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

.ipynb_checkpoints/Dockerfile-checkpoint

@@ -1,17 +0,0 @@
-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 model_creator.py /app/
-
-RUN chmod +x model_creator.py

.ipynb_checkpoints/Jenkinsfile-checkpoint

@@ -1,39 +0,0 @@
-pipeline {
-    agent any
-    
-    triggers {
-        upstream(upstreamProjects: 'z-s464953-create-dataset', threshold: hudson.model.Result.SUCCESS)
-    }
-    
-    parameters {
-        string(name: 'TEST_SIZE', defaultValue: '0.10', description: 'Size of test dataset')
-        string(name: 'MAX_ITER', defaultValue: '1000', description: 'Max number of iterations')
-        buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
-    }
-    
-    stages {
-        stage('Clone Repository') {
-            steps {
-                git branch: 'training', url: 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
-            }
-        }
-        stage('Copy Artifacts') {
-            steps {
-                copyArtifacts filter: 'artifacts/*', projectName: 'z-s464953-create-dataset', selector: buildParameter('BUILD_SELECTOR')
-            }
-        }
-        stage("Run Docker") {
-            agent {
-                dockerfile {
-                    filename 'Dockerfile'
-                    reuseNode true
-                }
-            }
-            steps {
-                
-                sh "python3 /app/model_creator.py ${params.TEST_SIZE} ${params.MAX_ITER}"
-                archiveArtifacts artifacts: '/app/model.pkl', onlyIfSuccessful: true
-            }
-        }
-    }
-}

Dockerfile

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

Jenkinsfile

@@ -1,38 +1,39 @@
 pipeline {
     agent any
     
-    triggers {
-        upstream(upstreamProjects: 'z-s464953-create-dataset', threshold: hudson.model.Result.SUCCESS)
-    }
-    
     parameters {
-        string(name: 'TEST_SIZE', defaultValue: '0.10', description: 'Size of test dataset')
+        string(name: 'KAGGLE_USERNAME', defaultValue: 'gulczas', description: 'Kaggle username')
-        string(name: 'MAX_ITER', defaultValue: '1000', description: 'Max number of iterations')
+        password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
-        buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
     }
     
     stages {
         stage('Clone Repository') {
             steps {
-                git branch: 'training', url: 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
+                git 'https://git.wmi.amu.edu.pl/s464953/ium_464953.git'
             }
         }
-        stage('Copy Artifacts') {
+        stage('Cleanup Artifacts') {
             steps {
-                copyArtifacts filter: 'artifacts/*', projectName: 'z-s464953-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                script {
-            }
+                    sh 'rm -rf artifacts'
-        }
-        stage("Run Docker") {
-            agent {
-                dockerfile {
-                    filename 'Dockerfile'
-                    reuseNode true
                 }
             }
+        }
+        stage('Run Script') {
             steps {
-                
+                script {
-                sh "python3 /app/model_creator.py ${params.TEST_SIZE} ${params.MAX_ITER}"
+                    withEnv([
-                archiveArtifacts artifacts: 'model.pkl, artifacts/docker_test_dataset.csv', onlyIfSuccessful: true
+                    "KAGGLE_USERNAME=${env.KAGGLE_USERNAME}", 
+                    "KAGGLE_KEY=${env.KAGGLE_KEY}"])  
+                    {
+                        sh "bash ./download_dataset.sh"
+                    }
+                }
+            }
+        }
+        stage('Archive Artifacts') {
+            steps {
+                archiveArtifacts artifacts: 'artifacts/*', onlyIfSuccessful: true
             }
         }
     }

Jenkinsfile-docker-image-build

@@ -0,0 +1,57 @@
+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
+            }
+        }
+    }
+}

Jenkinsfile-docker-image-pull

@@ -0,0 +1,44 @@
+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
+            }
+        }
+    }
+}

Jenkinsfile-ium-6

@@ -0,0 +1,57 @@
+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
+            }
+        }
+    }
+}

Jenkinsfile-stats

@@ -0,0 +1,42 @@
+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
+            }
+        }
+    }
+}

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
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