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11 Commits

Author SHA1 Message Date
d2ae1e0b32 added model evaluation 2024-05-09 02:33:28 +02:00
db5d87f034 added model evaluation 2024-05-09 02:30:19 +02:00
79947a5811 added model evaluation 2024-05-09 02:28:21 +02:00
e3b48a0364 added model evaluation 2024-05-09 02:23:04 +02:00
63f9975668 added model evaluation 2024-05-09 02:20:22 +02:00
0886815c28 added model evaluation 2024-05-09 02:17:29 +02:00
a987608675 added model evaluation 2024-05-09 02:15:19 +02:00
fbd021ed51 added model evaluation 2024-05-09 02:13:12 +02:00
a0f4bcf55a added model evaluation 2024-05-09 02:11:37 +02:00
b79467e2bf added model evaluation 2024-05-09 02:06:04 +02:00
1fb8564e19 added model evaluation 2024-05-09 01:56:58 +02:00
170 changed files with 1494 additions and 2000396 deletions

3
.dvc/.gitignore vendored
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/config.local
/tmp
/cache

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[core]
remote = ium_ssh_remote
['remote "ium_ssh_remote"']
url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl

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@ -1,3 +0,0 @@
# Add patterns of files dvc should ignore, which could improve
# the performance. Learn more at
# https://dvc.org/doc/user-guide/dvcignore

2
.gitignore vendored
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/Spotify_Dataset.csv
/spotify_songs.csv

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FROM ubuntu:latest
RUN apt-get update && \
apt-get install -y \
python3 \
python3-pip \
wget \
unzip \
&& rm -rf /var/lib/apt/lists/*
RUN pip3 install pandas scikit-learn requests numpy
WORKDIR /app
COPY use_model.py /app/
RUN chmod +x use_model.py

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pipeline {
agent {
dockerfile true
}
triggers {
upstream(upstreamProjects: 's464953-training/training', threshold: hudson.model.Result.SUCCESS)
}
parameters {
buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
}
stages {
stage('Clone Repository') {
steps {
git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
}
}
stage('Copy Training Artifacts') {
steps {
copyArtifacts filter: 'artifacts/*', projectName: 's464953-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
}
}
stage('Copy Evaluation Artifacts') {
steps {
copyArtifacts filter: 'metrics_df.csv', projectName: 's464953-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
}
}
stage('Run Script') {
steps {
sh "python3 /app/use_model.py ${currentBuild.number}"
}
}
stage('Archive Artifacts') {
steps {
archiveArtifacts artifacts: '*', onlyIfSuccessful: true
}
}
}
}

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@ -0,0 +1,77 @@
import pickle
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, f1_score, accuracy_score
import sys
import os
import matplotlib.pyplot as plt
def calculate_metrics(result):
rmse = np.sqrt(mean_squared_error(result["Real"], result["Predictions"]))
f1 = f1_score(result["Real"], result["Predictions"], average='macro')
accuracy = accuracy_score(result["Real"], result["Predictions"])
filename = 'metrics_df.csv'
if os.path.exists(filename):
metrics_df = pd.read_csv(filename)
new_row = pd.DataFrame({'Build number': sys.argv[1], 'RMSE': [rmse], 'F1 Score': [f1], 'Accuracy': [accuracy]})
metrics_df = metrics_df.append(new_row, ignore_index=True)
else:
metrics_df = pd.DataFrame({'Build number': sys.argv[1], 'RMSE': [rmse], 'F1 Score': [f1], 'Accuracy': [accuracy]})
metrics_df.to_csv(filename, index=False)
def create_plots():
metrics_df = pd.read_csv("metrics_df.csv")
plt.plot(metrics_df["Build number"], metrics_df["Accuracy"])
plt.xlabel("Build Number")
plt.ylabel("Accuracy")
plt.title("Accuracy of the model over time")
plt.xticks(range(min(metrics_df["Build number"]), max(metrics_df["Build number"]) + 1))
plt.show()
plt.savefig("Accuracy_plot.png")
plt.plot(metrics_df["Build number"], metrics_df["F1 Score"])
plt.xlabel("Build Number")
plt.ylabel("F1 Score")
plt.title("F1 Score of the model over time")
plt.xticks(range(min(metrics_df["Build number"]), max(metrics_df["Build number"]) + 1))
plt.show()
plt.savefig("F1_score_plot.png")
plt.plot(metrics_df["Build number"], metrics_df["RMSE"])
plt.xlabel("Build Number")
plt.ylabel("RMSE")
plt.title("RMSE of the model over time")
plt.xticks(range(min(metrics_df["Build number"]), max(metrics_df["Build number"]) + 1))
plt.show()
plt.savefig("RMSE_plot.png")
np.set_printoptions(threshold=20)
file_path = 'model.pkl'
with open(file_path, 'rb') as file:
model = pickle.load(file)
print("Model został wczytany z pliku:", file_path)
test_df = pd.read_csv("artifacts/docker_test_dataset.csv")
Y_test = test_df[['playlist_genre']]
X_test = test_df.drop(columns='playlist_genre')
Y_test = np.ravel(Y_test)
scaler = StandardScaler()
numeric_columns = X_test.select_dtypes(include=['int', 'float']).columns
X_test_scaled = scaler.fit_transform(X_test[numeric_columns])
Y_pred = model.predict(X_test_scaled)
result = pd.DataFrame({'Predictions': Y_pred, "Real": Y_test})
result.to_csv("spotify_genre_predictions.csv", index=False)
calculate_metrics(result)
create_plots()

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@ -2,11 +2,16 @@ FROM ubuntu:latest
RUN apt-get update && \ RUN apt-get update && \
apt-get install -y \ apt-get install -y \
python3 \ python3 \
python3-pip \ python3-pip \
git \
wget \ wget \
unzip \ unzip \
&& rm -rf /var/lib/apt/lists/* && rm -rf /var/lib/apt/lists/*
RUN pip3 install pandas scikit-learn requests kaggle numpy sacred pymongo --break-system-package RUN pip3 install pandas scikit-learn requests numpy matplotlib
WORKDIR /app
COPY use_model.py /app/
RUN chmod +x use_model.py

41
Jenkinsfile vendored
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pipeline { pipeline {
agent any agent {
dockerfile true
}
triggers {
upstream(upstreamProjects: 's464953-training/training', threshold: hudson.model.Result.SUCCESS)
}
parameters { parameters {
string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username') buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key') gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
} }
stages { stages {
stage('Clone Repository') { stage('Clone Repository') {
@ -12,28 +18,25 @@ pipeline {
git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git' git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
} }
} }
stage('Cleanup Artifacts') { stage('Copy Training Artifacts') {
steps { steps {
script { copyArtifacts filter: 'artifacts/*', projectName: 's464953-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
sh 'rm -rf artifacts' }
}
}
} }
stage('Copy Evaluation Artifacts') {
steps {
copyArtifacts filter: 'metrics_df.csv', projectName: '_s464953-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
}
}
stage('Run Script') { stage('Run Script') {
steps { steps {
script { sh "python3 /app/use_model.py ${currentBuild.number}"
withEnv([
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"])
{
sh "bash ./download_dataset.sh"
}
}
} }
} }
stage('Archive Artifacts') { stage('Archive Artifacts') {
steps { steps {
archiveArtifacts artifacts: 'artifacts/*', onlyIfSuccessful: true archiveArtifacts artifacts: 'metrics_df.csv, spotify_genre_predictions.csv, F1_score_plot.png, RMSE_plot.png, Accuracy_plot.png', onlyIfSuccessful: true
} }
} }
} }

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pipeline {
agent any
parameters {
string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
}
stages {
stage('Clone Repository') {
steps {
git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
}
}
stage('Stop and remove existing container') {
steps {
script {
sh "docker stop s464953 || true"
sh "docker rm s464953 || true"
}
}
}
stage('Build Docker image') {
steps {
script {
withEnv([
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"
]) {
sh "docker build --build-arg KAGGLE_USERNAME=$KAGGLE_USERNAME --build-arg KAGGLE_KEY=$KAGGLE_KEY -t s464953 ."
}
}
}
}
stage('Run Docker container') {
steps {
script {
withEnv([
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"
]) {
sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app s464953"
}
}
}
}
stage('Archive stats.txt artifact') {
steps {
archiveArtifacts artifacts: 'stats.txt', allowEmptyArchive: true
}
}
}
}

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pipeline {
agent any
parameters {
string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
}
stages {
stage('Clone Repository') {
steps {
git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
}
}
stage('Stop and remove existing container') {
steps {
script {
sh "docker stop s464953 || true"
sh "docker rm s464953 || true"
}
}
}
stage('Run Docker container') {
steps {
script {
withEnv([
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"
]) {
sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app michalgulczynski/ium_s464953:1.0"
}
}
}
}
stage('Archive stats.txt artifact') {
steps {
archiveArtifacts artifacts: 'stats.txt', allowEmptyArchive: true
}
}
}
}

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pipeline {
agent any
parameters {
string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
}
stages {
stage('Clone Repository') {
steps {
git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
}
}
stage('Stop and remove existing container') {
steps {
script {
sh "docker stop s464953 || true"
sh "docker rm s464953 || true"
}
}
}
stage('Build Docker image') {
steps {
script {
withEnv([
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"
]) {
sh "docker build --build-arg KAGGLE_USERNAME=$KAGGLE_USERNAME --build-arg KAGGLE_KEY=$KAGGLE_KEY -t s464953 ."
}
}
}
}
stage('Run Docker container') {
steps {
script {
withEnv([
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"
]) {
sh "docker run --name s464953 -e KAGGLE_USERNAME=$KAGGLE_USERNAME -e KAGGLE_KEY=$KAGGLE_KEY -v ${WORKSPACE}:/app s464953"
}
}
}
}
stage('Archive stats.txt artifact') {
steps {
archiveArtifacts artifacts: 'model.pkl', allowEmptyArchive: true
}
}
}
}

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pipeline {
agent any
parameters {
buildSelector( defaultSelector: lastSuccessful(), description: 'Build for copying artifacts', name: 'BUILD_SELECTOR')
}
stages {
stage('Clone Repository') {
steps {
git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
}
}
stage('Cleanup Artifacts') {
steps {
script {
sh 'rm -rf artifacts'
}
}
}
stage('Copy Artifact') {
steps {
withEnv([
"BUILD_SELECTOR=${params.BUILD_SELECTOR}"
]) {
copyArtifacts fingerprintArtifacts: true, projectName: 'z-s464953-create-dataset', selector: buildParameter('$BUILD_SELECTOR')}
}
}
stage('Execute Shell Script') {
steps {
script {
sh "bash ./dataset_stats.sh"
}
}
}
stage('Archive Results') {
steps {
archiveArtifacts artifacts: 'artifacts/*', onlyIfSuccessful: true
}
}
}
}

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@ -1,50 +0,0 @@
pipeline {
agent any
parameters {
string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
}
stages {
stage('Clone Repository') {
steps {
git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
}
}
stage('Download datasets') {
steps {
withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
sh "bash ./download_dataset.sh"
}
}
}
stage('Build and Run Experiments') {
agent {
dockerfile {
reuseNode true
}
}
environment {
KAGGLE_USERNAME = "${params.KAGGLE_USERNAME}"
KAGGLE_KEY = "${params.KAGGLE_KEY}"
}
steps {
sh 'chmod +x sacred/sacred_model_creator.py'
sh 'python3 sacred/sacred_model_creator.py'
sh 'chmod +x sacred/sacred_use_model.py'
sh 'python3 sacred/sacred_use_model.py'
}
}
stage('Archive Artifacts from Experiments') {
steps {
archiveArtifacts artifacts: 'my_experiment_logs/**', allowEmptyArchive: true
}
}
}
}

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name: MLflow Example
conda_env: conda.yaml
entry_points:
main:
command: "python model_creator.py {max_iter}"
parameters:
max_iter: {type: int, default: 1000}
test:
command: "python use_model.py"

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name: Spotify genre recognition - s464953
channels:
- defaults
dependencies:
- python=3.9
- pip
- pip:
- mlflow
- pandas
- scikit-learn
- numpy

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artifact_path: model
flavors:
python_function:
env:
conda: conda.yaml
virtualenv: python_env.yaml
loader_module: mlflow.sklearn
model_path: model.pkl
predict_fn: predict
python_version: 3.9.19
sklearn:
code: null
pickled_model: model.pkl
serialization_format: cloudpickle
sklearn_version: 1.4.2
mlflow_version: 2.12.2
model_size_bytes: 1446
model_uuid: 9026270861774aad82aee9fc231054b4
run_id: 04eba1c93f6a4510b4487ad0789fa76f
utc_time_created: '2024-05-13 21:25:05.523657'

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@ -1,15 +0,0 @@
channels:
- conda-forge
dependencies:
- python=3.9.19
- pip<=24.0
- pip:
- mlflow==2.12.2
- cloudpickle==3.0.0
- numpy==1.26.4
- packaging==23.1
- psutil==5.9.5
- pyyaml==6.0.1
- scikit-learn==1.4.2
- scipy==1.13.0
name: mlflow-env

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artifact_path: model
flavors:
python_function:
env:
conda: conda.yaml
virtualenv: python_env.yaml
loader_module: mlflow.sklearn
model_path: model.pkl
predict_fn: predict
python_version: 3.9.19
sklearn:
code: null
pickled_model: model.pkl
serialization_format: cloudpickle
sklearn_version: 1.4.2
mlflow_version: 2.12.2
model_size_bytes: 1446
model_uuid: 9026270861774aad82aee9fc231054b4
run_id: 04eba1c93f6a4510b4487ad0789fa76f
utc_time_created: '2024-05-13 21:25:05.523657'

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@ -1,15 +0,0 @@
channels:
- conda-forge
dependencies:
- python=3.9.19
- pip<=24.0
- pip:
- mlflow==2.12.2
- cloudpickle==3.0.0
- numpy==1.26.4
- packaging==23.1
- psutil==5.9.5
- pyyaml==6.0.1
- scikit-learn==1.4.2
- scipy==1.13.0
name: mlflow-env

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@ -1,7 +0,0 @@
python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools
- wheel==0.43.0
dependencies:
- -r requirements.txt

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@ -1,8 +0,0 @@
mlflow==2.12.2
cloudpickle==3.0.0
numpy==1.26.4
packaging==23.1
psutil==5.9.5
pyyaml==6.0.1
scikit-learn==1.4.2
scipy==1.13.0

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@ -1,7 +0,0 @@
python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools
- wheel==0.43.0
dependencies:
- -r requirements.txt

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@ -1,8 +0,0 @@
mlflow==2.12.2
cloudpickle==3.0.0
numpy==1.26.4
packaging==23.1
psutil==5.9.5
pyyaml==6.0.1
scikit-learn==1.4.2
scipy==1.13.0

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@ -1,15 +0,0 @@
artifact_uri: file:///D:/studia/inzynieria%20uczenia%20maszynowego/ium_464953/MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts
end_time: 1715635510283
entry_point_name: ''
experiment_id: '0'
lifecycle_stage: active
run_id: 04eba1c93f6a4510b4487ad0789fa76f
run_name: valuable-goat-689
run_uuid: 04eba1c93f6a4510b4487ad0789fa76f
source_name: ''
source_type: 4
source_version: ''
start_time: 1715635487472
status: 3
tags: []
user_id: Michał

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1715635505497 0.4782608695652174 0

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LogisticRegression

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https://git.wmi.amu.edu.pl/s464953/ium_464953.git

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[{"run_id": "04eba1c93f6a4510b4487ad0789fa76f", "artifact_path": "model", "utc_time_created": "2024-05-13 21:25:05.523657", "flavors": {"python_function": {"model_path": "model.pkl", "predict_fn": "predict", "loader_module": "mlflow.sklearn", "python_version": "3.9.19", "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"}}, "sklearn": {"pickled_model": "model.pkl", "sklearn_version": "1.4.2", "serialization_format": "cloudpickle", "code": null}}, "model_uuid": "9026270861774aad82aee9fc231054b4", "mlflow_version": "2.12.2", "model_size_bytes": 1446}]

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@ -1 +0,0 @@
390d6b118b45f3613f049b5cf665ff66ca00cbd5

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@ -1 +0,0 @@
https://git.wmi.amu.edu.pl/s464953/ium_464953.git

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@ -1 +0,0 @@
file://D:\studia\inzynieria uczenia maszynowego\ium_464953#\MLProject

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@ -1,20 +0,0 @@
artifact_path: model
flavors:
python_function:
env:
conda: conda.yaml
virtualenv: python_env.yaml
loader_module: mlflow.sklearn
model_path: model.pkl
predict_fn: predict
python_version: 3.9.19
sklearn:
code: null
pickled_model: model.pkl
serialization_format: cloudpickle
sklearn_version: 1.4.2
mlflow_version: 2.12.2
model_size_bytes: 1446
model_uuid: b733a1b574ba4815ac1f2887d47fe45c
run_id: 2e98f71c04cd4e21a26b13ae9daaf43b
utc_time_created: '2024-05-13 21:21:21.420484'

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@ -1,15 +0,0 @@
channels:
- conda-forge
dependencies:
- python=3.9.19
- pip<=24.0
- pip:
- mlflow==2.12.2
- cloudpickle==3.0.0
- numpy==1.26.4
- packaging==23.1
- psutil==5.9.5
- pyyaml==6.0.1
- scikit-learn==1.4.2
- scipy==1.13.0
name: mlflow-env

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@ -1,20 +0,0 @@
artifact_path: model
flavors:
python_function:
env:
conda: conda.yaml
virtualenv: python_env.yaml
loader_module: mlflow.sklearn
model_path: model.pkl
predict_fn: predict
python_version: 3.9.19
sklearn:
code: null
pickled_model: model.pkl
serialization_format: cloudpickle
sklearn_version: 1.4.2
mlflow_version: 2.12.2
model_size_bytes: 1446
model_uuid: b733a1b574ba4815ac1f2887d47fe45c
run_id: 2e98f71c04cd4e21a26b13ae9daaf43b
utc_time_created: '2024-05-13 21:21:21.420484'

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@ -1,15 +0,0 @@
channels:
- conda-forge
dependencies:
- python=3.9.19
- pip<=24.0
- pip:
- mlflow==2.12.2
- cloudpickle==3.0.0
- numpy==1.26.4
- packaging==23.1
- psutil==5.9.5
- pyyaml==6.0.1
- scikit-learn==1.4.2
- scipy==1.13.0
name: mlflow-env

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@ -1,7 +0,0 @@
python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools
- wheel==0.43.0
dependencies:
- -r requirements.txt

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@ -1,8 +0,0 @@
mlflow==2.12.2
cloudpickle==3.0.0
numpy==1.26.4
packaging==23.1
psutil==5.9.5
pyyaml==6.0.1
scikit-learn==1.4.2
scipy==1.13.0

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@ -1,7 +0,0 @@
python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools
- wheel==0.43.0
dependencies:
- -r requirements.txt

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@ -1,8 +0,0 @@
mlflow==2.12.2
cloudpickle==3.0.0
numpy==1.26.4
packaging==23.1
psutil==5.9.5
pyyaml==6.0.1
scikit-learn==1.4.2
scipy==1.13.0

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@ -1,15 +0,0 @@
artifact_uri: file:///D:/studia/inzynieria%20uczenia%20maszynowego/ium_464953/MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts
end_time: 1715635286846
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