mlrun.runtimes#

class mlrun.runtimes.BaseRuntime(metadata=None, spec=None)[source]#

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)[source]#

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 (dict of key: path)
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

KubeFlow containerOp

doc()[source]#

export(target='', format='.yaml', secrets=None, strip=True)[source]#

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

fill_credentials()[source]#

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

is_deployed()[source]#

kind = 'base'#

property metadata: mlrun.model.BaseMetadata#

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

Run a local or remote task.

Parameters

runspec – run template object or dict (see RunTemplate)
handler – pointer or name of a function handler
name – execution name
project – project name
params – input parameters (dict)
inputs – input objects (dict of key: path)
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/v3.6.3/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 hyper parameter 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 dont plan on changing the build config between runs

Returns

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

save(tag='', versioned=False, refresh=False) → str[source]#

set_db_connection(conn, is_api=False)[source]#

set_label(key, value)[source]#

property spec: mlrun.runtimes.base.FunctionSpec#

property status: mlrun.runtimes.base.FunctionStatus#

store_run(runobj: mlrun.model.RunObject)[source]#

to_dict(fields=None, exclude=None, strip=False)[source]#: convert the object to a python dictionary

try_auto_mount_based_on_config()[source]#

property uri#

validate_and_enrich_service_account(allowed_service_account, default_service_account)[source]#

verify_base_image()[source]#

with_code(from_file='', body=None, with_doc=True)[source]#

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_requirements(requirements: Union[str, List[str]])[source]#

add package requirements from file or list to build spec.

Parameters: requirements – python requirements file path or list of packages
Returns: function object

class mlrun.runtimes.DaskCluster(spec=None, metadata=None)[source]#

Bases: mlrun.runtimes.kubejob.KubejobRuntime

property client#

close(running=True)[source]#

cluster()[source]#

deploy(watch=True, with_mlrun=None, skip_deployed=False, is_kfp=False, mlrun_version_specifier=None, show_on_failure: bool = False)[source]#

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
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()[source]#

gpus(gpus, gpu_type='nvidia.com/gpu')[source]#

property initialized#

is_deployed()[source]#: check if the function is deployed (have 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')[source]#: 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)[source]#: 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)[source]#: 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)[source]#: 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)[source]#: 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)[source]#: 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)[source]#

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

kind = 'handler'#

class mlrun.runtimes.KubejobRuntime(spec=None, metadata=None)[source]#

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)[source]#

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

builder_status(watch=True, logs=True)[source]#

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[source]#

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
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)[source]#

is_deployed()[source]#: check if the function is deployed (have a valid container)

kind = 'job'#

with_source_archive(source, workdir=None, handler=None, pull_at_runtime=True)[source]#

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 or absolute (e.g. ‘./subdir’)
pull_at_runtime – load the archive into the container at job runtime vs on build/deploy

class mlrun.runtimes.LocalRuntime(metadata=None, spec=None)[source]#

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

is_deployed()[source]#

kind = 'local'#

property spec: mlrun.runtimes.local.LocalFunctionSpec#

to_job(image='')[source]#

with_source_archive(source, workdir=None, handler=None, target_dir=None)[source]#

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 or absolute (e.g. ‘./subdir’)
target_dir – local target dir for repo clone (by default its <current-dir>/code)

class mlrun.runtimes.RemoteRuntime(spec=None, metadata=None)[source]#

Bases: mlrun.runtimes.pod.KubeResource

add_secrets_config_to_spec()[source]#

add_trigger(name, spec)[source]#

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)[source]#

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='')[source]#

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

Deploy the nuclio function to the cluster

Parameters

dashboard – address of the nuclio dashboard service (keep blank for current cluster)
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)[source]#: return as a Kubeflow pipeline step (ContainerOp), recommended to use mlrun.deploy_function() instead

from_image(image)[source]#

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.api.schemas.auth.AuthInfo] = None)[source]#

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

kind = 'remote'#

set_config(key, value)[source]#

property spec: mlrun.runtimes.function.NuclioSpec#

property status: mlrun.runtimes.function.NuclioStatus#

with_http(workers=8, port=0, host=None, paths=None, canary=None, secret=None, worker_timeout: Optional[int] = None, gateway_timeout: Optional[int] = None, trigger_name=None, annotations=None, extra_attributes=None)[source]#

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)
port – TCP port
host – hostname
paths – list of sub paths
canary – k8s ingress canary (% traffic value between 0 to 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)[source]#

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)[source]#

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)[source]#

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_source_archive(source, workdir=None, handler=None, runtime='')[source]#

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”.

param source

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

param handler

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

param workdir

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

param 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='')[source]#

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)[source]#

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, show_on_failure: bool = False)[source]#

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
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()[source]#

is_deployed()[source]#: check if the function is deployed (have a valid container)

kind = 'remote-spark'#

property spec: mlrun.runtimes.remotesparkjob.RemoteSparkSpec#

with_security_context(security_context: kubernetes.client.models.v1_security_context.V1SecurityContext)[source]#: 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')[source]#: Attach spark service to function

class mlrun.runtimes.ServingRuntime(spec=None, metadata=None)[source]#

Bases: mlrun.runtimes.function.RemoteRuntime

MLRun Serving Runtime

add_child_function(name, url=None, image=None, requirements=None, kind=None)[source]#

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)[source]#

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()[source]#

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

deploy model serving function to a local/remote cluster

Parameters

dashboard – remote nuclio dashboard url (blank for local or auto detection)
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)[source]#

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)[source]#: 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][source]#

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)[source]#

set tracking stream 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, ..

property spec: mlrun.runtimes.serving.ServingSpec#

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

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)[source]#

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