Source code for dkube.sdk.rsrcs.serving

from __future__ import print_function

import sys
import time
from pprint import pprint

from dkube.sdk.internal import dkube_api
from dkube.sdk.internal.dkube_api.models.custom_container_model import \
    CustomContainerModel
from dkube.sdk.internal.dkube_api.models.custom_container_model_image import \
    CustomContainerModelImage
from dkube.sdk.internal.dkube_api.models.inference_job_model import \
    InferenceJobModel
from dkube.sdk.internal.dkube_api.models.job_model import JobModel
from dkube.sdk.internal.dkube_api.models.job_model_parameters import \
    JobModelParameters
from dkube.sdk.internal.dkube_api.models.job_model_parameters_run import \
    JobModelParametersRun
from dkube.sdk.internal.dkube_api.rest import ApiException

from .util import *


[docs]class DkubeServing(object): """ This class defines Model Deployment with helper functions to set properties of Model Deployment.:: from dkube.sdk import * serving = DkubeServing("oneconv", name="mnist-serving") Where first argument is the user of the Model Deployment. User should be a valid onboarded user in dkube. """ def __init__(self, user, name=generate('serving'), description='', tags=[]): self.predictor_container = CustomContainerModelImage( path='', username=None, password=None, runas=None) self.predictor = CustomContainerModel(image=None) self.transformer_container = CustomContainerModelImage( path='', username=None, password=None, runas=None) self.transformer = CustomContainerModel(image=None) self.serving_def = InferenceJobModel(model=None, version=None, owner=None, device=None, deploy=None, serving_image=self.predictor, transformer=False, transformer_image=self.transformer, transformer_project=None, transformer_commit_id=None, transformer_code=None, min_replicas=0, max_concurrent_requests=0) self.run_def = JobModelParametersRun(template=None, group='default') self.job_parameters = JobModelParameters( _class='inference', inference=self.serving_def, run=self.run_def) self.job = JobModel(name=None, parameters=self.job_parameters) self.update_basic(user, name, description, tags)
[docs] def update_basic(self, user, name, description, tags): """ Method to update the attributes specified at creation. Description and tags can be updated. tags is a list of string values. """ tags = list_of_strs(tags) self.user = user self.name = name self.description = description self.job.name = name self.job.description = description self.serving_def.tags = tags self.serving_def.device = "cpu" self.serving_def.transformer = False
[docs] def set_transformer(self, transformer: bool=False, script=None): """ Method to specify if a transformer is required for pre/post processing of Inference requests and the script to run from the Transformer Code Repo. *Inputs* transformer True or False script Script command to run in the transformer pod from Transformer Code Repo """ self.serving_def.transformer = transformer self.serving_def.transformer_code = script
[docs] def update_transformer_code(self, code=None, commitid=None): """ Method to update Code Repo to use for the Transformer. *Inputs* code Code Repo containing the script for Transformer commitid commit id used to retrieve the transformer Code Repo """ self.serving_def.transformer_project = self.user + ':' + code self.serving_def.transformer_commit_id = commitid
[docs] def update_transformer_image(self, image_url='', login_uname=None, login_pswd=None): """ Method to update the image to use for the transformer *Inputs* image_url url for the image repository |br| e.g, docker.io/ocdr/dkube-datascience-tf-cpu:v2.0.0 login_uname username to access the image repository login_pswd password to access the image repository """ self.transformer_container.path = image_url self.transformer_container.username = login_uname self.transformer_container.password = login_pswd self.transformer.image = self.transformer_container
[docs] def update_serving_model(self, model, version=None): """ Method to update Model Repo input for Model Serving *Inputs* name Name of Model Repo containing the model files version Version (unique id) to use from Model Repo """ self.serving_def.model = model self.serving_def.version = version self.serving_def.owner = self.user
[docs] def update_serving_image(self, deploy=None, image_url='', login_uname=None, login_pswd=None): """ Method to update the image to use for Model Serving *Inputs* deploy Flag to specify Serving for Test or Production (TODO) image_url url for the image repository |br| e.g, docker.io/ocdr/tensorflowserver:2.0.0 login_uname username to access the image repository login_pswd password to access the image repository """ self.predictor_container.path = image_url self.predictor_container.username = login_uname self.predictor_container.password = login_pswd self.serving_def.deploy = deploy self.predictor.image = self.predictor_container
[docs] def set_production_deploy(self): """ Method to update the mode to use for Model Serving *Inputs* deploy Flag to specify Serving for Test or Production (TODO) """ self.serving_def.deploy = True
[docs] def update_autoscaling_config(self, min_replicas, max_concurrent_requests): """ Method to update the autocale config to use for Model Serving *Inputs* min_replicas Min number of pods to be running for Serving max_concurrent_requests Soft target threshold value for number of concurrent requests to trigger scale up of Serving pods """ self.serving_def.min_replicas = min_replicas self.serving_def.max_concurrent_requests = max_concurrent_requests