Replace poetry by uv

Author: fmind

docs/0. Overview/0.6. Resources.md

@@ -21,6 +21,7 @@ To propose a new project resource, please forward your suggestions to the course
 ## Additional vendor resources
 
 - **[MLOps Python Package](https://github.com/fmind/mlops-python-package)**
+- **[LLMOps Python Package](https://github.com/callmesora/llmops-python-package/)**
 - **[Cookiecutter MLOps Package](https://github.com/fmind/cookiecutter-mlops-package)**
 - [MLOps: Continuous delivery and automation pipelines in machine learning](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)
 - [The Big Book of MLOps](https://www.databricks.com/sites/default/files/2023-10/2023-10-eb-big-book-of-mlops-2nd-edition-v2-102723-final.pdf)

docs/1. Initializing/1.0. System.md

@@ -14,10 +14,10 @@ While [JupyterLab](https://jupyterlab.readthedocs.io/en/latest/) is an acceptabl
 
 ## Are additional software installations required?
 
-Engaging with this course material necessitates installing several key software packages, including Python, Poetry, git, and VS Code. These tools form the backbone of your development workflow:
+Engaging with this course material necessitates installing several key software packages, including Python, uv, git, and VS Code. These tools form the backbone of your development workflow:
 
 - **Python** is indispensable for all course-related coding activities.
-- **Poetry** offers an efficient way to manage Python package dependencies.
+- **uv** offers an efficient way to manage Python package dependencies.
 - **Git** is crucial for version control and collaboration.
 - **VS Code** is recommended for its integrated development environment (IDE) capabilities, although alternatives may be used based on personal preference or specific needs.
 

docs/1. Initializing/1.3. Poetry.md

@@ -126,14 +126,14 @@ These configurations ensure that Poetry creates virtual environments directly wi
 
 ## How can you install dependencies for your project with Poetry?
 
-Poetry differentiates between main (production) and development dependencies, offering an organized approach to dependency management. To add dependencies, use the following commands:
+Uv differentiates between main (production) and development dependencies, offering an organized approach to dependency management. To add dependencies, use the following commands:
 
 ```bash
 # For main dependencies
-$ poetry add pandas scikit-learn
+$ uv add pandas scikit-learn
 
 # For development dependencies
-$ poetry add -G dev ipykernel
+$ uv add --group dev ipykernel
 ```
 
 Executing these commands updates the `pyproject.toml` file, accurately managing and versioning your project's dependencies.
@@ -144,9 +144,9 @@ In production, you can decide to install only the main dependencies using [this
 poetry install --only main
 ```
 
-## What is the difference between main and dev dependencies in Poetry?
+## What is the difference between main and dev dependencies in uv?
 
-In Poetry, dependencies are divided into two types: main dependencies and development (dev) dependencies.
+In uv, dependencies are divided into two types: main dependencies and development (dev) dependencies.
 
 **Main Dependencies**: These are essential for your project's production environment—your application can't run without them. For example, libraries like Pandas or XGBoost would be main dependencies for an MLOps project.
 

docs/1. Initializing/1.4. git.md

@@ -49,7 +49,7 @@ When using Git, it's important to selectively track files. Consider the followin
 
 - **Exclude Secrets**: Sensitive data, such as API keys and passwords, should never be committed to your repository.
 - **Manage Large Files**: For files exceeding 100MB (e.g., dataset files), use Git Large File Storage ([git-lfs](https://git-lfs.github.com/)) instead of directly committing them to your Git repository.
-- **Omit Cache Files**: Do not track temporary or environment-specific files (e.g., `.venv`, `poetry.lock`, log files) that don't contribute to the project's primary function.
+- **Omit Cache Files**: Do not track temporary or environment-specific files (e.g., `.venv`, `mlruns`, log files) that don't contribute to the project's primary function.
 
 To exclude certain files and directories from being tracked, create a `.gitignore` file in your project's root directory. This file should list patterns to match filenames you wish to exclude, for example:
 

docs/1. Initializing/1.6. VS Code.md

@@ -238,19 +238,19 @@ You can find below some settings for configuring VS Code. Each setting is annota
 }
 ```
 
-## How to configure VS Code for using the [Jupyter Extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) with [Poetry](https://python-poetry.org/)?
+## How to configure VS Code for using the [Jupyter Extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) with [uv](https://docs.astral.sh/uv/)?
 
-To configure VS Code for using the [Jupyter Extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) with [Poetry](https://python-poetry.org/), follow these steps to ensure that your Poetry-managed virtual environment is recognized within VS Code. This allows you to use the Jupyter Extension seamlessly with the Python interpreter provided by Poetry.
+To configure VS Code for using the [Jupyter Extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) with [uv](https://docs.astral.sh/uv/), follow these steps to ensure that your uv-managed virtual environment is recognized within VS Code. This allows you to use the Jupyter Extension seamlessly with the Python interpreter provided by uv.
 
 1. **Install the Jupyter Extension**: First, ensure that the [Jupyter Extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) is installed in VS Code. You can find and install this extension from the [VS Code Marketplace](https://marketplace.visualstudio.com/VSCode).
-2. **Open Your Project in VS Code**: Open your project folder in VS Code. If you've just created a new Poetry project, this will be the directory containing your `pyproject.toml` file.
-3. **Select Python Interpreter**: To make VS Code use the Python interpreter from your Poetry environment:
+2. **Open Your Project in VS Code**: Open your project folder in VS Code. If you've just created a new uv project, this will be the directory containing your `pyproject.toml` file.
+3. **Select Python Interpreter**: To make VS Code use the Python interpreter from your uv environment:
     - Open the Command Palette (`Ctrl+Shift+P` on Windows/Linux, `Cmd+Shift+P` on macOS).
     - Type `Python: Select Interpreter` and select it.
-    - Look for the interpreter that corresponds to your Poetry environment. It will typically be located under the `.venv` path within your project directory or listed as a virtual environment with your project's name.
+    - Look for the interpreter that corresponds to your uv environment. It will typically be located under the `.venv` path within your project directory or listed as a virtual environment with your project's name.
     - Select the appropriate interpreter.
-4. **Verify Jupyter Notebook Configuration**: Create a new Jupyter notebook in VS Code (`*.ipynb` file) and verify that the cells execute using the Python interpreter from your Poetry environment. You can check the upper-right corner of the notebook interface to see which interpreter is currently active.
-5. **Install Necessary Libraries**: If you need additional Python libraries that are not yet part of your Poetry project, you can add them by running `poetry add <library-name>` in your terminal or command prompt. This ensures that all dependencies are managed by Poetry and available in the notebook.
+4. **Verify Jupyter Notebook Configuration**: Create a new Jupyter notebook in VS Code (`*.ipynb` file) and verify that the cells execute using the Python interpreter from your uv environment. You can check the upper-right corner of the notebook interface to see which interpreter is currently active.
+5. **Install Necessary Libraries**: If you need additional Python libraries that are not yet part of your uv project, you can add them by running `uv add <library-name>` in your terminal or command prompt. This ensures that all dependencies are managed by uv and available in the notebook.
 
 ## VS Code additional resources
 

docs/2. Prototyping/2.0. Notebooks.md

@@ -28,7 +28,7 @@ As an alternative to notebooks, consider using the [Python Interactive Window](h
 
 Using notebooks in the early stages of development offers many advantages; however, they are not well-suited for production environments due to several limitations:
 
-- **Lack of Integration**: Notebooks often do not integrate seamlessly with tools commonly used in the Python software development ecosystem, such as testing frameworks (pytest), linting tools (ruff), and package managers (poetry).
+- **Lack of Integration**: Notebooks often do not integrate seamlessly with tools commonly used in the Python software development ecosystem, such as testing frameworks (pytest), linting tools (ruff), and package managers (uv).
 - **Mixed Content**: The intermingling of code, output, and narrative in a single document can complicate version control and maintenance, especially with complex projects.
 - **Non-Sequential Flow**: Notebooks do not enforce a linear execution order, which can lead to confusion and errors if cells are run out of sequence.
 - **Lack of Reusability**: The format of notebooks does not naturally encourage the development of reusable and modular code, such as functions, classes, or packages.

docs/2. Prototyping/2.1. Imports.md

@@ -33,13 +33,13 @@ In the realm of data science, a few key Python packages form the backbone of mos
 - **[Matplotlib](https://matplotlib.org/) or [Plotly](https://plotly.com/)**: For creating static, interactive, and animated visualizations.
 - **[Scikit-learn](https://scikit-learn.org/stable/)**: For machine learning, providing simple and efficient tools for data analysis and modeling.
 
-To integrate these packages into your project using poetry, you can execute the following command in your terminal:
+To integrate these packages into your project using uv, you can execute the following command in your terminal:
 
 ```bash
-poetry add pandas numpy matplotlib scikit-learn plotly
+uv add pandas numpy matplotlib scikit-learn plotly
 ```
 
-This command tells poetry to download and install these packages, along with their dependencies, into your project environment, ensuring version compatibility and easy package management.
+This command tells uv to download and install these packages, along with their dependencies, into your project environment, ensuring version compatibility and easy package management.
 
 ## How should you organize your imports to facilitate your work?
 

docs/2. Prototyping/2.2. Configs.md

@@ -12,16 +12,20 @@ Here's a practical illustration of configs within a notebook context:
 
 ```python
 # Define paths for caching and training data
-CACHE_PATH = '../.cache/'
-TRAIN_DATA_PATH = '../data/train.csv'
+ROOT = Path("../")
+DATA = str(ROOT / "data")
+CACHE = str(ROOT / ".cache")
+HOUR = str(DATA / "hour.csv")
 # Configure random state for reproducibility
-RANDOM_STATE = 0
+RANDOM = 42
+# Define dataset columns for feature engineering
+INDEX = "instant"
+TARGET = "cnt"
 # Setup dataset parameters for testing and shuffling
-SHUFFLE = True
-TEST_SIZE = 0.2
-TARGET = "SalePrice"
+SPLITS = 4
+SHUFFLE = False  # required (time sensitive)
+TEST_SIZE = 24 * 30 * 2  # use 2 months for backtesting
 # Parameters for pipeline configurations
-CV = 5
 SCORING = "neg_mean_squared_error"
 PARAM_GRID = {
     "regressor__max_depth": [12, 15, 18, 21],

docs/2. Prototyping/2.4. Analysis.md

@@ -46,8 +46,8 @@ For instance, [Plotly Express's `scatter_matrix`](https://plotly.com/python/splo
 import plotly.express as px
 df = pd.read_csv('your_dataset.csv')
 px.scatter_matrix(
-df, dimensions=["feature1", "feature2", "feature3"], color="target_variable",
-height=800, title="Scatter Matrix of Features"
+    df, dimensions=["feature1", "feature2", "feature3"], color="target_variable",
+    height=800, title="Scatter Matrix of Features"
 )
 ```
 

docs/2. Prototyping/2.5. Modeling.md

@@ -135,7 +135,6 @@ PARAM_GRID = {
     "regressor__max_depth": [15, 20, 25],
     "regressor__n_estimators": [150, 200, 250],
 }
-RANDOM_STATE = 0
 
 splitter = model_selection.TimeSeriesSplit(n_splits=CV)