feat(release): create v4.1.0 from mlops-python-package

.github/workflows/tests.yml

@@ -16,12 +16,12 @@ jobs:
     - name: Checkout
       uses: actions/checkout@v4
     - name: Install uv
-      uses: astral-sh/setup-uv@v4
+      uses: astral-sh/setup-uv@v5
       with:
         enable-cache: true
     - name: Setup Python
       uses: actions/setup-python@v5
       with:
         python-version-file: .python-version
     - name: Test
-      run: uv run invoke test
+      run: uv run just test

.pre-commit-config.yaml

@@ -2,10 +2,10 @@
 # https://pre-commit.com/hooks.html
 
 default_language_version:
-  python: python3.12
+  python: python3.13
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: 'v5.0.0'
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict

.python-version

@@ -1 +1 @@
-3.12
+3.13

README.md

@@ -16,7 +16,7 @@ The template provides a robust foundation for building, testing, packaging, and
 
 This [Cookiecutter](https://cookiecutter.readthedocs.io/) is designed to be a common ground for diverse MLOps environments. Whether you're working with [Kubernetes](https://www.kubeflow.org/), [Vertex AI](https://cloud.google.com/vertex-ai), [Databricks](https://www.databricks.com/), [Azure ML](https://azure.microsoft.com/en-us/products/machine-learning), or [AWS SageMaker](https://aws.amazon.com/sagemaker/), the core principles of using Python packages and Docker images remain consistent.
 
-This template equips you with the essentials for creating, testing, and packaging your AI/ML code, providing a solid base for integration into your chosen MLOps platform. To fully leverage its capabilities within a specific environment, you might need to combine it with external tools like [Airflow](https://airflow.apache.org/) for orchestration or platform-specific SDKs for deployment.
+This template equips you with the essentials for creating, testing, and packaging your AI/ML code, providing a solid base for [integration into your chosen MLOps platform](https://fmind.medium.com/stop-building-rigid-ai-ml-pipelines-embrace-reusable-components-for-flexible-mlops-6e165d837110). To fully leverage its capabilities within a specific environment, you might need to combine it with external tools like [Airflow](https://airflow.apache.org/) for orchestration or platform-specific SDKs for deployment.
 
 You have the freedom to structure your `src/` and `tests/` directories according to your preferences. Alternatively, you can draw inspiration from the structure used in the [MLOps Python Package](https://github.com/fmind/mlops-python-package) project for a ready-made implementation.
 
@@ -50,8 +50,8 @@ You'll be prompted for the following variables:
 - `license`: The license for your project.
 - `version`: The initial version of your project.
 - `description`: A brief description of your project.
-- `python_version`: The Python version to use (e.g., 3.12).
-- `mlflow_version`: The MLflow version to use (e.g., 2.19.0).
+- `python_version`: The Python version to use (e.g., 3.13).
+- `mlflow_version`: The MLflow version to use (e.g., 2.20.3).
 
 2. **Initialize a git repository:**
 
@@ -78,30 +78,33 @@ git init
 
 Use the provided Invoke tasks to manage your development workflow:
 
-- `invoke installs`: Install dependencies and pre-commit hooks.
-- `invoke formats`: Format your code.
-- `invoke checks`: Run code quality, type, security, and test checks.
-- `invoke docs`: Generate API documentation.
-- `invoke packages`: Build your Python package.
-- `invoke projects`: Run MLflow projects.
-- `invoke containers`: Build and run your Docker image.
+- `uv run just check`: Run code quality, type, security, and test checks.
+- `uv run just clean`: Clean up generated files.
+- `uv run just commit`: Commit changes to your repository.
+- `uv run just doc`: Generate API documentation.
+- `uv run just docker`: Build and run your Docker image.
+- `uv run just format`: Format your code with Ruff.
+- `uv run just install`: Install dependencies, pre-commit hooks, and GitHub rulesets.
+- `uv run just mlflow`: Start an Mlflow server.
+- `uv run just package`: Build your Python package.
+- `uv run just project`: Run the project in the CLI.
 
 ## Example Usage
 
-### Running an MLflow Project
+### Running the Project Script
 
 After installing dependencies and setting up MLflow:
 
 ```bash
-invoke projects
+uv run just project
 ```
 
 This will execute the job with the configuration file in your `confs` folder.
 
 ### Building and Running Your Docker Image
 
 ```bash
-invoke containers
+invoke docker
 ```
 
 This builds a Docker image based on your [`Dockerfile`](https://github.com/fmind/cookiecutter-mlops-package/blob/main/%7B%7Bcookiecutter.repository%7D%7D/Dockerfile) and runs it. The `CMD` in the Dockerfile executes your package with the `--help` flag.

cookiecutter-mlops-package.code-workspace

@@ -13,7 +13,6 @@
     },
     "extensions": {
         "recommendations": [
-			"dchanco.vsc-invoke",
             "ms-python.python"
         ]
     }

cookiecutter.json

@@ -6,8 +6,8 @@
     "license": "MIT",
     "version": "0.1.0",
     "description": "TODO",
-    "python_version": "3.12",
-    "mlflow_version": "2.19.0",
+    "python_version": "3.13",
+    "mlflow_version": "2.20.3",
     "__prompts__": {
         "user": "GitHub User",
         "name": "Project Name",

justfile

@@ -0,0 +1,33 @@
+# https://just.systems/man/en/
+
+# REQUIRES
+
+find := require("find")
+rm := require("rm")
+uv := require("uv")
+
+# DEFAULTS
+
+# display help information
+default:
+    @just --list
+
+# TASKS
+
+# clean the project
+clean:
+    rm -rf .venv/
+    rm -rf .mypy_cache/
+    rm -rf .pytest_cache/
+
+# install the project
+install:
+    uv sync --all-groups
+
+# setup the project hooks
+hooks:
+    uv run pre-commit install
+
+# run the project unit tests
+test:
+    uv run pytest tests/

pyproject.toml

@@ -5,23 +5,23 @@
 
 [project]
 name = "cookiecutter-mlops-package"
-version = "3.0.0"
+version = "4.1.0"
 description = "Build and deploy Python packages and Docker images for MLOps projects."
 authors = [{ name = "Médéric HURIER", email = "github@fmind.dev" }]
 readme = "README.md"
-requires-python = ">=3.12"
+requires-python = ">=3.13"
 license = { file = "LICENSE.txt" }
 
 # DEPENDENCIES
 
 [dependency-groups]
 dev = [
     "commitizen>=4.1.0",
-    "invoke>=2.2.0",
     "pre-commit>=4.0.1",
-    "pytest>=8.3.4",
     "pytest-cookies>=0.7.0",
     "pytest-shell-utilities>=1.9.7",
+    "pytest>=8.3.4",
+    "rust-just>=1.39.0",
 ]
 
 # TOOLS

tests/test_cookiecutter.py

@@ -9,15 +9,15 @@
 
 COMMANDS = [
     "git init",
-    "uv run invoke cleans.reset",
-    "uv run invoke installs",
-    "uv run invoke formats",
-    "uv run invoke checks",
-    "uv run invoke docs",
-    "uv run invoke projects",
-    "uv run invoke packages",
-    "uv run invoke containers",
-    "uv run invoke mlflow.doctor",
+    "uv run just clean",
+    "uv run just install",
+    "uv run just format",
+    "uv run just check",
+    "uv run just doc",
+    "uv run just project",
+    "uv run just package",
+    "uv run just docker",
+    "uv run just mlflow-doctor",
 ]
 
 # %% TESTS
@@ -26,14 +26,14 @@
 def test_project_generation(cookies: Cookies) -> None:
     """Test the generation of the project."""
     # given
-    context  = {
+    context = {
         "user": "tester",
         "name": "MLOps 123",
         "license": "apache-2",
         "version": "1.0.0",
         "description": "A test project.",
-        "python_version": "3.12",
-        "mlflow_version": "2.19.0",
+        "python_version": "3.13",
+        "mlflow_version": "2.20.3",
     }
     repository = context['name'].lower().replace(' ', '-')
     package = repository.replace('-', '_')
@@ -48,8 +48,8 @@ def test_project_generation(cookies: Cookies) -> None:
     assert result.context == {
         "user": context['user'],
         "name": context['name'],
-        "repository": repository,
         "package": package,
+        "repository": repository,
         "license": context['license'],
         "version": context['version'],
         "description": context['description'],