Data science and Machine learning Makefile for Python projects or in general :)

Makefile

# Thank you @Earthly https://www.youtube.com/watch?v=w2UeLF7EEwk
# Can be adapted to pipenv, and poetry
# Other languages coming soon especially R and Julia

# .ONESHELL tells make to run each recipe line in a single shell
.ONESHELL:

# .DEFAULT_GOAL tells make which target to run when no target is specified
.DEFAULT_GOAL := all

# Specify python location in virtual environment
# Specify pip location in virtual environment
PYTHON := .venv/bin/python3
PIP := .venv/bin/pip3
DOCKER_IMAGE_NAME := test_app
DOCKER_IMAGE_VERSION := v0.0.0
DOCKER_IMAGE_TAG := $(DOCKER_IMAGE_NAME):$(DOCKER_IMAGE_VERSION)

#-----------------------------------------------------------------
# Package Manager Detection
#-----------------------------------------------------------------

# Detect available package managers
# UV https://app.readytensor.ai/publications/uv-package-1yiSfLXTffSF?utm_id=12
DETECT_PIPENV := $(shell which pipenv)
DETECT_POETRY := $(shell which poetry)
DETECT_UV     := $(shell which uv)

# Set flags (1: available, 0: not available)
ifeq ($(DETECT_PIPENV),)
  HAS_PIPENV := 0
else
  HAS_PIPENV := 1
endif

ifeq ($(DETECT_POETRY),)
  HAS_POETRY := 0
else
  HAS_POETRY := 1
endif

ifeq ($(DETECT_UV),)
  HAS_UV := 0
else
  HAS_UV := 1
endif

#-----------------------------------------------------------------
# Default Package Manager Selection (priority: uv > pipenv > poetry)
#-----------------------------------------------------------------

ifeq ($(HAS_UV),1)
  PACKAGE_MANAGER := uv
else ifeq ($(HAS_PIPENV),1)
  PACKAGE_MANAGER := pipenv
else ifeq ($(HAS_POETRY),1)
  PACKAGE_MANAGER := poetry
else
  $(error No supported package manager found (pipenv, poetry, or uv))
endif

#-----------------------------------------------------------------
# Set Commands Based on the Package Manager
#-----------------------------------------------------------------

ifeq ($(PACKAGE_MANAGER),pipenv)
  INSTALL_COMMAND := pipenv install --dev
  RUN_COMMAND     := pipenv run
else ifeq ($(PACKAGE_MANAGER),poetry)
  INSTALL_COMMAND := poetry install
  RUN_COMMAND     := poetry run
else ifeq ($(PACKAGE_MANAGER),uv)
  INSTALL_COMMAND := uv pip install -e . -r requirements-dev.txt
  RUN_COMMAND     := python  # or python3, depending on your setup
endif

detect:
    @echo "Pipenv detected: $(HAS_PIPENV)"
    @echo "Poetry detected: $(HAS_POETRY)"
    @echo "UV detected: $(HAS_UV)"
	@echo "Package manager selected: $(PACKAGE_MANAGER)"


venv/bin/activate: requirements.txt
	# create virtual environment
	python3 -m venv .venv
	# make command executable
	chmod +x .venv/bin/activate
	# activate virtual environment
	. .venv/bin/activate

activate:
	# activate virtual environment
	. .venv/bin/activate

install: venv/bin/activate requirements.txt # prerequisite
	# install commands
	$(PIP) --no-cache-dir install --upgrade pip &&\
		$(PIP) --no-cache-dir install -r requirements.txt

docstring: activate
	# format docstring
	pyment -w -o numpydoc *.py

format: activate
	# format code
	black *.py utils/*.py testing/*.py

clean:
	# clean directory of cache
	rm -rf __pycache__ &&\
	rm -rf utils/__pycache__ &&\
	rm -rf testing/__pycache__ &&\
	rm -rf .pytest_cache &&\
	rm -rf .venv

lint: activate install format
	# flake8 or #pylint
	pylint --disable=R,C --errors-only *.py utils/*.py testing/*.py

setup_readme:  ## Create a README.md
	@if [ ! -f README.md ]; then \
		echo "# Project Name\n\
Description of the project.\n\n\
## Installation\n\
- Step 1\n\
- Step 2\n\n\
## Usage\n\
Explain how to use the project here.\n\n\
## Contributing\n\
Explain how to contribute to the project.\n\n\
## License\n\
License information." > README.md; \
		echo "README.md created."; \
	else \
		echo "README.md already exists."; \
	fi

test: activate install format
	# test
	$(PYTHON) -m pytest testing/*.py

run: activate install format lint
	# run application
  	# example $(PYTHON) app.py

review-code:
	@echo "#### General Code Review Prompt ####"
	@echo "1. Code Quality:"
	@echo "   - Architecture patterns"
	@echo "   - Design principles (SOLID, DRY, KISS)"
	@echo "   - Code complexity"
	@echo "   - Documentation quality"

	@echo "2. Reliability:"
	@echo "   - Single and multiple points of failure"
	@echo "   - Failover strategies"
	@echo "   - Resource management"
	@echo "   - Thread safety"

	@echo "3. Performance:"
	@echo "   - Algorithmic efficiency"
	@echo "   - Memory usage"
	@echo "   - I/O operations"
	@echo "   - Caching strategy"

review-ds:
	@echo "#### Data Science Review Prompt ####"
	@echo "1. Data Pipeline:"
	@echo "   - Data validation (schema, types, ranges)"
	@echo "   - Preprocessing steps (scaling, encoding, imputation)"
	@echo "   - Feature engineering (relevance, creation, selection)"
	@echo "   - Data versioning (tracking datasets)"
	@echo "   - Data leakage checks"
	@echo "   - Handling of missing or anomalous data"

	@echo "2. Model Development:"
	@echo "   - Algorithm selection rationale"
	@echo "   - Hyperparameter tuning methodology and tracking"
	@echo "   - Cross-validation strategy (appropriateness, implementation)"
	@echo "   - Choice of evaluation metrics (relevance to business goal)"
	@echo "   - Model interpretability/explainability methods"
	@echo "   - Bias and fairness assessment"
	@echo "   - Model persistence (saving/loading)"
	@echo "   - Code reproducibility (seeds, dependencies)"

	@echo "3. Production Readiness:"
	@echo "   - Scalability of prediction/inference code"
	@echo "   - Monitoring setup (technical and model performance metrics)"
	@echo "   - Logging for model inputs/outputs/errors"
	@echo "   - A/B testing or canary deployment capability"
	@echo "   - Model deployment pipeline (automation)"
	@echo "   - Rollback strategy for model updates"

	@echo "4. Experiment Tracking:"
	@echo "   - Are experiments logged (parameters, code versions, metrics)?"
	@echo "   - Is an experiment tracking tool used (e.g., MLflow, W&B)?"
	@echo "   - Are model artifacts versioned and stored?"

review-logging:
	@echo "#### Logging Review Prompt ####"
	@echo ""
	@echo "1. Level 1: Print Statements"
	@echo "   - Are there raw print() statements used for debugging?"
	@echo "   - Should these be replaced with proper logging?"
	@echo "   - Are any critical error conditions only visible via print statements?"
	@echo ""
	@echo "2. Level 2: Logging Libraries"
	@echo "   - Is a proper logging library being used consistently?"
	@echo "   - Are appropriate log levels (DEBUG, INFO, WARNING, ERROR) used?"
	@echo "   - Is logging properly configured (handlers, formatters)?"
	@echo "   - Are logs structured (JSON/key-value) for better analysis?"
	@echo "   - Is sensitive information protected from being logged?"
	@echo ""
	@echo "3. Level 3: Tracing"
	@echo "   - Is function/method entry/exit tracked for performance analysis?"
	@echo "   - Are execution paths through the code captured with timing data?"
	@echo "   - Are trace IDs used to track request flow?"
	@echo ""
	@echo "4. Level 4: Distributed Tracing"
	@echo "   - Is context propagated across service boundaries?"
	@echo "   - Are trace IDs maintained throughout the entire request lifecycle?"
	@echo "   - Is sampling strategy appropriate for production load?"
	@echo ""
	@echo "5. Level 5: Observability"
	@echo "   - Are logs, metrics, and traces integrated into a unified system?"
	@echo "   - Is there anomaly detection for unexpected behaviors?"
	@echo "   - Does the system provide business-level insights from technical data?"
	@echo "   - Is the observability stack scalable for production use?"

docker_build: Dockerfile
	# build container. Feel freee to change the platform given your needs
	# docker build -platform linux/amd64 -t plot-timeseries-app:v0 .
	# podman build -platform linux/amd64 -t plot-timeseries-app:v0 .

docker_run_test: Dockerfile
	# linting Dockerfile
	# podman run --rm -i hadolint/hadolint < Dockerfile
	docker run --rm -i hadolint/hadolint < Dockerfile

docker_clean: Dockerfile
	# remove dangling images, containers, volumes and networks
	# podman system prune -a
	docker system prune -a

docker_run: Dockerfile docker_build
	# run docker
	# # podman run --platform linux/amd64-e ENDPOINT_URL -e SECRET_KEY -e SPACES_ID -e SPACES_NAME plot-timeseries-app:v0
	# docker run --platform linux/amd64 -e ENDPOINT_URL -e SECRET_KEY -e SPACES_ID -e SPACES_NAME plot-timeseries-app:v0

docker_push: docker_build
	# push to registry
	# docker tag <my-image> registry.digitalocean.com/<my-registry>/<my-image>
	# docker push registry.digitalocean.com/<my-registry>/<my-image>
	# podman tag <my-image> registry.digitalocean.com/<my-registry>/<my-image>
	# podman push registry.digitalocean.com/<my-registry>/<my-image>

 help:
	@echo "Usage: make [target]"
	@echo ""
	@echo "Available targets:"
	@echo "  detect          Detect package managers"
	@echo "  activate        Activate virtual environment"
	@echo "  install         Install dependencies"
	@echo "  docstring       Format docstrings"
	@echo "  format          Format code"
	@echo "  clean           Clean directory of cache"
	@echo "  lint            Lint code"
	@echo "  test            Run tests"
	@echo "  run             Run application"
	@echo "  setup_readme    Create a README.md"
	@echo "  review-code     Code review prompt"
	@echo "  review-ds       Data Science review prompt"
	@echo "  review-logging  Logging implementation review prompt"
	@echo "  docker-build    Build Docker image $(DOCKER_IMAGE_TAG)"
	@echo "  docker_run_test Lint Dockerfile"
	@echo "  docker_clean    Remove dangling images, containers, volumes, and networks"
	@echo "  docker_run      Run Docker container"
	@echo "  docker_push     Push Docker container to registry"
	@echo "  help            Show this help message"

# .PHONY tells make that these targets do not represent actual files
.PHONY: activate format clean lint test build run docker_build docker_run docker_push docker_clean docker_run_test

all: install format lint test run docker_build docker_run docker_push

An attempt for java script

# Thank you @Earthly https://www.youtube.com/watch?v=w2UeLF7EEwk
# Can be adapted to pipenv, and poetry
# Other languages coming soon especially R and Julia

# .ONESHELL tells make to run each recipe line in a single shell
.ONESHELL:

# .DEFAULT_GOAL tells make which target to run when no target is specified
.DEFAULT_GOAL := all

# Specify node location in virtual environment
NODE := node

# Specify npm location in virtual environment
NPM := npm

# Specify Docker container name
DOCKER_CONTAINER_NAME := test_app:v0.0.0

venv/bin/activate: requirements.txt
    # create virtual environment
    npm install -g venv
    # make command executable
    chmod +x venv/bin/activate
    # activate virtual environment
    . venv/bin/activate

activate:
    # activate virtual environment
    . venv/bin/activate

install: venv/bin/activate requirements.txt # prerequisite
    # install commands
    npm install --no-cache-dir --upgrade npm &&\
        npm install -r requirements.txt

docstring: activate
    # format docstring
    javascriptdoc -w -o numpydoc *.js

format: activate 
    # format code
    prettier --write *.js utils/*.js testing/*.js

clean:
    # clean directory of cache
    rm -rf __pycache__ &&\
    rm -rf utils/__pycache__ &&\
    rm -rf testing/__pycache__ &&\
    rm -rf .pytest_cache &&\
    rm -rf .venv

lint: activate install format
    # eslint or #jshint
    eslint --fix --ignore-path .gitignore *.js utils/*.js testing/*.js

setup_readme:  ## Create a README.md
    @if [ ! -f README.md ]; then \
        echo "# Project Name\n\
Description of the project.\n\n\
## Installation\n\
- Step 1\n\
- Step 2\n\n\
## Usage\n\
Explain how to use the project here.\n\n\
## Contributing\n\
Explain how to contribute to the project.\n\n\
## License\n\
License information." > README.md; \
        echo "README.md created."; \
    else \
        
test: activate install format
    # test
    npm test

run: activate install format lint
    # run application
    # example node app.js

docker_build: Dockerfile
    # build container
    docker build -t plot-timeseries-app:v0 .

docker_run_test: Dockerfile
    # linting Dockerfile
    docker run --rm -i hadolint/hadolint < Dockerfile

docker_clean: Dockerfile
    # remove dangling images, containers, volumes and networks
    docker system prune -a

docker_run: Dockerfile docker_build
    # run docker
    # docker run -e ENDPOINT_URL -e SECRET_KEY -e SPACES_ID -e SPACES_NAME plot-timeseries-app:v0

docker_push: docker_build
  # push to registry
  # docker tag <my-image> registry.digitalocean.com/<my-registry>/<my-image>
  # docker push registry.digitalocean.com/<my-registry>/<my-image>  
  
 
# .PHONY tells make that these targets do not represent actual files
.PHONY: activate format clean lint test build run docker_build docker_run docker_push docker_clean docker_run_test
  
all: install format lint test run docker_build docker_run docker_push