s444356/ium_444356
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

update registry.py, jenkinsfile_registry
All checks were successful
s444356-evaluation/pipeline/head This commit looks good
Details
s444356-training/pipeline/head This commit looks good
Details

Browse Source
This commit is contained in:
Maciej Czajka 2022-05-11 20:55:45 +02:00
parent defc9740dd
commit e45315a8ed
2 changed files with 10 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
Jenkinsfile_registry
View File

@ -1,20 +1,13 @@
pipeline { pipeline {
agent { agent {
dockerfile { image 'maciejczajka'
additionalBuildArgs "--build-arg KAGGLE_USERNAME=${params.KAGGLE_USERNAME} --build-arg KAGGLE_KEY=${params.KAGGLE_KEY} --build-arg CUTOFF=${params.CUTOFF} -t maciejczajka" args '-v /mlruns:/mlruns'
}
}
parameters {
buildSelector(
defaultSelector: lastSuccessful(),
description: 'Which build to use for copying artifacts for predict',
name: 'BUILD_SELECTOR')
} }
stages { stages {
stage('Script') { stage('Script') {
steps { steps {
copyArtifacts projectName: 's444409-training/main', selector: buildParameter('BUILD_SELECTOR') copyArtifacts projectName: 's444409-training/main'
sh 'python3 ./registry.py' sh 'python3 ./registry.py'
} }
} }

8
registry.py
View File

@ -1,9 +1,15 @@
import mlflow import mlflow
import json
import numpy as np
logged_model = '/mlruns/12/1c2b9737c0204b0ca825811c35fb6c64/artifacts/s444409' logged_model = '/mlruns/12/1c2b9737c0204b0ca825811c35fb6c64/artifacts/s444409'
# Load model as a PyFuncModel. # Load model as a PyFuncModel.
loaded_model = mlflow.pyfunc.load_model(logged_model) loaded_model = mlflow.pyfunc.load_model(logged_model)
with open(f'{logged_model}/input_example.json') as f:
data = json.load(f)
input_example = np.array([data['inputs'][0]], dtype=np.float32)
# Predict on a Pandas DataFrame. # Predict on a Pandas DataFrame.
import pandas as pd import pandas as pd
print(f'Prediction: {loaded_model.predict(pd.DataFrame(data))}') print(f'Prediction: {loaded_model.predict(input_example)}')