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22 Commits
master_cop ... master

Author SHA1 Message Date
Michal Gulczynski
4d8cfa8dd0 ium_12 2024-06-11 23:34:03 +02:00
Michal Gulczynski
23b65895c0 ium_07 sacred 2024-06-11 20:14:38 +02:00
Michal Gulczynski
b78b3ec7da ium_07 sacred 2024-06-11 20:12:27 +02:00
Michal Gulczynski
113d505f58 ium_07 sacred 2024-06-11 20:07:04 +02:00
Michal Gulczynski
b88ddb3066 ium_07 sacred 2024-06-11 19:56:09 +02:00
Michal Gulczynski
9e90c820b8 ium_07 sacred 2024-06-11 19:41:39 +02:00
Michal Gulczynski
f6fa572ef8 ium_07 sacred 2024-06-11 19:35:16 +02:00
Michal Gulczynski
54c34cd3ad ium_07 sacred 2024-06-11 19:30:31 +02:00
Michal Gulczynski
ac15483ad1 ium_07 sacred 2024-06-11 19:27:36 +02:00
Michal Gulczynski
1d573f17f4 ium_07 sacred 2024-06-11 19:26:06 +02:00
Michal Gulczynski
19460ed294 ium_07 sacred 2024-06-11 19:24:36 +02:00
Michal Gulczynski
2724f348b0 ium_07 sacred 2024-06-11 19:15:00 +02:00
Michal Gulczynski
af91b85a30 ium_07 sacred 2024-06-11 19:06:13 +02:00
Michal Gulczynski
5eb5fb7172 added dvc.yaml 2024-05-26 20:58:02 +02:00
Michal Gulczynski
f6849deb29 ium-10 2024-05-25 00:54:59 +02:00
Michal Gulczynski
aace26318b added conda environment.yml 2024-05-21 22:14:56 +02:00
Michal Gulczynski
8c58e1b278 added mlflow project 2024-05-13 23:32:25 +02:00
s464953
3fb485b6a2 updated data download 2024-05-08 21:30:39 +02:00
s464953
7e92d821eb updated data download 2024-05-08 21:29:45 +02:00
s464953
6911e5d237 updated data download 2024-05-08 21:28:56 +02:00
s464953
db937e4e58 updated data download 2024-05-08 21:28:11 +02:00
s464953
279dbc885a updated data download 2024-05-08 21:23:10 +02:00
150 changed files with 696024 additions and 38 deletions
Show Stats Expand all files Collapse all files

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.dvc/.gitignore vendored Normal file
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/config.local
/tmp
/cache

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.dvc/config Normal file
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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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# Add patterns of files dvc should ignore, which could improve
# the performance. Learn more at
# https://dvc.org/doc/user-guide/dvcignore

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/Spotify_Dataset.csv
/spotify_songs.csv

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Dockerfile
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FROM ubuntu:latest
ENV KAGGLE_USERNAME=gulczas
ENV KAGGLE_KEY=default_key
RUN apt-get update && \
apt-get install -y \
python3 \
python3-pip \
git \
wget \
unzip \
&& rm -rf /var/lib/apt/lists/*
RUN pip3 install pandas scikit-learn requests kaggle numpy
WORKDIR /app
COPY model_creator.py /app/
COPY use_model.py /app/
COPY run_py_scripts.sh /app/
RUN chmod +x model_creator.py
RUN chmod +x use_model.py
CMD ["bash", "run_py_scripts.sh"]
RUN pip3 install pandas scikit-learn requests kaggle numpy sacred pymongo --break-system-package

3
Jenkinsfile vendored
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@ -4,7 +4,6 @@ pipeline {
parameters {
string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
string(name: 'CUTOFF', defaultValue: '90', description: 'Number of rows to cut')
}
stages {
@ -27,7 +26,7 @@ pipeline {
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"])
{
sh "bash ./download_dataset.sh ${params.CUTOFF}"
sh "bash ./download_dataset.sh"
}
}
}

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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('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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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

20
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@ -0,0 +1,20 @@
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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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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@ -0,0 +1,7 @@
python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools
- wheel==0.43.0
dependencies:
- -r requirements.txt

8
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@ -0,0 +1,8 @@
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

BIN
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python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools
- wheel==0.43.0
dependencies:
- -r requirements.txt

8
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@ -0,0 +1,8 @@
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

15
MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/meta.yaml Normal file
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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

1
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1715635505497 0.4782608695652174 0

1
MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/max_iter Normal file
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1000

1
MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/model_type Normal file
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LogisticRegression

1
MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/test_size Normal file
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0.1

1
MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.gitRepoURL Normal file
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https://git.wmi.amu.edu.pl/s464953/ium_464953.git

1
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1
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local

1
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main

1
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conda

1
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valuable-goat-689

1
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390d6b118b45f3613f049b5cf665ff66ca00cbd5

1
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https://git.wmi.amu.edu.pl/s464953/ium_464953.git

1
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file://D:\studia\inzynieria uczenia maszynowego\ium_464953#\MLProject

1
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PROJECT

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Michał

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@ -0,0 +1,20 @@
artifact_path: model
flavors:
python_function:
env:
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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'

15
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@ -0,0 +1,15 @@
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

20
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@ -0,0 +1,20 @@
artifact_path: model
flavors:
python_function:
env:
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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'

15
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@ -0,0 +1,15 @@
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

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

8
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@ -0,0 +1,8 @@
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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@ -0,0 +1,7 @@
python: 3.9.19
build_dependencies:
- pip==24.0
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dependencies:
- -r requirements.txt

8
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@ -0,0 +1,8 @@
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

15
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artifact_uri: file:///D:/studia/inzynieria%20uczenia%20maszynowego/ium_464953/MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts
end_time: 1715635286846
entry_point_name: ''
experiment_id: '0'
lifecycle_stage: active
run_id: 2e98f71c04cd4e21a26b13ae9daaf43b
run_name: illustrious-shark-67
run_uuid: 2e98f71c04cd4e21a26b13ae9daaf43b
source_name: ''
source_type: 4
source_version: ''
start_time: 1715635260477
status: 3
tags: []
user_id: Michał

1
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1715635281395 0.4782608695652174 0

1
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1000

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LogisticRegression

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0.1

1
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1
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1
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local

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main

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@ -0,0 +1 @@
conda

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