ium_12

.dvc/.gitignore

@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache

.dvc/config

@@ -0,0 +1,4 @@
+[core]
+    remote = ium_ssh_remote
+['remote "ium_ssh_remote"']
+    url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl

.dvcignore

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

.gitignore

@@ -0,0 +1,2 @@
+/Spotify_Dataset.csv
+/spotify_songs.csv

Dockerfile

@@ -1,26 +1,12 @@
 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
+RUN pip3 install pandas scikit-learn requests kaggle numpy sacred pymongo --break-system-package
-
-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"]

Jenkinsfile

@@ -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"
                     }
                 }
             }

Jenkinsfile_sacred

@@ -0,0 +1,50 @@
+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
+      }
+    }
+  }
+}

MLProject/MLProject

@@ -0,0 +1,11 @@
+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"

MLProject/conda.yaml

@@ -0,0 +1,11 @@
+name: Spotify genre recognition - s464953
+channels:
+  - defaults
+dependencies:
+  - python=3.9
+  - pip
+  - pip:
+      - mlflow
+      - pandas
+      - scikit-learn
+      - numpy

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/MLmodel

@@ -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'

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/conda.yaml

@@ -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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/MLmodel

@@ -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'

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/conda.yaml

@@ -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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/python_env.yaml

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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/metadata/requirements.txt

@@ -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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/python_env.yaml

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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/artifacts/model/requirements.txt

@@ -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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/meta.yaml

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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/metrics/accuracy

@@ -0,0 +1 @@
+1715635505497 0.4782608695652174 0

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/max_iter

@@ -0,0 +1 @@
+1000

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/model_type

@@ -0,0 +1 @@
+LogisticRegression

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/params/test_size

@@ -0,0 +1 @@
+0.1

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.gitRepoURL

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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.log-model.history

@@ -0,0 +1 @@
+[{"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}]

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.project.backend

@@ -0,0 +1 @@
+local

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.project.entryPoint

@@ -0,0 +1 @@
+main

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.project.env

@@ -0,0 +1 @@
+conda

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.runName

@@ -0,0 +1 @@
+valuable-goat-689

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.source.git.commit

@@ -0,0 +1 @@
+390d6b118b45f3613f049b5cf665ff66ca00cbd5

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.source.git.repoURL

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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.source.name

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

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.source.type

@@ -0,0 +1 @@
+PROJECT

MLProject/mlruns/0/04eba1c93f6a4510b4487ad0789fa76f/tags/mlflow.user

@@ -0,0 +1 @@
+Michał

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/MLmodel

@@ -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: b733a1b574ba4815ac1f2887d47fe45c
+run_id: 2e98f71c04cd4e21a26b13ae9daaf43b
+utc_time_created: '2024-05-13 21:21:21.420484'

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/conda.yaml

@@ -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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/metadata/MLmodel

@@ -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: b733a1b574ba4815ac1f2887d47fe45c
+run_id: 2e98f71c04cd4e21a26b13ae9daaf43b
+utc_time_created: '2024-05-13 21:21:21.420484'

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/metadata/conda.yaml

@@ -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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/metadata/python_env.yaml

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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/metadata/requirements.txt

@@ -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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/python_env.yaml

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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/artifacts/model/requirements.txt

@@ -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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/meta.yaml

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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/metrics/accuracy

@@ -0,0 +1 @@
+1715635281395 0.4782608695652174 0

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/params/max_iter

@@ -0,0 +1 @@
+1000

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/params/model_type

@@ -0,0 +1 @@
+LogisticRegression

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/params/test_size

@@ -0,0 +1 @@
+0.1

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.gitRepoURL

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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.log-model.history

@@ -0,0 +1 @@
+[{"run_id": "2e98f71c04cd4e21a26b13ae9daaf43b", "artifact_path": "model", "utc_time_created": "2024-05-13 21:21:21.420484", "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": "b733a1b574ba4815ac1f2887d47fe45c", "mlflow_version": "2.12.2", "model_size_bytes": 1446}]

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.project.backend

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+local

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.project.entryPoint

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+main

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.project.env

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+conda

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.runName

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+illustrious-shark-67

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.source.git.commit

@@ -0,0 +1 @@
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MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.source.git.repoURL

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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.source.name

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

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.source.type

@@ -0,0 +1 @@
+PROJECT

MLProject/mlruns/0/2e98f71c04cd4e21a26b13ae9daaf43b/tags/mlflow.user

@@ -0,0 +1 @@
+Michał

MLProject/mlruns/0/71242ca0b6f446d89f411c36212b6761/artifacts/model/MLmodel

@@ -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: 89ad4cf7b9e7444ea84049ba5d88fdb8
+run_id: 71242ca0b6f446d89f411c36212b6761
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MLProject/mlruns/0/71242ca0b6f446d89f411c36212b6761/artifacts/model/conda.yaml

@@ -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

MLProject/mlruns/0/71242ca0b6f446d89f411c36212b6761/artifacts/model/metadata/MLmodel

@@ -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
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MLProject/mlruns/0/71242ca0b6f446d89f411c36212b6761/tags/mlflow.user

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