├── .gitignore
├── Pipfile
├── Pipfile.lock
├── README.md
├── data
    ├── aug_test.csv
    ├── aug_train.csv
    └── sample_submission.csv
├── images
    ├── artifacts-s3.png
    ├── mlflow.png
    ├── runs-remote.png
    ├── runs.png
    └── s3-console.png
├── notebooks
    └── 1_EDA.ipynb
└── src
    ├── MLproject
    ├── conda.yaml
    └── train.py


/.gitignore:
--------------------------------------------------------------------------------
1 | .ipynb_checkpoints
2 | mlruns
3 | .DS_Store
4 | 
5 | .vscode


--------------------------------------------------------------------------------
/Pipfile:
--------------------------------------------------------------------------------
 1 | [[source]]
 2 | url = "https://pypi.org/simple"
 3 | verify_ssl = true
 4 | name = "pypi"
 5 | 
 6 | [packages]
 7 | mlflow = "*"
 8 | jupyter = "*"
 9 | matplotlib = "*"
10 | scikit-learn = "*"
11 | tqdm = "*"
12 | boto3 = "*"
13 | 
14 | [dev-packages]
15 | 
16 | [requires]
17 | python_version = "3.8"
18 | 


--------------------------------------------------------------------------------
/Pipfile.lock:
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1127 |             "version": "==6.1"
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1165 |             "version": "==0.5.1"
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1181 |             "version": "==1.0.1"
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1190 |     },
1191 |     "develop": {}
1192 | }
1193 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | ### How to use MLflow to manage the Machine Learning lifecycle
  2 | 
  3 | ![](./images/mlflow.png)
  4 | 
  5 | In this repo, I experiment with MLflow to:
  6 | 
  7 | - track machine learning experiments based on:
  8 | 
  9 |   - metrics
 10 |   - hyper-parameters
 11 |   - source scripts executing the run
 12 |   - code version
 13 |   - notes & comments
 14 | 
 15 | - compare different runs between each other
 16 | - set up a tracking server locally and on AWS
 17 | - deploy the your model using MLflow Models
 18 | 
 19 | ### Quickstart locally
 20 | 
 21 | To execute the code:
 22 | 
 23 | - Install pipenv to run a virtual environment with mlflow (it's cleaner this way)
 24 | 
 25 | ```bash
 26 | pip install pipenv
 27 | ```
 28 | 
 29 | - Clone the project
 30 | 
 31 | ```bash
 32 | git clone git@github.com:ahmedbesbes/mlflow.git
 33 | ```
 34 | 
 35 | - Install the dependencies
 36 | 
 37 | ```bash
 38 | cd mlflow/
 39 | pipenv install .
 40 | ```
 41 | 
 42 | - Start a tracking server locally
 43 | 
 44 | ```bash
 45 | mlflow ui
 46 | ```
 47 | 
 48 | - Launch the training (or whatever code that logs to MLflow)
 49 | 
 50 | ```bash
 51 | python train.py
 52 | ```
 53 | 
 54 | - Visit http://localhost:5000 to check the runs on the MLflow ui
 55 | 
 56 | ![](./images/runs.png)
 57 | 
 58 | ### Launch a tracking server on AWS
 59 | 
 60 | If you're a team of developers or data scientists, you can spin up a tracking server where everyone logs his/her runs
 61 | 
 62 | #### 1. Prepare an EC2 machine and an S3 bucket
 63 | 
 64 | - create an IAM user on AWS. Get its credentials, namely `Access key ID` and `Secret access key`
 65 | 
 66 | - with this same user, create an s3 bucket to store future artifacts: give this bucket a name.
 67 |   Mine is `mlflow-artifact-store-demo` but you cannot pick it
 68 | 
 69 | - Launch an EC2 instance: it doesn't have to be big. a `t2.micro` eligible to free tier does perfectly the job
 70 | 
 71 | - Configure the security group of this instance to accept inbound http traffic on port 5000
 72 | 
 73 | - ssh into your EC2:
 74 | 
 75 |   - install pip
 76 |     ```bash
 77 |     sudo apt update
 78 |     sudo apt install python3-pip
 79 |     ```
 80 |   - install pipenv
 81 | 
 82 |     ```bash
 83 |     sudo pip3 install pipenv
 84 |     sudo pip3 install virtualenv
 85 | 
 86 |     export PATH=$PATH:/home/[your_user]/.local/bin/
 87 |     ```
 88 | 
 89 | - now with pipenv, install the dependencies to run the mlflow server
 90 | 
 91 |   ```bash
 92 |   pipenv install mlflow
 93 |   pipenv install awscli
 94 |   pipenv install boto3
 95 |   ```
 96 | 
 97 | - on the EC2 machine, configure aws with user's crendentials so that the tracking server can have access to s3 and display the artifacts on the UI.
 98 | 
 99 |   enter `aws configure` then follow the instructions to enter the credentials
100 | 
101 | - start an mlflow server on the EC2 instance by defining the host as `0.0.0.0` and the `--default-artifact-root` as the S3 bucket
102 | 
103 |   ```shell
104 |   mlflow server -h 0.0.0.0  \
105 |                 --default-artifact-root s3://mlflow-artifact-store-demo
106 |   ```
107 | 
108 | #### 2. Set AWS credentials and change the tracking URI and
109 | 
110 | - set the AWS credentials as environment variables so that the code uploads artifacts to the s3 bucket
111 | 
112 |   ```shell
113 |   export AWS_ACCESS_KEY_ID=<your-aws-access-key-id>
114 |   export AWS_SECRET_ACCESS_KEY = <your-aws-secret-access-key>
115 |   ```
116 | 
117 | - change the tracking URI to the public dns of your EC2 machine + port 5000
118 | 
119 |   In my case the tracking URI was: http://ec2-35-180-45-108.eu-west-3.compute.amazonaws.com:5000/
120 | 
121 | Now you everything should be be good: after running the script locally you can inspect metrics on the UI that run on the remote server
122 | 
123 | ![](./images/runs-remote.png)
124 | 
125 | By clicking on a specific run, you can see its artifacts uploaded to S3.
126 | 
127 | ![](./images/artifacts-s3.png)
128 | 
129 | In fact, these artifacts are effectively on S3.
130 | 
131 | ![](./images/s3-console.png)
132 | 
133 | ### Slides
134 | 
135 | - French [version](https://docs.google.com/presentation/d/1wAZnpVB9tINqZVrMP0Am3BwBgZrFRosgyIzhZ9p9In4/edit?usp=sharing)
136 | - English version (coming soon)
137 | 


--------------------------------------------------------------------------------
/data/sample_submission.csv:
--------------------------------------------------------------------------------
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1001 | 23288,0.5
1002 | 33010,0.5
1003 | 4819,0.5
1004 | 3127,0.5
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "human-marks",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "The data comes from Kaggle:\n",
  9 |     "- https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists?select=sample_submission.csv"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 1,
 15 |    "id": "subsequent-anthropology",
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "%matplotlib inline\n",
 20 |     "%config Completer.use_jedi=False"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 2,
 26 |    "id": "annoying-sociology",
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "from urllib.parse import urlparse\n",
 31 |     "\n",
 32 |     "import pandas as pd\n",
 33 |     "from sklearn.preprocessing import LabelEncoder\n",
 34 |     "from sklearn.model_selection import train_test_split\n",
 35 |     "from sklearn.linear_model import LogisticRegression\n",
 36 |     "from sklearn.tree import DecisionTreeClassifier\n",
 37 |     "from sklearn.ensemble import RandomForestClassifier\n",
 38 |     "from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,\n",
 39 |     "                             roc_auc_score, classification_report)\n",
 40 |     "import mlflow\n",
 41 |     "import mlflow.sklearn"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 4,
 47 |    "id": "substantial-comedy",
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "mlflow.set_tracking_uri(\"file:///Users/ahmed.besbes/projects/mlflow/mlruns\")"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": 5,
 57 |    "id": "nervous-aquatic",
 58 |    "metadata": {},
 59 |    "outputs": [],
 60 |    "source": [
 61 |     "tracking_uri = mlflow.get_tracking_uri()"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": 6,
 67 |    "id": "comprehensive-future",
 68 |    "metadata": {},
 69 |    "outputs": [
 70 |     {
 71 |      "data": {
 72 |       "text/plain": [
 73 |        "'file:///Users/ahmed.besbes/projects/mlflow/mlruns'"
 74 |       ]
 75 |      },
 76 |      "execution_count": 6,
 77 |      "metadata": {},
 78 |      "output_type": "execute_result"
 79 |     }
 80 |    ],
 81 |    "source": [
 82 |     "tracking_uri"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 7,
 88 |    "id": "strategic-parameter",
 89 |    "metadata": {},
 90 |    "outputs": [],
 91 |    "source": [
 92 |     "data = pd.read_csv(\"./../data/aug_train.csv\")\n",
 93 |     "targets = data[[\"target\"]]\n",
 94 |     "data.drop([\"enrollee_id\", \"target\"], inplace=True, axis=1)"
 95 |    ]
 96 |   },
 97 |   {
 98 |    "cell_type": "code",
 99 |    "execution_count": 8,
100 |    "id": "worse-headquarters",
101 |    "metadata": {},
102 |    "outputs": [],
103 |    "source": [
104 |     "categorical_features = []\n",
105 |     "numerical_features = []\n",
106 |     "\n",
107 |     "for column in data.columns:\n",
108 |     "    dtype = str(data[column].dtype)\n",
109 |     "    if dtype in [\"float64\", \"int64\"]:\n",
110 |     "        numerical_features.append(column)\n",
111 |     "    else:\n",
112 |     "        categorical_features.append(column)"
113 |    ]
114 |   },
115 |   {
116 |    "cell_type": "code",
117 |    "execution_count": 9,
118 |    "id": "computational-browser",
119 |    "metadata": {},
120 |    "outputs": [],
121 |    "source": [
122 |     "for categorical_feature in categorical_features:\n",
123 |     "    data[categorical_feature].fillna('missing', inplace=True)"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "code",
128 |    "execution_count": 10,
129 |    "id": "atmospheric-volume",
130 |    "metadata": {},
131 |    "outputs": [],
132 |    "source": [
133 |     "for categorical_feature in categorical_features:\n",
134 |     "    le = LabelEncoder()\n",
135 |     "    data[categorical_feature] = le.fit_transform(data[categorical_feature])"
136 |    ]
137 |   },
138 |   {
139 |    "cell_type": "code",
140 |    "execution_count": 11,
141 |    "id": "liable-diameter",
142 |    "metadata": {},
143 |    "outputs": [],
144 |    "source": [
145 |     "x_train, x_test, y_train, y_test = train_test_split(data.values, \n",
146 |     "                                                    targets.values.ravel(), \n",
147 |     "                                                    test_size=0.3, \n",
148 |     "                                                    random_state=2021,\n",
149 |     "                                                    stratify=targets.values)"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": 12,
155 |    "id": "painful-diagnosis",
156 |    "metadata": {},
157 |    "outputs": [
158 |     {
159 |      "name": "stdout",
160 |      "output_type": "stream",
161 |      "text": [
162 |       "(13410, 12) (5748, 12)\n"
163 |      ]
164 |     }
165 |    ],
166 |    "source": [
167 |     "print(x_train.shape, x_test.shape)"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 13,
173 |    "id": "collect-clark",
174 |    "metadata": {},
175 |    "outputs": [
176 |     {
177 |      "name": "stdout",
178 |      "output_type": "stream",
179 |      "text": [
180 |       "(13410,) (5748,)\n"
181 |      ]
182 |     }
183 |    ],
184 |    "source": [
185 |     "print(y_train.shape, y_test.shape)"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 18,
191 |    "id": "double-safety",
192 |    "metadata": {
193 |     "scrolled": false
194 |    },
195 |    "outputs": [],
196 |    "source": [
197 |     "with mlflow.start_run():\n",
198 |     "    class_weight = \"balanced\"\n",
199 |     "    max_iter = 1000\n",
200 |     "\n",
201 |     "    logistic_regression = LogisticRegression(class_weight=class_weight, max_iter=max_iter)\n",
202 |     "    logistic_regression.fit(x_train, y_train)\n",
203 |     "\n",
204 |     "    y_pred = logistic_regression.predict(x_test)\n",
205 |     "\n",
206 |     "    accuracy = accuracy_score(y_test, y_pred)\n",
207 |     "    precision = precision_score(y_test, y_pred)\n",
208 |     "    recall = recall_score(y_test, y_pred)\n",
209 |     "    f1 = f1_score(y_test, y_pred)\n",
210 |     "    auc = roc_auc_score(y_test, y_pred)\n",
211 |     "    \n",
212 |     "    mlflow.log_param(\"class_weight\", class_weight)\n",
213 |     "    mlflow.log_param(\"max_iter\", max_iter)\n",
214 |     "    \n",
215 |     "    mlflow.log_metric(\"accuracy\", accuracy)\n",
216 |     "    mlflow.log_metric(\"precision\", precision)\n",
217 |     "    mlflow.log_metric(\"recall\", recall)\n",
218 |     "    mlflow.log_metric(\"f1\", f1)\n",
219 |     "    mlflow.log_metric(\"auc\", auc) \n",
220 |     "    \n",
221 |     "    mlflow.sklearn.log_model(logistic_regression, \"model\")"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "code",
226 |    "execution_count": null,
227 |    "id": "black-costa",
228 |    "metadata": {},
229 |    "outputs": [],
230 |    "source": []
231 |   }
232 |  ],
233 |  "metadata": {
234 |   "kernelspec": {
235 |    "display_name": "Python 3",
236 |    "language": "python",
237 |    "name": "python3"
238 |   },
239 |   "language_info": {
240 |    "codemirror_mode": {
241 |     "name": "ipython",
242 |     "version": 3
243 |    },
244 |    "file_extension": ".py",
245 |    "mimetype": "text/x-python",
246 |    "name": "python",
247 |    "nbconvert_exporter": "python",
248 |    "pygments_lexer": "ipython3",
249 |    "version": "3.8.6"
250 |   }
251 |  },
252 |  "nbformat": 4,
253 |  "nbformat_minor": 5
254 | }
255 | 


--------------------------------------------------------------------------------
/src/MLproject:
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 1 | name: hr_analytics
 2 | 
 3 | conda_env: conda.yaml
 4 | 
 5 | entry_points:
 6 |   main:
 7 |     parameters:
 8 |       n_estimators: int
 9 |     
10 |     command: "python train.py {n_estimators} {balanced}"
11 | 
12 | 


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/src/conda.yaml:
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 1 | name: hr_analytics
 2 | 
 3 | channels:
 4 |   - defaults
 5 | 
 6 | dependencies:
 7 |   - numpy=1.20.1
 8 |   - pandas=1.2.2
 9 |   - scikit-learn=0.24.1
10 |   - pip:
11 |       - mlflow
12 | 


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/src/train.py:
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  1 | import sys
  2 | 
  3 | import pandas as pd
  4 | import numpy as np
  5 | 
  6 | from sklearn.preprocessing import LabelEncoder
  7 | from sklearn.model_selection import train_test_split
  8 | 
  9 | from sklearn.ensemble import RandomForestClassifier
 10 | from sklearn.metrics import (
 11 |     accuracy_score,
 12 |     precision_score,
 13 |     recall_score,
 14 |     f1_score,
 15 |     roc_auc_score,
 16 | )
 17 | from tqdm import tqdm
 18 | 
 19 | import mlflow
 20 | import mlflow.sklearn
 21 | 
 22 | # set registry URI i.e. where MLflow saves runs
 23 | 
 24 | # mlflow.set_tracking_uri("file:///Users/ahmed.besbes/projects/mlflow/mlruns")
 25 | 
 26 | 
 27 | mlflow.set_tracking_uri(
 28 |     "http://ec2-35-180-45-108.eu-west-3.compute.amazonaws.com:5000/"
 29 | )
 30 | 
 31 | 
 32 | # print("artifact store : ", mlflow.get_artifact_uri())
 33 | 
 34 | # load data
 35 | data = pd.read_csv("./../data/aug_train.csv")
 36 | targets = data[["target"]]
 37 | 
 38 | 
 39 | data.drop(["enrollee_id", "target"], inplace=True, axis=1)
 40 | 
 41 | # process features
 42 | ## fill in missing categorical variables and label encode them
 43 | 
 44 | categorical_features = []
 45 | numerical_features = []
 46 | 
 47 | for column in data.columns:
 48 |     dtype = str(data[column].dtype)
 49 |     if dtype in ["float64", "int64"]:
 50 |         numerical_features.append(column)
 51 |     else:
 52 |         categorical_features.append(column)
 53 | 
 54 | for categorical_feature in categorical_features:
 55 |     data[categorical_feature].fillna("missing", inplace=True)
 56 |     le = LabelEncoder()
 57 |     data[categorical_feature] = le.fit_transform(data[categorical_feature])
 58 | 
 59 | print("features processed")
 60 | 
 61 | # split train / test
 62 | 
 63 | x_train, x_test, y_train, y_test = train_test_split(
 64 |     data.values,
 65 |     targets.values.ravel(),
 66 |     test_size=0.3,
 67 |     random_state=2021,
 68 |     stratify=targets.values,
 69 | )
 70 | alpha = sys.argv[0] if len(sys.argv) > 1 else 0.5
 71 | 
 72 | 
 73 | # experiment_id = mlflow.create_experiment("training experiment")
 74 | experiment_id = mlflow.set_experiment("training experiment")
 75 | 
 76 | 
 77 | n_estimators_range = np.arange(100, 500, 25)
 78 | max_depth_range = np.arange(1, 25, 2)
 79 | max_features_range = ["sqrt", None, "log2"]
 80 | 
 81 | 
 82 | for n_estimators in tqdm(n_estimators_range):
 83 |     for max_depth in tqdm(max_depth_range, leave=False):
 84 |         for max_features in tqdm(max_features_range, leave=False):
 85 | 
 86 |             with mlflow.start_run(experiment_id=experiment_id):
 87 | 
 88 |                 model = RandomForestClassifier(
 89 |                     n_estimators=n_estimators,
 90 |                     max_depth=max_depth,
 91 |                     max_features=max_features,
 92 |                     n_jobs=3,
 93 |                 )
 94 | 
 95 |                 model.fit(x_train, y_train)
 96 |                 y_pred = model.predict(x_test)
 97 | 
 98 |                 accuracy = accuracy_score(y_test, y_pred)
 99 |                 precision = precision_score(y_test, y_pred)
100 |                 recall = recall_score(y_test, y_pred)
101 |                 f1 = f1_score(y_test, y_pred)
102 |                 auc = roc_auc_score(y_test, y_pred)
103 | 
104 |                 mlflow.log_param("n_estimators", n_estimators)
105 |                 mlflow.log_param("max_depth", max_depth)
106 |                 mlflow.log_param("max_features", max_features)
107 | 
108 |                 mlflow.log_metric("accuracy", accuracy)
109 |                 mlflow.log_metric("precision", precision)
110 |                 mlflow.log_metric("recall", recall)
111 |                 mlflow.log_metric("f1", f1)
112 |                 mlflow.log_metric("auc", auc)
113 | 
114 |                 mlflow.sklearn.log_model(model, "model")
115 | 


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