Randy Phoa randyphoa

## wml_utils.py
import requests
# from watson_machine_learning_client import WatsonMachineLearningAPIClient
from ibm_watson_machine_learning import APIClient

class WMLUtils:
    def __init__(self, wml_credentials):
#         self.client = WatsonMachineLearningAPIClient(wml_credentials)
        self.client = APIClient(wml_credentials)

    def set_deployment_space(self, deployment_space):

## Deploy Prophet Model.ipynb

      
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                randyphoa
                / Deploy Prophet Model.ipynb
            
            
              Created
              December 13, 2021 04:15
            
              
                Deploying your Prophet model with custom environments on Watson Machine Learning
              
          
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## create-custom-environment.py
# create a conda config with the require libraries

config_yaml = """
name: custom
channels:
  - conda-forge
dependencies:
  - r
  - libiconv
"""

## train-iris-model.R
# install specific version of xgboost to ensure the development and deployment environment have the same version

install.packages("https://cloud.r-project.org/src/contrib/Archive/xgboost/xgboost_1.5.2.1.tar.gz", repos=NULL, type="source")

library(xgboost)

data(iris)

species <- iris$Species
label <- as.integer(iris$Species) - 1

## predict-function.R
#!/usr/bin/env Rscript

library(xgboost)
library(jsonlite)

args <- commandArgs(trailingOnly = TRUE)

if (length(args) == 0) {
  stop("Insufficient arguments.", call. = FALSE)
}

## deploy-python-function-to-call-R.py
params = {
    "wml_credentials": wml_credentials,
    "space_id": SPACE_ID,
    "script": "predict.R",
    "required_assets": ["iris_xgb.model"],
    "r_packages": ["jsonlite", "xgboost"],
}

def r_function(params=params):
    """

## r-online-deployment-demo.ipynb

      
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                randyphoa
                / r-online-deployment-demo.ipynb
            
            
              Created
              June 7, 2022 06:43
            
              
                Deploy R in online mode - complete example notebook
              
          
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## batch-job-to-deploy-R-model.py
config = {
    "function_name": "lead scoring",
    "script": "lead_scoring.R",
    "required_assets": ["products.csv", "transactions.rds", "xgboost.rds", "mssql-jdbc-10.2.1.jre11.jar"],
    "r_packages": ["dplyr", "RJDBC", "xgboost"],
}

payload = {"scoring": {"input_data": [{"values": [config]}]}, "deployment": {"id": deployment_id}, "space_id": SPACE_ID, "name": "lead_scoring_deployment"}
headers = {"Content-Type": "application/json", "Accept": "application/json", "Authorization": wml_client._get_headers()["Authorization"]}
params = {"version": "2021-06-24", "space_id": SPACE_ID}

## cpd-operators.yaml
apiVersion: v1
kind: Namespace
metadata:
  name: ibm-common-services
---
apiVersion: v1
kind: Namespace
metadata:
  name: cpd
---

## cpd-services.yaml
apiVersion: databases.cpd.ibm.com/v1
kind: Db2oltpService
metadata:
  name: db2oltp-cr
  namespace: cpd
spec:
  license:
    accept: true
    license: Standard
---
	import requests
	# from watson_machine_learning_client import WatsonMachineLearningAPIClient
	from ibm_watson_machine_learning import APIClient

	class WMLUtils:
	def __init__(self, wml_credentials):
	# self.client = WatsonMachineLearningAPIClient(wml_credentials)
	self.client = APIClient(wml_credentials)

	def set_deployment_space(self, deployment_space):
	# create a conda config with the require libraries

	config_yaml = """
	name: custom
	channels:
	- conda-forge
	dependencies:
	- r
	- libiconv
	"""
	# install specific version of xgboost to ensure the development and deployment environment have the same version

	install.packages("https://cloud.r-project.org/src/contrib/Archive/xgboost/xgboost_1.5.2.1.tar.gz", repos=NULL, type="source")

	library(xgboost)

	data(iris)

	species <- iris$Species
	label <- as.integer(iris$Species) - 1
	#!/usr/bin/env Rscript

	library(xgboost)
	library(jsonlite)

	args <- commandArgs(trailingOnly = TRUE)

	if (length(args) == 0) {
	stop("Insufficient arguments.", call. = FALSE)
	}
	params = {
	"wml_credentials": wml_credentials,
	"space_id": SPACE_ID,
	"script": "predict.R",
	"required_assets": ["iris_xgb.model"],
	"r_packages": ["jsonlite", "xgboost"],
	}

	def r_function(params=params):
	"""
	config = {
	"function_name": "lead scoring",
	"script": "lead_scoring.R",
	"required_assets": ["products.csv", "transactions.rds", "xgboost.rds", "mssql-jdbc-10.2.1.jre11.jar"],
	"r_packages": ["dplyr", "RJDBC", "xgboost"],
	}

	payload = {"scoring": {"input_data": [{"values": [config]}]}, "deployment": {"id": deployment_id}, "space_id": SPACE_ID, "name": "lead_scoring_deployment"}
	headers = {"Content-Type": "application/json", "Accept": "application/json", "Authorization": wml_client._get_headers()["Authorization"]}
	params = {"version": "2021-06-24", "space_id": SPACE_ID}
	apiVersion: v1
	kind: Namespace
	metadata:
	name: ibm-common-services
	---
	apiVersion: v1
	kind: Namespace
	metadata:
	name: cpd
	---
	apiVersion: databases.cpd.ibm.com/v1
	kind: Db2oltpService
	metadata:
	name: db2oltp-cr
	namespace: cpd
	spec:
	license:
	accept: true
	license: Standard
	---