Source code for mlrun.runtimes.function

# Copyright 2018 Iguazio
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import asyncio
import json
import typing
from base64 import b64encode
from datetime import datetime
from time import sleep
from urllib.parse import urlparse

import nuclio
import requests
import semver
from aiohttp.client import ClientSession
from kubernetes import client
from nuclio.deploy import find_dashboard_url, get_deploy_status
from nuclio.triggers import V3IOStreamTrigger

import mlrun.errors
from mlrun.datastore import parse_s3_bucket_and_key
from mlrun.db import RunDBError
from mlrun.utils import get_git_username_password_from_token

from ..api.schemas import AuthInfo
from ..config import config as mlconf
from ..k8s_utils import get_k8s_helper
from ..kfpops import deploy_op
from ..lists import RunList
from ..model import RunObject
from ..platforms.iguazio import mount_v3io, parse_path, split_path, v3io_cred
from ..utils import as_number, enrich_image_url, get_in, logger, update_in
from .base import FunctionStatus, RunError
from .constants import NuclioIngressAddTemplatedIngressModes
from .pod import KubeResource, KubeResourceSpec
from .utils import get_item_name, log_std

default_max_replicas = 4

def validate_nuclio_version_compatibility(*min_versions):
    :param min_versions: Valid minimum version(s) required, assuming no 2 versions has equal major and minor.
    parsed_min_versions = [
        semver.VersionInfo.parse(min_version) for min_version in min_versions
        parsed_current_version = semver.VersionInfo.parse(mlconf.nuclio_version)
    except ValueError:
            "Unable to parse nuclio version, assuming compatibility",
        return True

    for parsed_min_version in parsed_min_versions:
        if (
            parsed_current_version.major == parsed_min_version.major
            and parsed_current_version.minor == parsed_min_version.minor
            and parsed_current_version.patch < parsed_min_version.patch
            return False

        if parsed_current_version >= parsed_min_version:
            return True
    return False

def is_nuclio_version_in_range(min_version: str, max_version: str) -> bool:
    Return whether the Nuclio version is in the range, inclusive for min, exclusive for max - [min, max)
        parsed_min_version = semver.VersionInfo.parse(min_version)
        parsed_max_version = semver.VersionInfo.parse(max_version)
        nuclio_version = mlrun.runtimes.utils.resolve_nuclio_version()
        parsed_current_version = semver.VersionInfo.parse(nuclio_version)
    except ValueError:
            "Unable to parse nuclio version, assuming in range",
        return True
    return parsed_min_version <= parsed_current_version < parsed_max_version

def min_nuclio_versions(*versions):
    def decorator(function):
        def wrapper(*args, **kwargs):
            if validate_nuclio_version_compatibility(*versions):
                return function(*args, **kwargs)

            message = (
                f"{function.__name__} is supported since nuclio {' or '.join(versions)}, currently using "
                f"nuclio {mlconf.nuclio_version}, please upgrade."
            raise mlrun.errors.MLRunIncompatibleVersionError(message)

        return wrapper

    return decorator

class NuclioSpec(KubeResourceSpec):
    _dict_fields = KubeResourceSpec._dict_fields + [

    def __init__(


        self.base_spec = base_spec or {}
        self.function_kind = function_kind
        self.source = source or ""
        self.config = config or {}
        self.function_handler = None
        self.nuclio_runtime = None
        self.no_cache = no_cache
        self.readiness_timeout = readiness_timeout

        # TODO: we would prefer to default to 0, but invoking a scaled to zero function requires to either add the
        #  x-nuclio-target header or to create the function with http trigger and invoke the function through it - so
        #  we need to do one of the two
        self.min_replicas = min_replicas or 1
        self.max_replicas = max_replicas or default_max_replicas
        # When True it will set Nuclio spec.noBaseImagesPull to False (negative logic)
        # indicate that the base image should be pulled from the container registry (not cached)
        self.base_image_pull: bool = None

    def generate_nuclio_volumes(self):
        nuclio_volumes = []
        volume_with_volume_mounts_names = set()
        for volume_mount in self._volume_mounts.values():
            volume_name = get_item_name(volume_mount, "name")
            if volume_name not in self._volumes:
                raise ValueError(
                    f"Found volume mount without a volume. name={volume_name}"
                {"volume": self._volumes[volume_name], "volumeMount": volume_mount}

        volumes_without_volume_mounts = (
        if volumes_without_volume_mounts:
            raise ValueError(
                f"Found volumes without volume mounts. names={volumes_without_volume_mounts}"

        return nuclio_volumes

class NuclioStatus(FunctionStatus):
    def __init__(
        super().__init__(state, build_pod)

        self.nuclio_name = nuclio_name

        # exists on nuclio >= 1.6.x
        # infers the function invocation urls
        self.internal_invocation_urls = internal_invocation_urls or []
        self.external_invocation_urls = external_invocation_urls or []

        # still exists for backwards compatability reasons.
        # on latest Nuclio (>= 1.6.x) versions, use external_invocation_urls / internal_invocation_urls instead
        self.address = address

[docs]class RemoteRuntime(KubeResource): kind = "remote" _is_nested = False _mock_server = None @property def spec(self) -> NuclioSpec: return self._spec @spec.setter def spec(self, spec): self._spec = self._verify_dict(spec, "spec", NuclioSpec) @property def status(self) -> NuclioStatus: return self._status @status.setter def status(self, status): self._status = self._verify_dict(status, "status", NuclioStatus)
[docs] def set_config(self, key, value): self.spec.config[key] = value return self
[docs] def with_annotations(self, annotations: dict): """set a key/value annotations for function""" self.spec.base_spec.setdefault("metadata", {}) self.spec.base_spec["metadata"].setdefault("annotations", {}) for key, value in annotations.items(): self.spec.base_spec["metadata"]["annotations"][key] = str(value) return self
[docs] def add_volume(self, local, remote, name="fs", access_key="", user=""): raise Exception("deprecated, use .apply(mount_v3io())")
[docs] def add_trigger(self, name, spec): """add a nuclio trigger object/dict :param name: trigger name :param spec: trigger object or dict """ if hasattr(spec, "to_dict"): spec = spec.to_dict() spec["name"] = name self.spec.config[f"spec.triggers.{name}"] = spec return self
[docs] def with_source_archive( self, 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". :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://", 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") """ = source # update handler in function_handler self.spec.function_handler = handler if workdir: self.spec.workdir = workdir if runtime: self.spec.nuclio_runtime = runtime return self
[docs] def with_v3io(self, local="", remote=""): """Add v3io volume to the function :param local: local path (mount path inside the function container) :param remote: v3io path """ if local and remote: self.apply(mount_v3io(remote=remote, mount_path=local)) else: self.apply(v3io_cred()) return self
[docs] def with_http( self, workers=8, port=0, host=None, paths=None, canary=None, secret=None, worker_timeout: int = None, gateway_timeout: int = None, trigger_name=None, annotations=None, extra_attributes=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. :param workers: number of worker processes (default=8) :param port: TCP port :param host: hostname :param paths: list of sub paths :param canary: k8s ingress canary (% traffic value between 0 to 100) :param secret: k8s secret name for SSL certificate :param worker_timeout: worker wait timeout in sec (how long a message should wait in the worker queue before an error is returned) :param gateway_timeout: nginx ingress timeout in sec (request timeout, when will the gateway return an error) :param trigger_name: alternative nuclio trigger name :param annotations: key/value dict of ingress annotations :param extra_attributes: key/value dict of extra nuclio trigger attributes :return: function object (self) """ annotations = annotations or {} if worker_timeout: gateway_timeout = gateway_timeout or (worker_timeout + 60) if gateway_timeout: if worker_timeout and worker_timeout >= gateway_timeout: raise ValueError( "gateway timeout must be greater than the worker timeout" ) annotations[ "" ] = f"{gateway_timeout}" annotations[ "" ] = f"{gateway_timeout}" annotations[ "" ] = f"{gateway_timeout}" trigger = nuclio.HttpTrigger( workers, port=port, host=host, paths=paths, canary=canary, secret=secret, annotations=annotations, extra_attributes=extra_attributes, ) if worker_timeout: trigger._struct["workerAvailabilityTimeoutMilliseconds"] = ( worker_timeout ) * 1000 self.add_trigger(trigger_name or "http", trigger) return self
[docs] def from_image(self, image): config = nuclio.config.new_config() update_in( config, "spec.handler", self.spec.function_handler or "main:handler", ) update_in(config, "spec.image", image) update_in(config, "", "image") self.spec.base_spec = config
[docs] def add_v3io_stream_trigger( self, 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 :param stream_path: v3io stream path (e.g. 'v3io:///projects/myproj/stream1') :param name: trigger name :param group: consumer group :param seek_to: start seek from: "earliest", "latest", "time", "sequence" :param shards: number of shards (used to set number of replicas) :param extra_attributes: key/value dict with extra trigger attributes :param 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) :param kwargs: extra V3IOStreamTrigger class attributes """ endpoint = None if "://" in stream_path: endpoint, stream_path = parse_path(stream_path, suffix="") container, path = split_path(stream_path) shards = shards or 1 extra_attributes = extra_attributes or {} if ack_window_size: extra_attributes["ackWindowSize"] = ack_window_size self.add_trigger( name, V3IOStreamTrigger( name=name, container=container, path=path[1:], consumerGroup=group, seekTo=seek_to, webapi=endpoint or "http://v3io-webapi:8081", extra_attributes=extra_attributes, readBatchSize=256, **kwargs, ), ) self.spec.min_replicas = shards self.spec.max_replicas = shards
[docs] def add_secrets_config_to_spec(self): # For nuclio functions, we just add the project secrets as env variables. Since there's no MLRun code # to decode the secrets and special env variable names in the function, we just use the same env variable as # the key name (encode_key_names=False) self._add_project_k8s_secrets_to_spec( None, project=self.metadata.project, encode_key_names=False )
[docs] def deploy( self, dashboard="", project="", tag="", verbose=False, auth_info: AuthInfo = None, builder_env: dict = None, ): """Deploy the nuclio function to the cluster :param dashboard: address of the nuclio dashboard service (keep blank for current cluster) :param project: project name :param tag: function tag :param verbose: set True for verbose logging :param auth_info: service AuthInfo :param builder_env: env vars dict for source archive config/credentials e.g. builder_env={"GIT_TOKEN": token} """ # todo: verify that the function name is normalized verbose = verbose or self.verbose if verbose: self.set_env("MLRUN_LOG_LEVEL", "DEBUG") if project: self.metadata.project = project if tag: self.metadata.tag = tag save_record = False if not dashboard: # Attempt auto-mounting, before sending to remote build self.try_auto_mount_based_on_config() self.fill_credentials() db = self._get_db()"Starting remote function deploy") data = db.remote_builder(self, False, builder_env=builder_env) self.status = data["data"].get("status") self._update_credentials_from_remote_build(data["data"]) self._wait_for_function_deployment(db, verbose=verbose) # NOTE: on older mlrun versions & nuclio versions, function are exposed via NodePort # now, functions can be not exposed (using service type ClusterIP) and hence # for BC we first try to populate the external invocation url, and then # if not exists, take the internal invocation url if self.status.external_invocation_urls: self.spec.command = f"http://{self.status.external_invocation_urls[0]}" save_record = True elif self.status.internal_invocation_urls: self.spec.command = f"http://{self.status.internal_invocation_urls[0]}" save_record = True elif self.status.address: self.spec.command = f"http://{self.status.address}" save_record = True else: # todo: should be deprecated (only work via MLRun service) self._ensure_run_db() internal_invocation_urls, external_invocation_urls = deploy_nuclio_function( self, dashboard=dashboard, watch=True, auth_info=auth_info, ) self.status.internal_invocation_urls = internal_invocation_urls self.status.external_invocation_urls = external_invocation_urls # save the (first) function external invocation url # this is made for backwards compatability because the user, at this point, may # work remotely and need the external invocation url on the spec.command # TODO: when using `ClusterIP`, this block might not fulfilled # as long as function doesnt have ingresses if self.status.external_invocation_urls: address = self.status.external_invocation_urls[0] self.spec.command = f"http://{address}" self.status.state = "ready" self.status.address = address save_record = True "successfully deployed function", internal_invocation_urls=self.status.internal_invocation_urls, external_invocation_urls=self.status.external_invocation_urls, ) if save_record: return self.spec.command
def _wait_for_function_deployment(self, db, verbose=False): text = "" state = "" last_log_timestamp = 1 while state not in ["ready", "error", "unhealthy"]: sleep(1) try: text, last_log_timestamp = db.get_builder_status( self, last_log_timestamp=last_log_timestamp, verbose=verbose ) except RunDBError: raise ValueError("function or deploy process not found") state = self.status.state if text: print(text) if state != "ready": logger.error("Nuclio function failed to deploy", function_state=state) raise RunError(f"function {} deployment failed")
[docs] @min_nuclio_versions("1.5.20", "1.6.10") def with_node_selection( self, node_name: typing.Optional[str] = None, node_selector: typing.Optional[typing.Dict[str, str]] = None, affinity: typing.Optional[client.V1Affinity] = None, tolerations: typing.Optional[typing.List[client.V1Toleration]] = None, ): """k8s node selection attributes""" if tolerations and not validate_nuclio_version_compatibility("1.7.5"): raise mlrun.errors.MLRunIncompatibleVersionError( "tolerations are only supported from nuclio version 1.7.5" ) super().with_node_selection(node_name, node_selector, affinity, tolerations)
[docs] @min_nuclio_versions("1.8.6") def with_preemption_mode(self, mode): """ 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** :param mode: allow | constrain | prevent | none defined in :py:class:`~mlrun.api.schemas.PreemptionModes` """ super().with_preemption_mode(mode=mode)
[docs] @min_nuclio_versions("1.6.18") def with_priority_class(self, name: typing.Optional[str] = None): """k8s priority class""" super().with_priority_class(name)
def _get_state( self, dashboard="", last_log_timestamp=0, verbose=False, raise_on_exception=True, resolve_address=True, auth_info: AuthInfo = None, ) -> typing.Tuple[str, str, typing.Optional[float]]: if dashboard: ( state, address, name, last_log_timestamp, text, function_status, ) = get_nuclio_deploy_status(, self.metadata.project, self.metadata.tag, dashboard, last_log_timestamp=last_log_timestamp, verbose=verbose, resolve_address=resolve_address, auth_info=auth_info, ) self.status.internal_invocation_urls = function_status.get( "internalInvocationUrls", [] ) self.status.external_invocation_urls = function_status.get( "externalInvocationUrls", [] ) self.status.state = state self.status.nuclio_name = name if address: self.status.address = address self.spec.command = f"http://{address}" return state, text, last_log_timestamp try: text, last_log_timestamp = self._get_db().get_builder_status( self, last_log_timestamp=last_log_timestamp, verbose=verbose ) except RunDBError: if raise_on_exception: return "", "", None raise ValueError("function or deploy process not found") return self.status.state, text, last_log_timestamp def _get_runtime_env(self): # for runtime specific env var enrichment (before deploy) runtime_env = { "MLRUN_DEFAULT_PROJECT": self.metadata.project or mlconf.default_project, } if self.spec.rundb or mlconf.httpdb.api_url: runtime_env["MLRUN_DBPATH"] = self.spec.rundb or mlconf.httpdb.api_url if mlconf.namespace: runtime_env["MLRUN_NAMESPACE"] = mlconf.namespace if self.metadata.credentials.access_key: runtime_env[ mlrun.runtimes.constants.FunctionEnvironmentVariables.auth_session ] = self.metadata.credentials.access_key return runtime_env def _get_nuclio_config_spec_env(self): env_dict = {} external_source_env_dict = {} api = client.ApiClient() for env_var in self.spec.env: # sanitize env if not sanitized if isinstance(env_var, dict): sanitized_env_var = env_var else: sanitized_env_var = api.sanitize_for_serialization(env_var) value = sanitized_env_var.get("value") if value is not None: env_dict[sanitized_env_var.get("name")] = value continue value_from = sanitized_env_var.get("valueFrom") if value_from is not None: external_source_env_dict[sanitized_env_var.get("name")] = value_from for key, value in self._get_runtime_env().items(): env_dict[key] = value return env_dict, external_source_env_dict
[docs] def deploy_step( self, 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""" models = {} if models is None else models function_name = or "function" name = f"deploy_{function_name}" project = project or self.metadata.project if models and isinstance(models, dict): models = [{"key": k, "model_path": v} for k, v in models.items()] # verify auto mount is applied (with the client credentials) self.try_auto_mount_based_on_config() # if the function spec contain KFP PipelineParams (futures) pass the full spec to the # ContainerOp this way KFP will substitute the params with previous step outputs func_has_pipeline_params = self.to_json().find("{{pipelineparam:op") > 0 if ( use_function_from_db or use_function_from_db is None and not func_has_pipeline_params ): url =, refresh=True) else: url = None return deploy_op( name, self, func_url=url, dashboard=dashboard, project=project, models=models, env=env, tag=tag, verbose=verbose, )
[docs] def invoke( self, path: str, body: typing.Union[str, bytes, dict] = None, method: str = None, headers: dict = None, dashboard: str = "", force_external_address: bool = False, auth_info: AuthInfo = None, ): """Invoke the remote (live) function and return the results example:: function.invoke("/api", body={"inputs": x}) :param path: request sub path (e.g. /images) :param body: request body (str, bytes or a dict for json requests) :param method: HTTP method (GET, PUT, ..) :param headers: key/value dict with http headers :param dashboard: nuclio dashboard address :param force_external_address: use the external ingress URL :param auth_info: service AuthInfo """ if not method: method = "POST" if body else "GET" if "://" not in path: if not self.status.address: state, _, _ = self._get_state(dashboard, auth_info=auth_info) if state != "ready" or not self.status.address: raise ValueError( "no function address or not ready, first run .deploy()" ) path = self._resolve_invocation_url(path, force_external_address) if headers is None: headers = {} # if function is scaled to zero, let the DLX know we want to wake it up full_function_name = get_fullname(, self.metadata.project, self.metadata.tag ) headers.setdefault("x-nuclio-target", full_function_name) kwargs = {} if body: if isinstance(body, (str, bytes)): kwargs["data"] = body else: kwargs["json"] = body try:"invoking function", method=method, path=path) resp = requests.request(method, path, headers=headers, **kwargs) except OSError as err: raise OSError(f"error: cannot run function at url {path}, {err}") if not resp.ok: raise RuntimeError(f"bad function response {resp.status_code}: {resp.text}") data = resp.content if resp.headers["content-type"] == "application/json": data = json.loads(data) return data
def _pre_run_validations(self): if self.spec.function_kind != "mlrun": raise RunError( '.run() can only be execute on "mlrun" kind' ', recreate with function kind "mlrun"' ) state = self.status.state if state != "ready": if state: raise RunError(f"cannot run, function in state {state}") state, _, _ = self._get_state(raise_on_exception=True) if state != "ready":"starting nuclio build!") self.deploy() def _run(self, runobj: RunObject, execution): self._pre_run_validations() self.store_run(runobj) if self._secrets: runobj.spec.secret_sources = self._secrets.to_serial() log_level = execution.log_level command = self.spec.command if runobj.spec.handler: command = f"{command}/{runobj.spec.handler_name}" headers = {"x-nuclio-log-level": log_level} try: resp = requests.put(command, json=runobj.to_dict(), headers=headers) except OSError as err: logger.error(f"error invoking function: {err}") raise OSError(f"error: cannot run function at url {command}") if not resp.ok: logger.error(f"bad function resp!!\n{resp.text}") raise RunError("bad function response") logs = resp.headers.get("X-Nuclio-Logs") if logs: log_std(self._db_conn, runobj, parse_logs(logs)) return self._update_state(resp.json()) def _run_many(self, generator, execution, runobj: RunObject): self._pre_run_validations() tasks = generator.generate(runobj) secrets = self._secrets.to_serial() if self._secrets else None log_level = execution.log_level headers = {"x-nuclio-log-level": log_level} command = self.spec.command if runobj.spec.handler: command = f"{command}/{runobj.spec.handler_name}" loop = asyncio.get_event_loop() future = asyncio.ensure_future( self._invoke_async(tasks, command, headers, secrets, generator=generator) ) loop.run_until_complete(future) return future.result() def _update_state(self, rundict: dict): last_state = get_in(rundict, "status.state", "") if last_state != "error": update_in(rundict, "status.state", "completed") self._store_run_dict(rundict) return rundict async def _invoke_async(self, tasks, url, headers, secrets, generator): results = RunList() runs = [] num_errors = 0 stop = False parallel_runs = generator.options.parallel_runs or 1 semaphore = asyncio.Semaphore(parallel_runs) async with ClientSession() as session: for task in tasks: # TODO: store run using async calls to improve performance self.store_run(task) task.spec.secret_sources = secrets or [] resp = submit(session, url, task, semaphore, headers=headers) runs.append( asyncio.ensure_future( resp, ) ) for result in asyncio.as_completed(runs): status, resp, logs, task = await result if status != 200: err_message = f"failed to access {url} - {resp}" # TODO: store logs using async calls to improve performance log_std( self._db_conn, task, parse_logs(logs) if logs else None, err_message, silent=True, ) # TODO: update run using async calls to improve performance results.append(self._update_run_state(task=task, err=err_message)) num_errors += 1 else: if logs: log_std(self._db_conn, task, parse_logs(logs)) resp = self._update_run_state(json.loads(resp)) state = get_in(resp, "status.state", "") if state == "error": num_errors += 1 results.append(resp) run_results = get_in(resp, "status.results", {}) stop = generator.eval_stop_condition(run_results) if stop: f"reached early stop condition ({generator.options.stop_condition}), stopping iterations!" ) break if num_errors > generator.max_errors: logger.error("max errors reached, stopping iterations!") stop = True break if stop: for task in runs: task.cancel() return results def _resolve_invocation_url(self, path, force_external_address): if path.startswith("/"): path = path[1:] # internal / external invocation urls is a nuclio >= 1.6.x feature # try to infer the invocation url from the internal and if not exists, use external. # $$$$ we do not want to use the external invocation url (e.g.: ingress, nodePort, etc.) if ( not force_external_address and self.status.internal_invocation_urls and get_k8s_helper( silent=True, log=False ).is_running_inside_kubernetes_cluster() ): return f"http://{self.status.internal_invocation_urls[0]}/{path}" if self.status.external_invocation_urls: return f"http://{self.status.external_invocation_urls[0]}/{path}" else: return f"http://{self.status.address}/{path}" def _update_credentials_from_remote_build(self, remote_data): self.metadata.credentials = remote_data.get("metadata", {}).get( "credentials", {} ) credentials_env_var_names = ["V3IO_ACCESS_KEY", "MLRUN_AUTH_SESSION"] new_env = [] # the env vars in the local spec and remote spec are in the format of a list of dicts # e.g.: # env = [ # { # "name": "V3IO_ACCESS_KEY", # "value": "some-value" # }, # ... # ] # remove existing credentials env vars for env in self.spec.env: if isinstance(env, dict): env_name = env["name"] elif isinstance(env, client.V1EnvVar): env_name = else: continue if env_name not in credentials_env_var_names: new_env.append(env) # add credentials env vars from remote build for remote_env in remote_data.get("spec", {}).get("env", []): if remote_env.get("name") in credentials_env_var_names: new_env.append(remote_env) self.spec.env = new_env def _set_as_mock(self, enable): # todo: create mock_server for Nuclio if enable: raise NotImplementedError( "Mock (simulation) is currently not supported for Nuclio, Turn off the mock (mock=False) " "and make sure Nuclio is installed for real deployment to Nuclio" )
def parse_logs(logs): logs = json.loads(logs) lines = "" for line in logs: extra = [] for key, value in line.items(): if key not in ["time", "level", "name", "message"]: extra.append(f"{key}={value}") extra = ", ".join(extra) time = datetime.fromtimestamp(float(line["time"]) / 1000).strftime( "%Y-%m-%d %H:%M:%S.%f" ) lines += f"{time} {line['level']:<6} {line['message']} {extra}\n" return lines async def submit(session, url, run, semaphore, headers=None): async with semaphore: async with session.put(url, json=run.to_dict(), headers=headers) as response: text = await response.text() logs = response.headers.get("X-Nuclio-Logs", None) return response.status, text, logs, run def fake_nuclio_context(body, headers=None): return nuclio.Context(), nuclio.Event(body=body, headers=headers) def _fullname(project, name): if project: return f"{project}-{name}" return name def get_fullname(name, project, tag): if project: name = f"{project}-{name}" if tag and tag != "latest": name = f"{name}-{tag}" return name def deploy_nuclio_function( function: RemoteRuntime, dashboard="", watch=False, auth_info: AuthInfo = None, client_version: str = None, builder_env: dict = None, ): dashboard = dashboard or mlconf.nuclio_dashboard_url function_name, project_name, function_config = compile_function_config( function, client_version, builder_env or {}, auth_info=auth_info ) # if mode allows it, enrich function http trigger with an ingress enrich_function_with_ingress( function_config, mlconf.httpdb.nuclio.add_templated_ingress_host_mode, mlconf.httpdb.nuclio.default_service_type, ) return nuclio.deploy.deploy_config( function_config, dashboard_url=dashboard, name=function_name, project=project_name, tag=function.metadata.tag, verbose=function.verbose, create_new=True, watch=watch, return_address_mode=nuclio.deploy.ReturnAddressModes.all, auth_info=auth_info.to_nuclio_auth_info() if auth_info else None, ) def resolve_function_ingresses(function_spec): http_trigger = resolve_function_http_trigger(function_spec) if not http_trigger: return [] ingresses = [] for _, ingress_config in ( http_trigger.get("attributes", {}).get("ingresses", {}).items() ): ingresses.append(ingress_config) return ingresses def resolve_function_http_trigger(function_spec): for trigger_name, trigger_config in function_spec.get("triggers", {}).items(): if trigger_config.get("kind") != "http": continue return trigger_config def compile_function_config( function: RemoteRuntime, client_version: str = None, builder_env=None, auth_info=None, ): labels = function.metadata.labels or {} labels.update({"mlrun/class": function.kind}) for key, value in labels.items(): function.set_config(f"metadata.labels.{key}", value) # Add secret configurations to function's pod spec, if secret sources were added. # Needs to be here, since it adds env params, which are handled in the next lines. # This only needs to run if we're running within k8s context. If running in Docker, for example, skip. if get_k8s_helper(silent=True).is_running_inside_kubernetes_cluster(): function.add_secrets_config_to_spec() env_dict, external_source_env_dict = function._get_nuclio_config_spec_env() nuclio_runtime = ( function.spec.nuclio_runtime or mlrun.config.config.default_nuclio_runtime ) if is_nuclio_version_in_range("0.0.0", "1.6.0") and nuclio_runtime in [ "python:3.7", "python:3.8", ]: nuclio_runtime_set_from_spec = nuclio_runtime == function.spec.nuclio_runtime if nuclio_runtime_set_from_spec: raise mlrun.errors.MLRunInvalidArgumentError( f"Nuclio version does not support the configured runtime: {nuclio_runtime}" ) else: # our default is python:3.7, simply set it to python:3.6 to keep supporting envs with old Nuclio nuclio_runtime = "python:3.6" # In nuclio 1.6.0<=v<1.8.0 python 3.7 and 3.8 runtime default behavior was to not decode event strings # Our code is counting on the strings to be decoded, so add the needed env var for those versions if ( nuclio_runtime in ["python:3.7", "python:3.8", "python"] and is_nuclio_version_in_range("1.6.0", "1.8.0") and "NUCLIO_PYTHON_DECODE_EVENT_STRINGS" not in env_dict ): env_dict["NUCLIO_PYTHON_DECODE_EVENT_STRINGS"] = "true" nuclio_spec = nuclio.ConfigSpec( env=env_dict, external_source_env=external_source_env_dict, config=function.spec.config, ) nuclio_spec.cmd = or [] project = function.metadata.project or "default" tag = function.metadata.tag handler = function.spec.function_handler if _compile_nuclio_archive_config( nuclio_spec, function, builder_env, project, auth_info=auth_info ) nuclio_spec.set_config("spec.runtime", nuclio_runtime) # In Nuclio >= 1.6.x default serviceType has changed to "ClusterIP". nuclio_spec.set_config( "spec.serviceType", mlconf.httpdb.nuclio.default_service_type ) if function.spec.readiness_timeout: nuclio_spec.set_config( "spec.readinessTimeoutSeconds", function.spec.readiness_timeout ) if function.spec.resources: nuclio_spec.set_config("spec.resources", function.spec.resources) if function.spec.no_cache: nuclio_spec.set_config("", True) if nuclio_spec.set_config( "", ) # the corresponding attribute for build.secret in nuclio is imagePullSecrets, attached link for reference # if nuclio_spec.set_config("spec.imagePullSecrets", if function.spec.base_image_pull: nuclio_spec.set_config("", False) # don't send node selections if nuclio is not compatible if validate_nuclio_version_compatibility("1.5.20", "1.6.10"): if function.spec.node_selector: nuclio_spec.set_config("spec.nodeSelector", function.spec.node_selector) if function.spec.node_name: nuclio_spec.set_config("spec.nodeName", function.spec.node_name) if function.spec.affinity: nuclio_spec.set_config( "spec.affinity", mlrun.runtimes.pod.get_sanitized_attribute(function.spec, "affinity"), ) # don't send tolerations if nuclio is not compatible if validate_nuclio_version_compatibility("1.7.5"): if function.spec.tolerations: nuclio_spec.set_config( "spec.tolerations", mlrun.runtimes.pod.get_sanitized_attribute( function.spec, "tolerations" ), ) # don't send preemption_mode if nuclio is not compatible if validate_nuclio_version_compatibility("1.8.6"): if function.spec.preemption_mode: nuclio_spec.set_config( "spec.PreemptionMode", function.spec.preemption_mode, ) # don't send default or any priority class name if nuclio is not compatible if ( function.spec.priority_class_name and validate_nuclio_version_compatibility("1.6.18") and len(mlconf.get_valid_function_priority_class_names()) ): nuclio_spec.set_config( "spec.priorityClassName", function.spec.priority_class_name ) if function.spec.replicas: nuclio_spec.set_config( "spec.minReplicas", as_number("spec.Replicas", function.spec.replicas) ) nuclio_spec.set_config( "spec.maxReplicas", as_number("spec.Replicas", function.spec.replicas) ) else: nuclio_spec.set_config( "spec.minReplicas", as_number("spec.minReplicas", function.spec.min_replicas), ) nuclio_spec.set_config( "spec.maxReplicas", as_number("spec.maxReplicas", function.spec.max_replicas), ) if function.spec.service_account: nuclio_spec.set_config("spec.serviceAccount", function.spec.service_account) if function.spec.security_context: nuclio_spec.set_config( "spec.securityContext", mlrun.runtimes.pod.get_sanitized_attribute( function.spec, "security_context" ), ) if ( function.spec.base_spec or or or function.kind == mlrun.runtimes.RuntimeKinds.serving # serving can be empty ): config = function.spec.base_spec if not config: # if base_spec was not set (when not using code_to_function) and we have base64 code # we create the base spec with essential attributes config = nuclio.config.new_config() update_in(config, "spec.handler", handler or "main:handler") config = nuclio.config.extend_config( config, nuclio_spec, tag, ) update_in(config, "", update_in(config, "spec.volumes", function.spec.generate_nuclio_volumes()) base_image = ( get_in(config, "") or function.spec.image or ) if base_image: update_in( config, "", enrich_image_url(base_image, client_version), )"deploy started") name = get_fullname(, project, tag) function.status.nuclio_name = name update_in(config, "", name) if function.kind == mlrun.runtimes.RuntimeKinds.serving and not get_in( config, "" ): if not # set the source to the mlrun serving wrapper body = mlrun.runtimes.serving.serving_subkind ) update_in( config, "", b64encode(body.encode("utf-8")).decode("utf-8"), ) elif not function.spec.function_handler: # point the nuclio function handler to mlrun serving wrapper handlers update_in( config, "spec.handler", "mlrun.serving.serving_wrapper:handler", ) else: # todo: should be deprecated (only work via MLRun service) # this may also be called in case of using single file code_to_function(embed_code=False) # this option need to be removed or be limited to using remote files (this code runs in server) name, config, code = nuclio.build_file( function.spec.source,, project=project, handler=handler, tag=tag, spec=nuclio_spec, kind=function.spec.function_kind, verbose=function.verbose, ) update_in(config, "spec.volumes", function.spec.generate_nuclio_volumes()) base_image = function.spec.image or if base_image: update_in( config, "", enrich_image_url(base_image, client_version), ) name = get_fullname(name, project, tag) function.status.nuclio_name = name update_in(config, "", name) return name, project, config def enrich_function_with_ingress(config, mode, service_type): # do not enrich with an ingress if mode == NuclioIngressAddTemplatedIngressModes.never: return ingresses = resolve_function_ingresses(config["spec"]) # function has ingresses already, nothing to add / enrich if ingresses: return # if exists, get the http trigger the function has # we would enrich it with an ingress http_trigger = resolve_function_http_trigger(config["spec"]) if not http_trigger: # function has an HTTP trigger without an ingress # TODO: read from nuclio-api frontend-spec http_trigger = { "kind": "http", "name": "http", "maxWorkers": 1, "workerAvailabilityTimeoutMilliseconds": 10000, # 10 seconds "attributes": {}, } def enrich(): http_trigger.setdefault("attributes", {}).setdefault("ingresses", {})["0"] = { "paths": ["/"], # this would tell Nuclio to use its default ingress host template # and would auto assign a host for the ingress "hostTemplate": "@nuclio.fromDefault", } http_trigger["attributes"]["serviceType"] = service_type config["spec"].setdefault("triggers", {})[http_trigger["name"]] = http_trigger if mode == NuclioIngressAddTemplatedIngressModes.always: enrich() elif mode == NuclioIngressAddTemplatedIngressModes.on_cluster_ip: # service type is not cluster ip, bail out if service_type and service_type.lower() != "clusterip": return enrich() def get_nuclio_deploy_status( name, project, tag, dashboard="", last_log_timestamp=0, verbose=False, resolve_address=True, auth_info: AuthInfo = None, ): api_address = find_dashboard_url(dashboard or mlconf.nuclio_dashboard_url) name = get_fullname(name, project, tag) state, address, last_log_timestamp, outputs, function_status = get_deploy_status( api_address, name, last_log_timestamp, verbose, resolve_address, return_function_status=True, auth_info=auth_info.to_nuclio_auth_info() if auth_info else None, ) text = "\n".join(outputs) if outputs else "" return state, address, name, last_log_timestamp, text, function_status def _compile_nuclio_archive_config( nuclio_spec, function: RemoteRuntime, builder_env, project=None, auth_info=None, ): secrets = {} if project and get_k8s_helper(silent=True).is_running_inside_kubernetes_cluster(): secrets = get_k8s_helper().get_project_secret_data(project) def get_secret(key): return builder_env.get(key) or secrets.get(key, "") source = parsed_url = urlparse(source) code_entry_type = "" if source.startswith("s3://"): code_entry_type = "s3" if source.startswith("git://"): code_entry_type = "git" for archive_prefix in ["http://", "https://", "v3io://", "v3ios://"]: if source.startswith(archive_prefix): code_entry_type = "archive" if code_entry_type == "": raise mlrun.errors.MLRunInvalidArgumentError( "Couldn't resolve code entry type from source" ) code_entry_attributes = {} # resolve work_dir and handler work_dir, handler = _resolve_work_dir_and_handler(function.spec.function_handler) work_dir = function.spec.workdir or work_dir if work_dir != "": code_entry_attributes["workDir"] = work_dir # archive if code_entry_type == "archive": v3io_access_key = builder_env.get("V3IO_ACCESS_KEY", "") if source.startswith("v3io"): if not parsed_url.netloc: source = mlrun.mlconf.v3io_api + parsed_url.path else: source = f"http{source[len('v3io'):]}" if auth_info and not v3io_access_key: v3io_access_key = auth_info.data_session or auth_info.access_key if v3io_access_key: code_entry_attributes["headers"] = {"X-V3io-Session-Key": v3io_access_key} # s3 if code_entry_type == "s3": bucket, item_key = parse_s3_bucket_and_key(source) code_entry_attributes["s3Bucket"] = bucket code_entry_attributes["s3ItemKey"] = item_key code_entry_attributes["s3AccessKeyId"] = get_secret("AWS_ACCESS_KEY_ID") code_entry_attributes["s3SecretAccessKey"] = get_secret("AWS_SECRET_ACCESS_KEY") code_entry_attributes["s3SessionToken"] = get_secret("AWS_SESSION_TOKEN") # git if code_entry_type == "git": # change git:// to https:// as nuclio expects it to be if source.startswith("git://"): source = source.replace("git://", "https://") source, reference, branch = _resolve_git_reference_from_source(source) if not branch and not reference: raise mlrun.errors.MLRunInvalidArgumentError( "git branch or refs must be specified in the source e.g.: " "'git://<url>/org/repo.git#<branch-name or refs/heads/..>'" ) if reference: code_entry_attributes["reference"] = reference if branch: code_entry_attributes["branch"] = branch password = get_secret("GIT_PASSWORD") username = get_secret("GIT_USERNAME") token = get_secret("GIT_TOKEN") if token: username, password = get_git_username_password_from_token(token) code_entry_attributes["username"] = username code_entry_attributes["password"] = password # populate spec with relevant fields nuclio_spec.set_config("spec.handler", handler) nuclio_spec.set_config("", source) nuclio_spec.set_config("", code_entry_type) nuclio_spec.set_config("", code_entry_attributes) def _resolve_git_reference_from_source(source): # kaniko allow multiple "#" e.g. #refs/..#commit split_source = source.split("#", 1) # no reference was passed if len(split_source) < 2: return source, "", "" reference = split_source[1] if reference.startswith("refs/"): return split_source[0], reference, "" return split_source[0], "", reference def _resolve_work_dir_and_handler(handler): """ Resolves a nuclio function working dir and handler inside an archive/git repo :param handler: a path describing working dir and handler of a nuclio function :return: (working_dir, handler) tuple, as nuclio expects to get it Example: ("a/b/c#main:Handler") -> ("a/b/c", "main:Handler") """ def extend_handler(base_handler): # return default handler and module if not specified if not base_handler: return "main:handler" if ":" not in base_handler: base_handler = f"{base_handler}:handler" return base_handler if not handler: return "", "main:handler" split_handler = handler.split("#") if len(split_handler) == 1: return "", extend_handler(handler) return split_handler[0], extend_handler(split_handler[1])