolute

• target_dir – local target dir for repo clone (by default its <current-dir>/code)

class mlrun.runtimes.RemoteRuntime(spec=None, metadata=None)

Bases: mlrun.runtimes.pod.KubeResource

add_secrets_config_to_spec()

add_trigger(name, spec)

add a nuclio trigger object/dict

Parameters

• name – trigger name

• spec – trigger object or dict

add_v3io_stream_trigger(stream_path, name='stream', group='serving', seek_to='earliest', shards=1, extra_attributes=None, ack_window_size=None, **kwargs)

add v3io stream trigger to the function

Parameters

• stream_path – v3io stream path (e.g. ‘v3io:///projects/myproj/stream1’)

• name – trigger name

• group – consumer group

• seek_to – start seek from: “earliest”, “latest”, “time”, “sequence”

• shards – number of shards (used to set number of replicas)

• extra_attributes – key/value dict with extra trigger attributes

• ack_window_size – stream ack window size (the consumer group will be updated with the event id - ack_window_size, on failure the events in the window will be retransmitted)

• kwargs – extra V3IOStreamTrigger class attributes

add_volume(local, remote, name='fs', access_key='', user='')

deploy(dashboard='', project='', tag='', verbose=False, auth_info: Optional[mlrun.common.schemas.auth.AuthInfo] = None, builder_env: Optional[dict] = None)

Deploy the nuclio function to the cluster

Parameters

• dashboard – DEPRECATED. Keep empty to allow auto-detection by MLRun API

• project – project name

• tag – function tag

• verbose – set True for verbose logging

• auth_info – service AuthInfo

• builder_env – env vars dict for source archive config/credentials e.g. builder_env={“GIT_TOKEN”: token}

deploy_step(dashboard='', project='', models=None, env=None, tag=None, verbose=None, use_function_from_db=None)

return as a Kubeflow pipeline step (ContainerOp), recommended to use mlrun.deploy_function() instead

Parameters

• dashboard – DEPRECATED. Keep empty to allow auto-detection by MLRun API.

• project – project name, defaults to function project

• models – model name and paths

• env – dict of environment variables

• tag – version tag

• verbose – verbose output

• use_function_from_db – use the function from the DB instead of the local function object

from_image(image)

get_url(force_external_address: bool = False, auth_info: Optional[mlrun.common.schemas.auth.AuthInfo] = None)

This method returns function’s url.

Parameters

• force_external_address – use the external ingress URL

• auth_info – service AuthInfo

Returns

returns function’s url

invoke(path: str, body: Optional[Union[str, bytes, dict]] = None, method: Optional[str] = None, headers: Optional[dict] = None, dashboard: str = '', force_external_address: bool = False, auth_info: Optional[mlrun.common.schemas.auth.AuthInfo] = None, mock: Optional[bool] = None)

Invoke the remote (live) function and return the results

example:

function.invoke("/api", body={"inputs": x})

Parameters

• path – request sub path (e.g. /images)

• body – request body (str, bytes or a dict for json requests)

• method – HTTP method (GET, PUT, ..)

• headers – key/value dict with http headers

• dashboard – nuclio dashboard address

• force_external_address – use the external ingress URL

• auth_info – service AuthInfo

• mock – use mock server vs a real Nuclio function (for local simulations)

kind = 'remote'

set_config(key, value)

property spec: mlrun.runtimes.function.NuclioSpec

property status: mlrun.runtimes.function.NuclioStatus

with_annotations(annotations: dict)

set a key/value annotations for function

with_http(workers: Optional[int] = 8, port: Optional[int] = None, host: Optional[str] = None, paths: Optional[List[str]] = None, canary: Optional[float] = None, secret: Optional[str] = None, worker_timeout: Optional[int] = None, gateway_timeout: Optional[int] = None, trigger_name: Optional[str] = None, annotations: Optional[Mapping[str, str]] = None, extra_attributes: Optional[Mapping[str, str]] = None)

update/add nuclio HTTP trigger settings

Note: gateway timeout is the maximum request time before an error is returned, while the worker timeout if the max time a request will wait for until it will start processing, gateway_timeout must be greater than the worker_timeout.

Parameters

• workers – number of worker processes (default=8). set 0 to use Nuclio’s default workers count

• port – TCP port to listen on. by default, nuclio will choose a random port as long as the function service is NodePort. if the function service is ClusterIP, the port is ignored.

• host – Ingress hostname

• paths – list of Ingress sub paths

• canary – k8s ingress canary (% traffic value between 0 and 100)

• secret – k8s secret name for SSL certificate

• worker_timeout – worker wait timeout in sec (how long a message should wait in the worker queue before an error is returned)

• gateway_timeout – nginx ingress timeout in sec (request timeout, when will the gateway return an error)

• trigger_name – alternative nuclio trigger name

• annotations – key/value dict of ingress annotations

• extra_attributes – key/value dict of extra nuclio trigger attributes

Returns

function object (self)

with_node_selection(**kwargs)

Enables to control on which k8s node the job will run

Parameters

• node_name – The name of the k8s node

• node_selector – Label selector, only nodes with matching labels will be eligible to be picked

• affinity – Expands the types of constraints you can express - see https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity for details

• tolerations – Tolerations are applied to pods, and allow (but do not require) the pods to schedule onto nodes with matching taints - see https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration for details

with_preemption_mode(**kwargs)

Preemption mode controls whether pods can be scheduled on preemptible nodes. Tolerations, node selector, and affinity are populated on preemptible nodes corresponding to the function spec.

The supported modes are:

• allow - The function can be scheduled on preemptible nodes

• constrain - The function can only run on preemptible nodes

• prevent - The function cannot be scheduled on preemptible nodes

• none - No preemptible configuration will be applied on the function

The default preemption mode is configurable in mlrun.mlconf.function_defaults.preemption_mode, by default it’s set to prevent

Parameters

mode – allow | constrain | prevent | none defined in PreemptionModes

with_priority_class(**kwargs)

Enables to control the priority of the pod If not passed - will default to mlrun.mlconf.default_function_priority_class_name

Parameters

name – The name of the priority class

with_service_type(service_type: str, add_templated_ingress_host_mode: Optional[str] = None)

Enables to control the service type of the pod and the addition of templated ingress host

Parameters

• service_type – service type (ClusterIP, NodePort), defaults to mlrun.mlconf.httpdb.nuclio.service_type

• add_templated_ingress_host_mode – add templated ingress host mode (never, always, onClusterIP), see mlrun.mlconf.httpdb.nuclio.add_templated_ingress_host_mode for the default and more information

with_source_archive(source, workdir=None, handler=None, runtime='')

Load nuclio function from remote source

Note: remote source may require credentials, those can be stored in the project secrets or passed in the function.deploy() using the builder_env dict, see the required credentials per source:

• v3io - “V3IO_ACCESS_KEY”.

• git - “GIT_USERNAME”, “GIT_PASSWORD”.

• AWS S3 - “AWS_ACCESS_KEY_ID”, “AWS_SECRET_ACCESS_KEY” or “AWS_SESSION_TOKEN”.

Parameters

• source – a full path to the nuclio function source (code entry) to load the function from

• handler – a path to the function’s handler, including path inside archive/git repo

• workdir – working dir relative to the archive root (e.g. ‘subdir’)

• runtime – (optional) the runtime of the function (defaults to python:3.7)

Examples

git:

fn.with_source_archive("git://github.com/org/repo#my-branch",
        handler="main:handler",
        workdir="path/inside/repo")

s3:

fn.spec.nuclio_runtime = "golang"
fn.with_source_archive("s3://my-bucket/path/in/bucket/my-functions-archive",
    handler="my_func:Handler",
    workdir="path/inside/functions/archive",
    runtime="golang")

with_v3io(local='', remote='')

Add v3io volume to the function

Parameters

• local – local path (mount path inside the function container)

• remote – v3io path

class mlrun.runtimes.RemoteSparkRuntime(spec=None, metadata=None)

Bases: mlrun.runtimes.kubejob.KubejobRuntime

default_image = '.remote-spark-default-image'

deploy(watch=True, with_mlrun=None, skip_deployed=False, is_kfp=False, mlrun_version_specifier=None, builder_env: Optional[dict] = None, show_on_failure: bool = False)

deploy function, build container with dependencies

Parameters

• watch – wait for the deploy to complete (and print build logs)

• 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

• is_kfp – deploy as part of a kfp pipeline

• 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}

• show_on_failure – show logs only in case of build failure

:return True if the function is ready (deployed)

classmethod deploy_default_image()

is_deployed()

check if the function is deployed (has a valid container)

kind = 'remote-spark'

property spec: mlrun.runtimes.remotesparkjob.RemoteSparkSpec

with_security_context(security_context: kubernetes.client.models.v1_security_context.V1SecurityContext)

With security context is not supported for spark runtime. Driver / Executor processes run with uid / gid 1000 as long as security context is not defined. If in the future we want to support setting security context it will work only from spark version 3.2 onwards.

with_spark_service(spark_service, provider='iguazio')

Attach spark service to function

class mlrun.runtimes.ServingRuntime(spec=None, metadata=None)

Bases: mlrun.runtimes.function.RemoteRuntime

MLRun Serving Runtime

add_child_function(name, url=None, image=None, requirements=None, kind=None)

in a multi-function pipeline add child function

example:

fn.add_child_function('enrich', './enrich.ipynb', 'mlrun/mlrun')

Parameters

• name – child function name

• url – function/code url, support .py, .ipynb, .yaml extensions

• image – base docker image for the function

• requirements – py package requirements file path OR list of packages

• kind – mlrun function/runtime kind

:return function object

add_model(key: str, model_path: Optional[str] = None, class_name: Optional[str] = None, model_url: Optional[str] = None, handler: Optional[str] = None, router_step: Optional[str] = None, child_function: Optional[str] = None, **class_args)

add ml model and/or route to the function.

Example, create a function (from the notebook), add a model class, and deploy:

fn = code_to_function(kind='serving')
fn.add_model('boost', model_path, model_class='MyClass', my_arg=5)
fn.deploy()

only works with router topology, for nested topologies (model under router under flow) need to add router to flow and use router.add_route()

Parameters

• key – model api key (or name:version), will determine the relative url/path

• model_path – path to mlrun model artifact or model directory file/object path

• class_name – V2 Model python class name or a model class instance (can also module.submodule.class and it will be imported automatically)

• model_url – url of a remote model serving endpoint (cannot be used with model_path)

• handler – for advanced users!, override default class handler name (do_event)

• router_step – router step name (to determine which router we add the model to in graphs with multiple router steps)

• child_function – child function name, when the model runs in a child function

• class_args – extra kwargs to pass to the model serving class __init__ (can be read in the model using .get_param(key) method)

add_secrets_config_to_spec()

deploy(dashboard='', project='', tag='', verbose=False, auth_info: Optional[mlrun.common.schemas.auth.AuthInfo] = None, builder_env: Optional[dict] = None)

deploy model serving function to a local/remote cluster

Parameters

• dashboard – DEPRECATED. Keep empty to allow auto-detection by MLRun API

• project – optional, override function specified project name

• tag – specify unique function tag (a different function service is created for every tag)

• verbose – verbose logging

• auth_info – The auth info to use to communicate with the Nuclio dashboard, required only when providing dashboard

• builder_env – env vars dict for source archive config/credentials e.g. builder_env={“GIT_TOKEN”: token}

kind = 'serving'

plot(filename=None, format=None, source=None, **kw)

plot/save graph using graphviz

example:

serving_fn = mlrun.new_function("serving", image="mlrun/mlrun", kind="serving")
serving_fn.add_model('my-classifier',model_path=model_path,
                      class_name='mlrun.frameworks.sklearn.SklearnModelServer')
serving_fn.plot(rankdir="LR")

Parameters

• filename – target filepath for the image (None for the notebook)

• format – The output format used for rendering ('pdf', 'png', etc.)

• source – source step to add to the graph

• kw – kwargs passed to graphviz, e.g. rankdir=”LR” (see: https://graphviz.org/doc/info/attrs.html)

Returns

graphviz graph object

remove_states(keys: list)

remove one, multiple, or all states/models from the spec (blank list for all)

set_topology(topology=None, class_name=None, engine=None, exist_ok=False, **class_args) → Union[mlrun.serving.states.RootFlowStep, mlrun.serving.states.RouterStep]

set the serving graph topology (router/flow) and root class or params

examples:

# simple model router topology
graph = fn.set_topology("router")
fn.add_model(name, class_name="ClassifierModel", model_path=model_uri)

# async flow topology
graph = fn.set_topology("flow", engine="async")
graph.to("MyClass").to(name="to_json", handler="json.dumps").respond()

topology options are:

router - root router + multiple child route states/models
         route is usually determined by the path (route key/name)
         can specify special router class and router arguments

flow   - workflow (DAG) with a chain of states
         flow support "sync" and "async" engines, branches are not allowed in sync mode
         when using async mode calling state.respond() will mark the state as the
         one which generates the (REST) call response

Parameters

• topology –

  • graph topology, router or flow

• class_name –

  • optional for router, router class name/path or router object

• engine –

  • optional for flow, sync or async engine (default to async)

• exist_ok –

  • allow overriding existing topology

• class_args –

  • optional, router/flow class init args

:return graph object (fn.spec.graph)

set_tracking(stream_path: Optional[str] = None, batch: Optional[int] = None, sample: Optional[int] = None, stream_args: Optional[dict] = None, tracking_policy: Optional[Union[mlrun.utils.model_monitoring.TrackingPolicy, dict]] = None)

set tracking parameters:

Parameters

• stream_path – Path/url of the tracking stream e.g. v3io:///users/mike/mystream you can use the “dummy://” path for test/simulation.

• batch – Micro batch size (send micro batches of N records at a time).

• sample – Sample size (send only one of N records).

• stream_args – Stream initialization parameters, e.g. shards, retention_in_hours, ..

• tracking_policy –

  Tracking policy object or a dictionary that will be converted into a tracking policy object. By using TrackingPolicy, the user can apply his model monitoring requirements, such as setting the scheduling policy of the model monitoring batch job or changing the image of the model monitoring stream.

  example:

  # initialize a new serving function
  serving_fn = mlrun.import_function("hub://v2-model-server", new_name="serving")
  # apply model monitoring and set monitoring batch job to run every 3 hours
  tracking_policy = {'default_batch_intervals':"0 */3 * * *"}
  serving_fn.set_tracking(tracking_policy=tracking_policy)
  

property spec: mlrun.runtimes.serving.ServingSpec

to_mock_server(namespace=None, current_function='*', track_models=False, workdir=None, **kwargs) → mlrun.serving.server.GraphServer

create mock server object for local testing/emulation

Parameters

• namespace – one or list of namespaces/modules to search the steps classes/functions in

• log_level – log level (error | info | debug)

• current_function – specify if you want to simulate a child function, * for all functions

• track_models – allow model tracking (disabled by default in the mock server)

• workdir – working directory to locate the source code (if not the current one)

with_secrets(kind, source)

register a secrets source (file, env or dict)

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

task.with_secrets('file', 'file.txt')
task.with_secrets('inline', {'key': 'val'})
task.with_secrets('env', 'ENV1,ENV2')
task.with_secrets('vault', ['secret1', 'secret2'...])

# If using an empty secrets list [] then all accessible secrets will be available.
task.with_secrets('vault', [])

# To use with Azure key vault, a k8s secret must be created with the following keys:
# kubectl -n <namespace> create secret generic azure-key-vault-secret \
#     --from-literal=tenant_id=<service principal tenant ID> \
#     --from-literal=client_id=<service principal client ID> \
#     --from-literal=secret=<service principal secret key>

task.with_secrets('azure_vault', {
    'name': 'my-vault-name',
    'k8s_secret': 'azure-key-vault-secret',
    # An empty secrets list may be passed ('secrets': []) to access all vault secrets.
    'secrets': ['secret1', 'secret2'...]
})

Parameters

• kind – secret type (file, inline, env)

• source – secret data or link (see example)

Returns

The Runtime (function) object