diff --git a/.dvcignore b/.dvcignore new file mode 100644 index 0000000..5197305 --- /dev/null +++ b/.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 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..77e2607 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,34 @@ +name: CI + +on: + push: + branches: [ main, dev ] + pull_request: + branches: [ main ] + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.11", "3.12"] + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v3 + with: + version: "latest" + + - name: Set up Python ${{ matrix.python-version }} + run: uv python install ${{ matrix.python-version }} + + - name: Install dependencies + run: make install + + - name: Run code quality checks (Linting, Formatting Check, Type Check, Pre-commit hooks) + run: make check-all + + - name: Run tests with coverage + run: make test-cov diff --git a/.gitignore b/.gitignore index 5824097..f429457 100644 --- a/.gitignore +++ b/.gitignore @@ -1,34 +1,11 @@ -# folders - -## IDEs -.idea/ -.vscode/ - -## Cache -__pycache__ -.ipynb_checkpoints - -## Project -experiments/* -models/* - - -# files - ## Mac OS .DS_Store - -## Configs -.env -config/*.json - +.venv/ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] -*$py.classdvc get https://github.com/iterative/dataset-registry \ - use-cases/cats-dogs - +*$py.class # C extensions *.so @@ -121,6 +98,12 @@ ipython_config.py # install all needed dependencies. #Pipfile.lock +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more @@ -133,8 +116,10 @@ ipython_config.py #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. -# https://pdm.fming.dev/#use-with-ide +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control .pdm.toml +.pdm-python +.pdm-build/ # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ @@ -185,3 +170,23 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the enitre vscode folder +# .vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Cursor +# Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to +# exclude from AI features like autocomplete and code analysis. Recommended for sensitive data +# refer to https://docs.cursor.com/context/ignore-files +.cursorignore +.cursorindexingignore diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..aeb65fc --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,63 @@ +# Pre-commit hooks to ensure code quality before commits +# Install: uv run pre-commit install +# Run manually: uv run pre-commit run --all-files + +repos: + # Basic file checks + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-toml + - id: check-added-large-files + args: ['--maxkb=2048'] # Allow up to 2MB files + - id: check-merge-conflict + - id: check-case-conflict + - id: debug-statements + exclude: ^tests/ # Allow debug statements in tests + + # Python code formatting and linting + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.11.11 + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] + types_or: [python, pyi, jupyter] + - id: ruff-format + types_or: [python, pyi, jupyter] + + # Type checking (with relaxed settings for MLOps course) + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.15.0 + hooks: + - id: mypy + args: [ + --config-file=pyproject.toml, + --explicit-package-bases, + --namespace-packages + ] + additional_dependencies: [ + pandas-stubs>=2.0.0, + types-PyYAML>=6.0.0, + types-requests>=2.31.0, + ] + exclude: ^tests/ # Skip type checking for tests initially + + # Security scanning + - repo: https://github.com/Yelp/detect-secrets + rev: v1.5.0 + hooks: + - id: detect-secrets + args: ['--baseline', '.secrets.baseline'] + exclude: ^(\.venv/|\.git/|htmlcov/|\.pytest_cache/|.*\.dvc$) + + # Additional Python checks + - repo: https://github.com/pycqa/bandit + rev: 1.8.3 + hooks: + - id: bandit + args: [--quiet] + files: ^src/.*\.py$ + exclude: ^tests/ diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..375f5ca --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11.6 diff --git a/.secrets.baseline b/.secrets.baseline new file mode 100644 index 0000000..77df53f --- /dev/null +++ b/.secrets.baseline @@ -0,0 +1,127 @@ +{ + "version": "1.5.0", + "plugins_used": [ + { + "name": "ArtifactoryDetector" + }, + { + "name": "AWSKeyDetector" + }, + { + "name": "AzureStorageKeyDetector" + }, + { + "name": "Base64HighEntropyString", + "limit": 4.5 + }, + { + "name": "BasicAuthDetector" + }, + { + "name": "CloudantDetector" + }, + { + "name": "DiscordBotTokenDetector" + }, + { + "name": "GitHubTokenDetector" + }, + { + "name": "GitLabTokenDetector" + }, + { + "name": "HexHighEntropyString", + "limit": 3.0 + }, + { + "name": "IbmCloudIamDetector" + }, + { + "name": "IbmCosHmacDetector" + }, + { + "name": "IPPublicDetector" + }, + { + "name": "JwtTokenDetector" + }, + { + "name": "KeywordDetector", + "keyword_exclude": "" + }, + { + "name": "MailchimpDetector" + }, + { + "name": "NpmDetector" + }, + { + "name": "OpenAIDetector" + }, + { + "name": "PrivateKeyDetector" + }, + { + "name": "PypiTokenDetector" + }, + { + "name": "SendGridDetector" + }, + { + "name": "SlackDetector" + }, + { + "name": "SoftlayerDetector" + }, + { + "name": "SquareOAuthDetector" + }, + { + "name": "StripeDetector" + }, + { + "name": "TelegramBotTokenDetector" + }, + { + "name": "TwilioKeyDetector" + } + ], + "filters_used": [ + { + "path": "detect_secrets.filters.allowlist.is_line_allowlisted" + }, + { + "path": "detect_secrets.filters.common.is_ignored_due_to_verification_policies", + "min_level": 2 + }, + { + "path": "detect_secrets.filters.heuristic.is_indirect_reference" + }, + { + "path": "detect_secrets.filters.heuristic.is_likely_id_string" + }, + { + "path": "detect_secrets.filters.heuristic.is_lock_file" + }, + { + "path": "detect_secrets.filters.heuristic.is_not_alphanumeric_string" + }, + { + "path": "detect_secrets.filters.heuristic.is_potential_uuid" + }, + { + "path": "detect_secrets.filters.heuristic.is_prefixed_with_dollar_sign" + }, + { + "path": "detect_secrets.filters.heuristic.is_sequential_string" + }, + { + "path": "detect_secrets.filters.heuristic.is_swagger_file" + }, + { + "path": "detect_secrets.filters.heuristic.is_templated_secret" + } + ], + "results": {}, + "generated_at": "2025-05-29T13:17:00Z" +} diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..6b32554 --- /dev/null +++ b/Makefile @@ -0,0 +1,67 @@ +.PHONY: help install test lint format clean run-pipeline tutorial-setup tutorial-clean tutorial-init tutorial-file tutorial-directory tutorial-changes tutorial-versions tutorial-remotes tutorial-advanced tutorial-pipelines tutorial-full tutorial-reset + +help: ## Show this help message + @echo "Available commands:" + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-20s\033[0m %s\n", $$1, $$2}' + +setup-hooks: ## Install pre-commit hooks + uv run pre-commit install + @echo "Pre-commit hooks installed! + +install: ## Install dependencies + uv sync --group dev + uv run pre-commit install + +pre-commit: ## Run pre-commit on all files + uv run pre-commit run --all-files + +autofix: lint-fix format ## Automatically fix linting issues and then format code + @echo "Code auto-fixing and formatting complete." + +type-check: ## Run mypy type checking separately + uv run mypy src/ --config-file=pyproject.toml + +check-all: lint format-check type-check test pre-commit ## Run all checks (lint + test) + @echo "All checks complete." + + +tutorial-clean: ## Clean up all DVC-related files and data (reset to initial state) + @echo "๐Ÿงน Cleaning up DVC tutorial state..." + @# Remove DVC directory and config + rm -rf .dvc/ + @# Remove all .dvc files + find . -name "*.dvc" -type f -delete + @# Remove data directories and files created during tutorial + rm -rf data/ + rm -rf datadir/ + rm -rf cats-dogs-untracked/ + rm -rf imported_data.xml* external_data.xml* + @# Remove pipeline files + rm -rf dvc.yaml dvc.lock + rm -rf model.weights.h5 metrics.csv + @# Remove temporary remote storage + rm -rf /tmp/dvc_remote_storage + @# Clean git index of any staged DVC files + -git reset HEAD *.dvc .dvc/ dvc.yaml 2>/dev/null || true + @# Remove any DVC-related entries from .gitignore that might have been added + @if [ -f .gitignore ]; then \ + grep -v "^/data" .gitignore > .gitignore.tmp && mv .gitignore.tmp .gitignore || true; \ + grep -v "^/datadir" .gitignore > .gitignore.tmp && mv .gitignore.tmp .gitignore || true; \ + fi + @# Remove Git branches created during tutorial + @echo "Switching to a safe branch before cleanup..." + @if git show-ref --verify --quiet refs/heads/main; then \ + git checkout main; \ + elif git show-ref --verify --quiet refs/heads/master; then \ + git checkout master; \ + else \ + git checkout -b cleanup-temp; \ + fi + @echo "Removing tutorial branches..." + @for branch in cats-dogs-v1 cats-dogs-v2 tutorial cleanup-temp; do \ + if git show-ref --verify --quiet refs/heads/$$branch && [ "$$branch" != "$$(git branch --show-current)" ]; then \ + echo "Deleting branch $$branch"; \ + git branch -D $$branch 2>/dev/null || true; \ + fi; \ + done + @echo "โœ… DVC tutorial cleanup complete! Repository reset to initial state." diff --git a/README.md b/README.md index fccd0db..3fe974d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,95 @@ -# Tutorial: Get started with Data Version Control (DVC) +# ๐Ÿš€ Tutorial: Data and Model Versioning with DVC -The project is outdated. Use https://github.com/mlrepa/mlops-starter-project-iris instead +**๐ŸŽ“ What is this?** This tutorial provides hands-on experience with **Data Version Control (DVC)**, demonstrating how to manage multiple datasets and machine learning model artifacts alongside your code. We'll use a classic image classification problem (cats vs. dogs) to illustrate DVC's capabilities for reproducibility and collaboration in MLOps. + +**๐Ÿ‘ฉโ€๐Ÿ’ป Who is this for?** ML Engineers, Data Scientists, and AI Developers who need to manage large datasets and models within Git repositories, ensuring experiment reproducibility and traceable deployments. A basic understanding of Python, Git, and command-line operations is assumed. + +**๐ŸŽฏ What will you learn?** + +- How to initialize a DVC project within a Git repository. + +* How to version large files and directories using `dvc add`. +- How DVC tracks data changes using `.dvc` files and a cache. +- How to switch between different versions of your data and models using `dvc checkout`. +- How to set up and use remote storage to store and share DVC-tracked data with `dvc push` and `dvc pull`. +- Advanced data access methods like `dvc get`, `dvc import`, and `dvc import-url`. +- The fundamentals of DVC pipelines using `dvc stage add` and `dvc repro` for automating ML workflows. + +## ๐Ÿ“‚ Project Structure + +```text +mlops-get-started-iris/ +โ”œโ”€โ”€ .github/ # GitHub specific configurations (e.g., Workflows for CI) +โ”œโ”€โ”€ docs/ +โ”‚ โ””โ”€โ”€ DEVELOPMENT.md # Detailed guide for developers on coding standards and tools +โ”œโ”€โ”€ .gitignore # Global Git ignore patterns for the project +โ”œโ”€โ”€ .pre-commit-config.yaml # Configuration for pre-commit hooks (Ruff, Mypy, etc.) +โ”œโ”€โ”€ .python-version # Specifies the preferred Python version (e.g., for pyenv or uv) +โ”œโ”€โ”€ .secrets.baseline # Baseline file for detect-secrets (prevents committing secrets) +โ”œโ”€โ”€ Makefile # Defines useful development commands (e.g., make lint, make test) +โ”œโ”€โ”€ pyproject.toml # Project configuration, dependencies, and tool settings (PEP 621) +โ””โ”€โ”€ uv.lock # Lock file for reproducible Python dependencies (generated by uv) +``` + +## ๐Ÿ› ๏ธ Prerequisites + +Ensure you have the following installed on your system: + +- **Python 3.11+**: We recommend using the version specified in the `.python-version` file. +- **`uv`**: A fast Python package installer and project manager. See the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/). + +## ๐Ÿš€ Quick Start: Installation & Setup + +1. **Clone the Repository:** + + ```bash + git clone + cd dvc-1-get-started + ``` + +2. **Set Up Python Environment** + + ```bash + # Create a virtual environment (e.g., named .venv) using the project's Python version + uv venv .venv --python 3.12 + + # Activate the virtual environment: + # On macOS and Linux: + source .venv/bin/activate + # On Windows (PowerShell): + # .\.venv\Scripts\Activate.ps1 + # On Windows (Command Prompt): + # .\.venv\Scripts\activate.bat + + ``` + +3. **Install Dependencies:** + We'll use `uv` to create a virtual environment and install dependencies. + + ```bash + # Install project dependencies, including development tools: + uv sync --dev + + # Initialize Pre-commit Hooks + uv run pre-commit install + ``` + + Alternatively, the `Makefile` provides a shortcut: + + ```bash + make install # This target in the Makefile should execute the uv commands above + ``` + +You're now ready to start! + +# โ–ถ๏ธ Running the Tutorial + +Open the `tutorial.md` file to start the tutorial. + +## ๐Ÿ’ป Development Workflow & Tools + +For a deeper dive into the development workflow, tool configurations, and contribution guidelines, please consult the [DEVELOPMENT.md](docs/DEVELOPMENT.md) file. + +--- + +**Happy DVC journey! ๐Ÿš€** diff --git a/data/.gitignore b/data/.gitignore index b164211..fc12be5 100644 --- a/data/.gitignore +++ b/data/.gitignore @@ -1,8 +1 @@ -/iris.csv -/iris2.csv -/hymenoptera_data -/features_iris.csv -/train.csv -/test.csv -/model.joblib -/eval.txt +/data.xml diff --git a/data/data.xml.dvc b/data/data.xml.dvc new file mode 100644 index 0000000..1fd0f22 --- /dev/null +++ b/data/data.xml.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 22a1a2931c8370d3aeedd7183606fd7f + size: 14445097 + hash: md5 + path: data.xml diff --git a/docs/DEVELOPMENT.md b/docs/DEVELOPMENT.md new file mode 100644 index 0000000..c54f8de --- /dev/null +++ b/docs/DEVELOPMENT.md @@ -0,0 +1,154 @@ +# ๐Ÿ’ป Development Guide + +This guide provides instructions and best practices for developing within this project. Our goal is to maintain high code quality, ensure reproducibility, and make collaboration easier through consistent tooling and workflows. + +## ๐Ÿš€ Getting Started: Environment Setup + +To get started, follow the [๐Ÿš€ Quick Start: Installation & Setup](../README.md#-quick-start-installation--setup) section in the main README.md + +## โœจ Code Quality & Consistency + +We use a suite of tools to maintain high code quality. Most of these can be run using `make` commands. Configuration for these tools can be found in `pyproject.toml`. + +1. **Formatting (Ruff Formatter):** Ensures consistent code style. + +- Check formatting: `make format-check` + - *What it does:* Checks if files adhere to formatting rules without changing them. Fails if reformatting is needed (useful for CI). + - *Underlying command:* `uv run ruff format --check src/ tests/` + +- Apply formatting: `make format` + - *What it does:* Automatically reformats your code. + - *Underlying command:* `uv run ruff format src/ tests/` + +2. **Linting (Ruff Linter):** Identifies potential bugs, style issues, and anti-patterns. + +- Check for linting issues: `make lint` + - *What it does:* Reports linting errors and warnings. + - *Underlying command:* `uv run ruff check src/ tests/` +- Attempt to auto-fix linting issues: `make lint-fix` (if available in your Makefile) + - *What it does:* Fixes many common linting issues automatically. + - *Underlying command:* `uv run ruff check --fix src/ tests/` + +3. **Static Type Checking (Mypy):** Helps catch type-related errors before runtime. + +- Run type checks: `make mypy` + - *What it does:* Analyzes your type hints and reports inconsistencies. + - *Underlying command:* `uv run mypy src/ tests/` + +4. **Combined Auto-Fixing & Formatting:** + +- Comprehensive fix: `make autofix` (if available in your Makefile) + - *What it does:* Typically runs `make lint-fix` and then `make format`. This is often the go-to command before committing. + +5. **All Checks (for CI simulation):** + +- Run all quality checks: `make check-all` (if available in your Makefile) + - *What it does:* This should ideally run `make format-check`, `make lint`, and `make mypy`. It simulates what the CI pipeline might do. + +## ๐Ÿงช Testing with Pytest + +We use `pytest` for writing and running unit tests. Tests are located in the `tests/` directory. + +1. **Run all tests:** `make test` + +- *What it does:* Discovers and runs all tests. +- *Underlying command:* `uv run pytest tests/ -v` (verbosity might vary) + +2. **Run tests with code coverage:** `make test-cov` + +- *What it does:* Runs tests and generates a report on how much of your code is covered by tests. +- *Underlying command:* `uv run pytest tests/ -v --cov=src --cov-report=xml --cov-report=term-missing` (or similar) + +3. **View HTML Coverage Report:** After running `make test-cov` (or `make test` if it includes coverage), open `htmlcov/index.html` in your web browser to see a detailed coverage report. + +## ๐Ÿ”„ Typical Development Workflow + +1. **(Optional but Recommended) Create a new branch:** + + ```bash + git checkout main # Or your development branch like 'dev' + git pull + git checkout -b feature/my-new-feature + ``` + +2. **Make your code changes** in the `src/` directory and add corresponding tests in `tests/`. +3. **Run linters and formatters frequently:** + + ```bash + make autofix # Or run lint-fix and format separately + ``` + +4. **Run type checks:** + + ```bash + make type-check + ``` + +5. **Run tests to ensure nothing is broken:** + + ```bash + make test # Or make test-cov + ``` + +6. **Review changes and stage them:** + + ```bash + git status + git add . + ``` + +7. **Commit your changes:** + + ```bash + git commit -m "Your descriptive commit message" + ``` + + - At this point, **pre-commit hooks will run automatically**. If they fail, address the issues, `git add` any modified files again, and re-commit. +8. **Push your branch (if using feature branches):** + + ```bash + git push origin feature/my-new-feature + ``` + +9. **Open a Pull Request** for review if applicable. + +## ๐Ÿ“ฆ Managing Dependencies + +Project dependencies are managed in `pyproject.toml` under the `[project.dependencies]` and `[project.optional-dependencies]` (e.g., `dev`) sections. `uv.lock` stores the exact versions. + +1. **To add a new runtime dependency:** + + ```bash + uv pip install + ``` + + This will update `pyproject.toml` and `uv.lock`. +2. **To add a new development dependency:** + + ```bash + uv pip install --dev + ``` + +3. **To update all dependencies to their latest allowed versions and regenerate `uv.lock`:** + + ```bash + uv pip compile pyproject.toml --all-extras -o uv.lock # Review changes before committing uv.lock + uv sync --dev # To install based on the new lock file + ``` + + Or, more simply, if you just want to update and install: + + ```bash + uv pip install -U # For a specific package + uv sync --dev # To update lockfile and install + ``` + + *Always commit your updated `pyproject.toml` and `uv.lock` files together.* + +## ๐Ÿค– Continuous Integration (CI) + +We use GitHub Actions for CI. The workflow is defined in `.github/workflows/ci.yml`. It typically runs `make check-all` (or individual linting, formatting, and testing commands) on every push and pull request to ensure the codebase remains healthy. + +--- + +Happy Developing! If you have questions or suggestions for improving this guide, please let us know. diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..0f21936 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,95 @@ +[project] +name = "dvc-1-get-started" +version = "0.1.0" +description = "MLOps starter project with DVC for data version control" +readme = "README.md" +requires-python = ">=3.11.6" + +# Core dependencies needed to run the project +dependencies = [ + + "dvc>=3.60.0", + "joblib>=1.5.1", + "pandas>=2.3.0", + "scikit-learn>=1.7.0", +] + +[dependency-groups] +# Dependencies needed only during development (testing, linting, etc.) +# Install with: uv sync --group dev +dev = [ + "mypy>=1.15.0", + "ruff>=0.11.11", + "pytest>=7.0.0", + "pytest-cov>=4.0.0", + "pre-commit>=4.2.0", + "detect-secrets>=1.5.0", +] + +[tool.ruff] +# Ruff is a super-fast Python linter and formatter. +line-length = 88 # Max characters per line (like Black's default) +target-version = "py311" # Assumes Python 3.11 features + +[tool.ruff.lint] +# Select which types of issues Ruff should check for. +# E/W: pycodestyle errors/warnings (PEP 8) +# F: Pyflakes (logical errors like unused imports/variables) +# I: isort (import sorting) +# B: flake8-bugbear (potential bugs and design issues) +# C4: flake8-comprehensions (more Pythonic list/dict/set comprehensions) +# UP: pyupgrade (suggests modern Python syntax) +select = ["E", "W", "F", "I", "B", "C4", "UP"] +ignore = [] # Add specific rule codes here to ignore them + +[tool.ruff.format] +# Configure Ruff's auto-formatting behavior. +quote-style = "double" # Prefer "double" quotes for strings +indent-style = "space" # Use spaces for indentation + +[tool.mypy] +# Mypy is a static type checker for Python. +python_version = "3.11" # Target Python version for type checking +warn_return_any = true # Warn if functions implicitly return 'Any' +warn_unused_configs = true # Warn about unused Mypy settings +disallow_untyped_defs = true # Require type hints for function definitions +disallow_incomplete_defs = true # Require all parts of a function signature to be typed +check_untyped_defs = true # Still type check the body of untyped functions +disallow_untyped_decorators = true # Require decorators to be typed +no_implicit_optional = true # Make 'Optional[T]' explicit (no 'T' implying 'T | None') +warn_redundant_casts = true # Warn if a type cast is unnecessary +warn_unused_ignores = true # Warn about '# type: ignore' comments that are no longer needed +warn_no_return = true # Warn if a function doesn't return when it should (or vice-versa) +warn_unreachable = true # Warn about code that can never be executed +strict_equality = true # Enforce stricter type checking for equality operations + +# Fix module path issues +explicit_package_bases = true +namespace_packages = true + +# Handle missing imports gracefully for MLOps libraries +ignore_missing_imports = true + +# Exclude problematic directories +exclude = [ + "^build/", + "^dist/", + "^\\.venv/", + "^\\.git/", +] + +# Specific overrides for ML libraries without type stubs +[[tool.mypy.overrides]] +module = [ + "sklearn.*", + "joblib.*", + "pandas.*", + "numpy.*", +] +ignore_missing_imports = true + +[tool.pytest.ini_options] +# Pytest is a popular Python testing framework. +testpaths = ["tests"] # # Directory where your tests are located +pythonpath = ["."] # This adds the project root to Python path +addopts = "--cov=src --cov-report=term-missing --cov-report=html" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 7429bc3..0000000 --- a/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -dvc==2.57.0 -pandas==2.0.1 -scikit-learn==1.2.2 -joblib==1.2.0 diff --git a/src/evaluate.py b/src/evaluate.py deleted file mode 100755 index c26ef17..0000000 --- a/src/evaluate.py +++ /dev/null @@ -1,30 +0,0 @@ -import json -import pandas as pd -from sklearn.metrics import confusion_matrix, f1_score -import joblib - - -if __name__ == '__main__': - - classes = pd.read_csv('data/iris.csv')['target'].unique().tolist() - - test_dataset = pd.read_csv('data/test.csv') - y = test_dataset.loc[:, 'target'].values.astype("float32") - X = test_dataset.drop('target', axis=1).values - - clf = joblib.load('data/model.joblib') - - prediction = clf.predict(X) - cm = confusion_matrix(prediction, y) - f1 = f1_score(y_true=y, y_pred=prediction, average='macro') - - json.dump( - obj={ - 'f1_score': f1, - 'confusion_matrix': { - 'classes': classes, - 'matrix': cm.tolist() - } - }, - fp=open('data/eval.txt', 'w') - ) diff --git a/src/featurize.py b/src/featurize.py deleted file mode 100755 index 34e83fc..0000000 --- a/src/featurize.py +++ /dev/null @@ -1,25 +0,0 @@ -import pandas as pd - - -def get_features(dataset): - - features = dataset.copy() - # Rename columns: replace (cm) and spaces - features.rename( - columns=lambda s: s.replace('(cm)', '').strip().replace(' ', '_'), - inplace=True - ) - - # uncomment for step 5.2 Add features - # features['sepal_length_to_sepal_width'] = features['sepal_length'] / features['sepal_width'] - # features['petal_length_to_petal_width'] = features['petal_length'] / features['petal_width'] - - return features - - -if __name__ == '__main__': - - dataset = pd.read_csv('data/iris.csv') - - features = get_features(dataset) - features.to_csv('data/features_iris.csv', index=False) diff --git a/src/load_data.py b/src/load_data.py deleted file mode 100644 index 0ac5938..0000000 --- a/src/load_data.py +++ /dev/null @@ -1,5 +0,0 @@ -from sklearn.datasets import load_iris - -data = load_iris(as_frame=True) -list(data.target_names) -data.frame.to_csv('data/iris.csv', index=False) diff --git a/src/split_dataset.py b/src/split_dataset.py deleted file mode 100755 index 2c8fb7d..0000000 --- a/src/split_dataset.py +++ /dev/null @@ -1,21 +0,0 @@ -import argparse -import pandas as pd - -from sklearn.model_selection import train_test_split - - -if __name__ == '__main__': - - arg_parser = argparse.ArgumentParser() - arg_parser.add_argument('--test_size', type=float) - args = arg_parser.parse_args() - - dataset = pd.read_csv('data/features_iris.csv') - - # Split in train/test - - df_train, df_test = train_test_split(dataset, test_size=args.test_size, random_state=42) - - df_train.to_csv('data/train.csv', index=False) - df_test.to_csv('data/test.csv', index=False) - diff --git a/src/train.py b/src/train.py deleted file mode 100755 index 42b45b4..0000000 --- a/src/train.py +++ /dev/null @@ -1,19 +0,0 @@ -import pandas as pd -from sklearn.linear_model import LogisticRegression -import joblib - - -if __name__ == '__main__': - - # Load train set - train_dataset = pd.read_csv('data/train.csv') - - # Get X and Y - y = train_dataset.loc[:, 'target'].values.astype("float32") - X = train_dataset.drop('target', axis=1).values - - # Create an instance of Logistic Regression Classifier and fit the data. - clf = LogisticRegression(C=0.01, solver='lbfgs', multi_class='multinomial', max_iter=100) - clf.fit(X, y) - - joblib.dump(clf, 'data/model.joblib') diff --git a/tutorial.md b/tutorial.md new file mode 100644 index 0000000..ccf382b --- /dev/null +++ b/tutorial.md @@ -0,0 +1,923 @@ +# ๐Ÿš€ Tutorial: Get started with Data Version Control (DVC) + +## ๐Ÿ‘€ Description + +`๐ŸŽ“ What is this?` This tutorial provides hands-on experience with **Data Version Control (DVC)**, demonstrating how to manage multiple datasets and machine learning model artifacts alongside your code. We'll use a classic image classification problem (cats vs. dogs) to illustrate DVC's capabilities for reproducibility and collaboration in MLOps. + +`๐Ÿ‘ฉโ€๐Ÿ’ป Who is this for?` ML Engineers, Data Scientists, and AI Developers who need to manage large datasets and models within Git repositories, ensuring experiment reproducibility and traceable deployments. A basic understanding of Python, Git, and command-line operations is assumed. + +`๐ŸŽฏ What will you learn?` + +- How to initialize a DVC project within a Git repository. +- How to version large files and directories using `dvc add`. +- How DVC tracks data changes using `.dvc` files and a cache. +- How to switch between different versions of your data and models using `dvc checkout`. +- How to set up and use remote storage to store and share DVC-tracked data with `dvc push` and `dvc pull`. +- Advanced data access methods like `dvc get`, `dvc import`, and `dvc import-url`. +- *The fundamentals of DVC pipelines using `dvc stage add` and `dvc repro` for automating ML workflows.* + +(This is a conceptual learning objective, even if the dedicated section is removed, as it's a core DVC concept.) + +`๐Ÿ” How is it structured?` We'll start by setting up our environment and performing basic file and directory versioning. Then, we'll demonstrate tracking changes and switching versions. Next, we'll configure remote storage for sharing data. Finally, we'll cover advanced data access patterns and introduce DVC pipelines for automated model versioning conceptually. + +`โฑ๏ธ How much time will it take?` Approximately **55-70 minutes** to complete, providing a solid foundation in DVC for your MLOps workflows. + +--- + +## ๐Ÿ“– Table of Contents + +- [โš™๏ธ 1. Setting Up Your Environment](#-1-setting-up-your-environment) + - [Step 1.2: Clone the Project Repository](#-step-12-clone-the-project-repository) + - [Step 1.3: Initialize Your Python Environment](#-step-13-initialize-your-python-environment) + - [Step 1.4: Initialize DVC in Your Project](#-step-14-initialize-dvc-in-your-project) +- [โญ 2. Versioning Your First Dataset (File)](#-2-versioning-your-first-dataset-file) + - [Step 2.1: Download Raw Data](#-step-21-download-raw-data) + - [Step 2.2: Add the File to DVC](#-step-22-add-the-file-to-dvc) + - [Step 2.3: Commit DVC-file to Git](#-step-23-commit-dvc-file-to-git) +- [๐Ÿ“ 3. Versioning a Directory (Cats & Dogs Dataset)](#-3-versioning-a-directory-cats--dogs-dataset) + - [Step 3.1: Create a New Git Branch](#-step-31-create-a-new-git-branch) + - [Step 3.2: Download the Cats & Dogs Dataset](#-step-32-download-the-cats--dogs-dataset) + - [Step 3.3: Add the Directory to DVC](#-step-33-add-the-directory-to-dvc) + - [Step 3.4: Commit the DVC-file and Tag the Version](#-step-34-commit-the-dvc-file-and-tag-the-version) +- [โ™ป๏ธ 4. Tracking Changes & Updating Data](#-4-tracking-changes--updating-data) + - [Step 4.1: Check Current DVC Status](#-step-41-check-current-dvc-status) + - [Step 4.2: Introduce a New Data Version](#-step-42-introduce-a-new-data-version) + - [Step 4.3: Capture the New Data Version with DVC](#-step-43-capture-the-new-data-version-with-dvc) +- [โช 5. Switching Between Data Versions (`dvc checkout`)](#-5-switching-between-data-versions-dvc-checkout) + - [Step 5.1: Switch to a Different Branch (`datadir` Removal)](#-step-51-switch-to-a-different-branch-datadir-removal) + - [Step 5.2: Restore a Specific Data Version](#-step-52-restore-a-specific-data-version) +- [โ˜๏ธ 6. Storing and Sharing Data with Remotes](#-6-storing-and-sharing-data-with-remotes) + - [Step 6.1: Set Up a Local Remote Storage](#-step-61-set-up-a-local-remote-storage) + - [Step 6.2: Push Data to Remote Storage](#-step-62-push-data-to-remote-storage) + - [Step 6.3: Retrieve Data from Remote Storage](#-step-63-retrieve-data-from-remote-storage) +- [๐Ÿ“ฅ 7. Advanced Data Access Commands](#-7-advanced-data-access-commands) + - [Step 7.1: Explore Remote DVC Repositories (`dvc list`)](#-step-71-explore-remote-dvc-repositories-dvc-list) + - [Step 7.2: Download Data Without Tracking (`dvc get`)](#-step-72-download-data-without-tracking-dvc-get) + - [Step 7.3: Download and Track Data (`dvc import`)](#-step-73-download-and-track-data-dvc-import) + - [Step 7.4: Track External URLs (`dvc import-url`)](#-step-74-track-external-urls-dvc-import-url) +- [๐Ÿ” 8. Understanding DVC File and Cache Internals](#-8-understanding-dvc-file-and-cache-internals) + - [Step 8.1: The `.dvc` File](#-step-81-the-dvc-file) + - [Step 8.2: The DVC Cache (`.dvc/cache`)](#-step-82-the-dvc-cache-dvc/cache) + - [Step 8.3: The `.dvc` Directory Structure](#-step-83-the-dvc-directory-structure) +- [๐Ÿ”— Additional Resources](#-additional-resources) +- [๐ŸŽ‰ Conclusion & Next Steps](#-conclusion--next-steps) + +--- + +## โš™๏ธ 1. Setting Up Your Environment + +Let's prepare your system for working with **DVC**. + +### Step 1.1: Prerequisites + +Make sure you have the following installed: + +- **Python 3.8+**: For running the `train.py` script. +- **Git**: Essential for version control of your code and **DVC-files**. +- **DVC (Data Version Control)**: Follow the [official DVC installation instructions](https://dvc.org/doc/install) if you don't have it already. +- **`unzip`**: A command-line tool for extracting `.zip` archives. + +### Step 1.2: Clone the Project Repository + +We'll start by cloning a ready-made **Git** repository that contains a `train.py` script and `requirements.txt`. + +```bash +git clone https://github.com/iterative/example-versioning.git +cd example-versioning +``` + +**Output (example):** + +``` +Cloning into 'example-versioning'... +remote: Enumerating objects: 49, done. +... +``` + +Now, let's create a new **Git** branch for our tutorial work to keep things organized. + +```bash +git checkout -b tutorial +``` + +**Output:** + +``` +Switched to a new branch 'tutorial' +``` + +### Step 1.3: Initialize Your Python Environment + +It's best practice to use a virtual environment for your **Python** projects to manage dependencies. + +1. **Create a virtual environment:** + + ```bash + python3 -m venv .env + ``` + +2. **Activate the virtual environment:** + + - On macOS/Linux: + + ```bash + source .env/bin/activate + ``` + + - On Windows (Command Prompt): + + ```bash + .env\Scripts\activate.bat + ``` + + - On Windows (PowerShell): + + ```bash + .env\Scripts\Activate.ps1 + ``` + + You should see `(.env)` prepended to your command prompt, indicating the virtual environment is active. + +3. **Install project requirements:** + + ```bash + pip install -r requirements.txt + ``` + + This might take a few minutes as it installs libraries like **TensorFlow**/ **Keras** for the dummy model. + +### Step 1.4: Initialize DVC in Your Project + +The first step in any **DVC** project is to initialize it within your **Git** repository. + +```bash +dvc init +``` + +**Output:** + +``` +Initialized DVC repository. +You can now add your first data file or folder to DVC: + dvc add + +... +``` + +Let's see what DVC initialization has done: + +```bash +ls -la .dvc/ +``` + +**Output (example):** + +``` +total 8 +drwxr-xr-x 4 user user 4096 Apr 1 10:00 . +drwxr-xr-x 8 user user 4096 Apr 1 10:00 .. +-rw-r--r-- 1 user user 122 Apr 1 10:00 config +drwxr-xr-x 2 user user 4096 Apr 1 10:00 tmp +-rw-r--r-- 1 user user 8 Apr 1 10:00 .gitignore +``` + +**DVC** creates a `.dvc/` directory and populates it with internal files. The most important one right now is `.dvc/config`, which stores **DVC**'s settings. It also creates a `.dvc/.gitignore` file to ensure **Git** ignores **DVC**'s internal **cache**. + +Now, let's add these **DVC**-related files to **Git** and commit them. This ties your **DVC** setup to your **Git** repository. + +```bash +git status +``` + +**Output (example):** + +``` +On branch tutorial +... +Untracked files: + (use "git add ..." to include in what will be committed) + .dvc/ +``` + +```bash +git add .dvc/ +git commit -m "Initialize DVC" +``` + +**Output:** + +``` +[tutorial 0c7d42a] Initialize DVC + 2 files changed, 5 insertions(+) + create mode 100644 .dvc/.gitignore + create mode 100644 .dvc/config +``` + +--- + +## โญ 2. Versioning Your First Dataset (File) + +Now that **DVC** is set up, let's learn how to track individual files. We'll start with a simple XML dataset. + +### Step 2.1: Download Raw Data + +We'll download a small `data.xml` file. Notice the `-o data/data.xml` flag, which specifies the output path. + +```bash +dvc get https://github.com/iterative/dataset-registry get-started/data.xml -o data/data.xml +``` + +**Output:** + +``` +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2.72K/2.72K [00:00] +``` + +`dvc get` is like `wget` or `curl` but specifically designed to download files tracked by **DVC** (or **Git**) repositories. + +### Step 2.2: Add the File to DVC + +Now, let's tell **DVC** to track this `data.xml` file. + +```bash +dvc add data/data.xml -v +``` + +**Output (example):** + +``` +Added 'data/data.xml' to DVC cache. +To share it, run 'dvc push'. +To put it under Git version control, run 'git add data/data.xml.dvc'. +``` + +What happened? + +- **DVC** moved the actual `data.xml` file into its **cache** (located in `.dvc/cache`). +- It replaced the original `data.xml` with a small **`.dvc` file** (specifically, `data/data.xml.dvc`). This `.dvc` file is a plain text file that acts as a pointer to the data's content in the **DVC cache**. +- **DVC** also automatically created `data/.gitignore` to tell **Git** to ignore the actual `data.xml` content, only tracking the `.dvc` pointer file. + +Let's inspect the `.dvc` file: + +```bash +cat data/data.xml.dvc +``` + +**Output (example):** + +``` +outs: +- md5: a304afb96060aad90176268345e10355 + path: data.xml + size: 2785 +``` + +This `.dvc` file contains metadata about the data: an **MD5 hash** of its content, its path, and its size. The **MD5 hash** is crucial as it uniquely identifies the data's content in the **DVC cache**. + +### Step 2.3: Commit DVC-file to Git + +Since `data.xml.dvc` is just a small text file, we can commit it to **Git**. This links the specific version of your data to your **Git** commit history. + +```bash +git add data/.gitignore data/data.xml.dvc +git commit -m "Add raw data.xml" +``` + +**Output:** + +``` +[tutorial 4d0e7f7] Add raw data.xml + 2 files changed, 6 insertions(+) + create mode 100644 data/.gitignore + create mode 100644 data/data.xml.dvc +``` + +Now, your **Git** repository tracks the *metadata* about your data, while **DVC** manages the large data content efficiently. + +--- + +## ๐Ÿ“ 3. Versioning a Directory (Cats & Dogs Dataset) + +**DVC** excels at versioning entire directories containing many files, which is common for image datasets. + +### Step 3.1: Create a New Git Branch + +Let's create a new **Git** branch to track our first version of the cats and dogs dataset. + +```bash +git checkout -b cats-dogs-v1 +``` + +**Output:** + +``` +Switched to a new branch 'cats-dogs-v1' +``` + +### Step 3.2: Download the Cats & Dogs Dataset + +We'll download a larger dataset consisting of cat and dog images into a directory named `datadir`. We're using `dvc get --rev` to specify a particular version of the dataset from the remote **DVC** repository. + +```bash +dvc get --rev cats-dogs-v1 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir +``` + +**Output (example):** + +``` +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 67.5M/67.5M [00:03] +``` + +This command downloads the dataset, which includes `train` and `validation` subdirectories for cats and dogs images. + +Let's confirm some files are there: + +```bash +ls datadir/data/train/cats +``` + +**Output (example):** + +``` +cat.1.jpg cat.10.jpg cat.100.jpg ... cat.999.jpg +``` + +### Step 3.3: Add the Directory to DVC + +Now, add the entire `datadir` to **DVC**. + +```bash +dvc add datadir +``` + +**Output (example):** + +``` +Added 'datadir' to DVC cache. +To share it, run 'dvc push'. +To put it under Git version control, run 'git add datadir.dvc'. +``` + +Similar to adding a file, `dvc add` moves the `datadir` content to the **DVC cache** and creates `datadir.dvc`. For directories, **DVC** creates a special `.dir` file in its **cache** that lists the hashes of all files and subdirectories within the tracked directory. This allows efficient versioning of large folder structures. + +Inspect the `.dvc` file for the directory: + +```bash +cat datadir.dvc +``` + +**Output (example):** + +``` +outs: +- md5: b6923e1e4ad16ea1a7e2b328842d56a2.dir + path: datadir +``` + +Notice the `.dir` suffix on the **MD5 hash**, indicating it refers to a directory hash. + +### Step 3.4: Commit the DVC-file and Tag the Version + +Commit the `datadir.dvc` file to **Git** and add a **Git** tag to mark this specific data version. + +```bash +git add .gitignore datadir.dvc # .gitignore is created by DVC when adding a dir +git commit -m "Add cats-dogs dataset (v1)" +git tag -a cats-dogs-v1 -m "Dataset version v1.0" +``` + +**Output:** + +``` +[cats-dogs-v1 2a3b4c5] Add cats-dogs dataset (v1) + 2 files changed, 4 insertions(+) + create mode 100644 .gitignore + create mode 100644 datadir.dvc +``` + +You now have a **Git** tag `cats-dogs-v1` that points to a specific version of your `datadir` dataset! + +--- + +## โ™ป๏ธ 4. Tracking Changes & Updating Data + +One of **DVC**'s core strengths is detecting and tracking changes to your data. + +### Step 4.1: Check Current DVC Status + +Use `dvc status` to see the state of your **DVC**-tracked files. + +```bash +dvc status +``` + +**Output:** + +``` +Data and pipelines are up to date. +``` + +This indicates that all **DVC**-tracked data in your workspace matches what **DVC** expects (i.e., the content pointed to by the `.dvc` files in your current **Git** commit). + +### Step 4.2: Introduce a New Data Version + +Let's simulate an update where our `cats-dogs` dataset doubles in size (e.g., new images added after more data collection). We'll switch to a new **Git** branch first. + +```bash +git checkout -b cats-dogs-v2 +``` + +**Output:** + +``` +Switched to a new branch 'cats-dogs-v2' +``` + +Now, download the updated dataset version. This will overwrite the `datadir` in your workspace. + +```bash +dvc get --rev cats-dogs-v2 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir +``` + +**Output (example):** + +``` +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 90.7M/90.7M [00:04] +``` + +The `datadir` in your project now contains the "v2" version of the data. + +Let's check `dvc status` again: + +```bash +dvc status +``` + +**Output (example):** + +``` +? datadir +``` + +The `?` indicates that `datadir` exists in your workspace but is **not tracked by DVC's current `.dvc` file**. **DVC** recognizes that the content of `datadir` has changed since the last `dvc add` operation. + +### Step 4.3: Capture the New Data Version with DVC + +To capture this new version of the data, we simply run `dvc add` again. + +```bash +dvc add datadir +``` + +**Output (example):** + +``` +M datadir +Updated 'datadir' in DVC cache. +To share it, run 'dvc push'. +To put it under Git version control, run 'git add datadir.dvc'. +``` + +**DVC** detects the change, updates its **cache**, and modifies `datadir.dvc` to point to the new content hash. + +Now, commit the updated `datadir.dvc` file to **Git** and tag it as `cats-dogs-v2`. + +```bash +git add datadir.dvc +git commit -m "Updated cats-dogs dataset (v2)" +git tag -a cats-dogs-v2 -m "Dataset version v2.0" +``` + +**Output:** + +``` +[cats-dogs-v2 b7d8e9f] Updated cats-dogs dataset (v2) + 1 file changed, 2 insertions(+), 2 deletions(-) +``` + +You now have two distinct versions of your dataset (`cats-dogs-v1` and `cats-dogs-v2`) linked to different **Git** tags, all managed efficiently by **DVC**. + +--- + +## โช 5. Switching Between Data Versions (`dvc checkout`) + +The power of **DVC** comes from its ability to quickly switch between different versions of your data, just like **Git** switches code versions. + +### Step 5.1: Switch to a Different Branch (`datadir` Removal) + +Let's switch back to our initial `tutorial` branch, which does *not* contain the `datadir.dvc` file in its **Git** history. + +```bash +git checkout tutorial +``` + +**Output:** + +``` +Switched to branch 'tutorial' +Your branch is up to date with 'origin/tutorial'. +``` + +Now, check your files: + +```bash +ls +``` + +**Output (example):** + +``` +.dvc .env data main.py requirements.txt train.py +``` + +Notice that `datadir` is gone from your workspace! This is because the `tutorial` branch's **Git** history doesn't track `datadir.dvc`. + +To ensure your workspace accurately reflects the **DVC-tracked data** for the *current Git commit*, you need to run `dvc checkout`. This command will synchronize your workspace with the **DVC cache** based on the `.dvc` files that are currently in your **Git** index. + +```bash +dvc checkout +``` + +**Output:** + +``` +M data/data.xml +D datadir +``` + +What happened? + +- `M data/data.xml`: If the `data/data.xml` in your workspace (from previous steps) was different from what `data/data.xml.dvc` (tracked on `tutorial` branch) points to, **DVC** updated it. +- `D datadir`: Since the `tutorial` branch's **Git** history *does not* include `datadir.dvc`, **DVC** recognized that the physical `datadir` directory in your workspace was no longer tracked by **DVC** for this **Git** commit. Thus, **DVC** **deleted** `datadir` from your workspace to keep it clean and consistent with the current **Git** branch's data versioning state. + +This demonstrates how **DVC** intelligently removes untracked (by **DVC**) data from your workspace when you switch to a **Git** commit that doesn't expect it. + +Let's specifically try to restore the `cats-dogs-v1` version. + +### Step 5.2: Restore a Specific Data Version + +We want to get back the `cats-dogs-v1` dataset. First, switch your **Git** repository to that version's tag. + +```bash +git checkout cats-dogs-v1 +``` + +**Output:** + +``` +Note: switching to 'cats-dogs-v1'. +... +HEAD is now at 2a3b4c5... Add cats-dogs dataset (v1) +``` + +Now, if you check your files: + +```bash +ls +``` + +**Output (example):** + +``` +.dvc .env data datadir main.py requirements.txt train.py +``` + +`datadir` is there, but its content is likely the `v2` content you just had. Why? Because `git checkout` only updates the `.dvc` *pointer files* in your workspace. It does *not* automatically fetch the actual data content. That's **DVC**'s job! + +To truly bring the `cats-dogs-v1` data content into your workspace, you must run `dvc checkout`: + +```bash +dvc checkout +``` + +**Output:** + +``` +M datadir +``` + +`M` indicates that `datadir` was modified (i.e., its content was swapped to the `v1` version). Now, your workspace's `datadir` should match the `cats-dogs-v1` content. + +```bash +ls datadir/data/train/dogs | head -n 5 +``` + +You can verify the number of files or file names to ensure you are on `v1` (e.g., v1 had 500 dogs, v2 had 1000). This demonstrates that **DVC** efficiently retrieves the correct data version from its **cache** without re-downloading or copying large files unnecessarily. + +--- + +## โ˜๏ธ 6. Storing and Sharing Data with Remotes + +**DVC**'s **cache** stores data locally. To share data with your team or deploy models, you need a **remote storage** location. This can be cloud storage (**S3**, **GCS**, **Azure Blob**), network file systems, or even a local directory. + +### Step 6.1: Set Up a Local Remote Storage + +For simplicity in this tutorial, we'll set up a local directory as our **DVC remote**. In a real-world scenario, this would be cloud storage. + +```bash +mkdir -p /tmp/dvc_remote_storage +dvc remote add -d local_storage /tmp/dvc_remote_storage +``` + +**Output (example):** + +``` +Setting 'local_storage' as a default remote. +``` + +- **`mkdir -p /tmp/dvc_remote_storage`**: Creates a temporary directory. + ๐Ÿ‘‰ **IMPORTANT:** In a real project, **NEVER** use `/tmp` for long-term storage, as it's frequently cleared by your system. Use a persistent directory or, ideally, cloud storage. +- **`dvc remote add -d local_storage /tmp/dvc_remote_storage`**: Tells **DVC** about a new **remote** named `local_storage` and sets it as the default (`-d`). + +This command modifies your `.dvc/config` file. Let's see: + +```bash +cat .dvc/config +``` + +**Output (example):** + +``` +['core'] + remote = local_storage +['remote "local_storage"'] + url = /tmp/dvc_remote_storage +``` + +Now, commit this configuration change to **Git** so your team knows where the data is supposed to be stored. + +```bash +git add .dvc/config +git commit -m "Add local DVC remote storage" +``` + +**Output:** + +``` +[cats-dogs-v1 8e9f0a1] Add local DVC remote storage + 1 file changed, 3 insertions(+) +``` + +### Step 6.2: Push Data to Remote Storage + +To upload the **DVC**-tracked data from your local **cache** to the configured **remote**, use `dvc push`. + +```bash +dvc push -v +``` + +**Output (example, showing data being pushed):** + +``` +Preparing to push data. +... +Pushing 'datadir' to 'local_storage' +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 10/10 [00:00] +... +Pushed data to 'local_storage'. +``` + +**DVC** only pushes the actual data content, not the `.dvc` pointer files (which are in **Git**). It's intelligent enough to only upload data that isn't already present in the **remote storage**. + +You can verify the data was pushed by checking the **remote** directory (it mirrors **DVC**'s **cache** structure): + +```bash +ls /tmp/dvc_remote_storage +``` + +**Output (example):** + +``` +a3 b6 +``` + +These are directories named after the first two characters of the data's **MD5 hashes**. + +### Step 6.3: Retrieve Data from Remote Storage + +Now, let's simulate a scenario where a new team member joins or you're working on a new machine. The data isn't in your local **DVC cache**. + +1. **Remove local cache and data:** + + ```bash + rm -rf .dvc/cache + rm -rf datadir + rm -rf data/data.xml # Clean up the individual file too + ``` + + Your workspace should now be clean of the actual data files. + +2. **Pull data from the remote:** + + ```bash + dvc pull -v + ``` + + **Output (example):** + + ``` + Preparing to pull data. + ... + 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2/2 [00:00] + Pulled data to 'local_storage'. + ``` + + `dvc pull` checks the `.dvc` files in your current **Git** commit, looks up the corresponding data hashes in the **remote**, downloads them to your local **DVC cache**, and then links them back into your workspace. + + Verify that your data is back: + + ```bash + ls datadir + ls data + ``` + + You should see both `datadir` and `data/data.xml` restored. This demonstrates how **DVC** enables seamless data sharing and environment setup for collaborative **ML** projects. + +--- + +## ๐Ÿ“ฅ 7. Advanced Data Access Commands + +**DVC** offers more specialized commands for accessing and incorporating data from other **DVC** repositories or external **URLs**. + +### Step 7.1: Explore Remote DVC Repositories (`dvc list`) + +You can explore the contents of any **DVC** repository hosted on a **Git** server without cloning it first. + +```bash +dvc list https://github.com/iterative/dataset-registry use-cases +``` + +**Output (example):** + +``` +cats-dogs/ +get-started/ +``` + +This shows the directories available under the `use-cases` path in that **DVC** dataset registry. + +### Step 7.2: Download Data Without Tracking (`dvc get`) + +You've already used `dvc get` earlier. It downloads data from a **DVC remote** repository to your current working directory, but it **does not** automatically add the downloaded data to *your* **DVC** project. + +Let's get `cats-dogs` again, but this time, into a new folder to confirm it's not **DVC**-tracked locally: + +```bash +dvc get https://github.com/iterative/dataset-registry use-cases/cats-dogs -o cats-dogs-untracked +``` + +**Output (example):** + +``` +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 90.7M/90.7M [00:04] +``` + +```bash +ls +``` + +You'll see `cats-dogs-untracked` directory. If you check `dvc status` or look for a `cats-dogs-untracked.dvc` file, you won't find one. This is useful when you just need a copy of data for temporary exploration or a one-off task. + +### Step 7.3: Download and Track Data (`dvc import`) + +`dvc import` is like `dvc get` followed by `dvc add`. It downloads data from another **DVC** repository *and* automatically registers it with your local **DVC** project, creating a `.dvc` file for it. + +```bash +dvc import git@github.com:iterative/example-get-started data/data.xml -o imported_data.xml +``` + +**Output (example):** + +``` +Importing 'data/data.xml' from 'git@github.com:iterative/example-get-started'. +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2.72K/2.72K [00:00] +``` + +Now, check your directory: + +```bash +ls +cat imported_data.xml.dvc +``` + +You'll see `imported_data.xml` and its corresponding `imported_data.xml.dvc` file, indicating it's now tracked by your **DVC** project. + +### Step 7.4: Track External URLs (`dvc import-url`) + +`dvc import-url` allows you to track data directly from an external **URL** (**HTTP/HTTPS**, **S3**, **GCS**, **Azure Blob**, etc.) into your **DVC** project. It downloads the data and creates a `.dvc` file for it. This is useful for managing external datasets that you don't control. + +```bash +dvc import-url https://data.dvc.org/get-started/data.xml -o external_data.xml +``` + +**Output (example):** + +``` +100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2.72K/2.72K [00:00] +``` + +You will find `external_data.xml` and `external_data.xml.dvc` in your project. If the data at the **URL** changes, `dvc update external_data.xml.dvc` (or `dvc pull`) can pull the new version. + +--- + +## ๐Ÿ” 8. Understanding DVC File and Cache Internals + +Let's take a closer look at the files and directories **DVC** creates and manages, which underpin its versioning capabilities. + +### Step 8.1: The `.dvc` File + +We've already seen examples like `data/data.xml.dvc` and `datadir.dvc`. These are small **YAML** files that act as pointers to the actual data content, which lives in **DVC**'s **cache**. + +For a single file (`data/data.xml.dvc`), the `.dvc` file usually contains: + +- `md5`: An **MD5 hash** of the file's content. This uniquely identifies the file. +- `path`: The relative path to the data file in the workspace. +- `size`: The size of the data file in bytes. + +For a directory (`datadir.dvc`), the `md5` field will end with `.dir`, indicating that the hash represents the content of the entire directory, not a single file. This "directory hash" is calculated based on the hashes of all files and subdirectories within it. + +### Step 8.2: The DVC Cache (`.dvc/cache`) + +The `.dvc/cache` directory is where **DVC** stores the actual content of your data and model files. + +```bash +ls -la .dvc/cache +``` + +**Output (example):** + +``` +drwxr-xr-x 4 user user 4096 Apr 1 10:00 . +drwxr-xr-x 8 user user 4096 Apr 1 10:00 .. +drwxr-xr-x 2 user user 4096 Apr 1 10:00 a3 +drwxr-xr-x 2 user user 4096 Apr 1 10:00 b6 +``` + +Inside `.dvc/cache`, **DVC** organizes files based on their **MD5 hash**. The first two characters of the hash form a subdirectory, and the rest become the filename within that subdirectory. This helps to prevent too many files in one directory and makes lookup efficient. + +For example, if your `data.xml` had an **MD5 hash** starting with `a3`, its content would be stored in `.dvc/cache/a3/04afb96060aad90176268345e10355`. If `datadir` had a directory hash starting with `b6`, its metadata (listing hashes of its contents) would be in `.dvc/cache/b6/923e1e4ad16ea1a7e2b328842d56a2.dir`. + +**Key Advantage:** If you have multiple **DVC**-tracked files with different names but *identical content*, **DVC** only stores one copy of that content in the **cache**, saving disk space. + +### Step 8.3: The `.dvc` Directory Structure + +The `.dvc/` directory is **DVC**'s internal workspace for your project. + +```bash +ls -la .dvc +``` + +**Output (example):** + +``` +drwxr-xr-x 9 user user 4096 Apr 1 10:00 . +drwxr-xr-x 8 user user 4096 Apr 1 10:00 .. +-rw-r--r-- 1 user user 122 Apr 1 10:00 config +drwxr-xr-x 2 user user 4096 Apr 1 10:00 cache +-rw-r--r-- 1 user user 8 Apr 1 10:00 .gitignore +drwxr-xr-x 2 user user 4096 Apr 1 10:00 tmp +# Other directories may appear depending on DVC version and features used: +# plots/, experiments/ etc. +``` + +- `.dvc/config`: Main **DVC** configuration (**remotes**, **cache** location, etc.). +- `.dvc/cache`: Where actual data/model content is stored. +- `.dvc/.gitignore`: Ensures **Git** ignores the **cache** and other **DVC internal files**. +- `.dvc/tmp`: Temporary files used by **DVC**. +- Other directories like `.dvc/plots` or `.dvc/experiments` might appear if you use **DVC**'s plotting or experiment management features. + +Understanding these internals helps you grasp how **DVC** efficiently links your **Git**-versioned code with your large, **DVC**-versioned data and models. + +--- + +## ๐Ÿ”— Additional Resources + +- **DVC Official Documentation:** [https://dvc.org/doc](https://dvc.org/doc) +- **DVC Get Started:** [https://dvc.org/doc/start](https://dvc.org/doc/start) +- **DVC User Guide (Files & Directories):** [https://dvc.org/doc/user-guide/dvc-files-and-directories](https://dvc.org/doc/user-guide/dvc-files-and-directories) +- **DVC Get Started: Data Pipelines:** [https://dvc.org/doc/start/data-pipelines](https://dvc.org/doc/start/data-pipelines) +- **Example: Tracking a remote file (for `dvc import-url`):** [https://dvc.org/doc/command-reference/import-url](https://dvc.org/doc/command-reference/import-url) + +--- + +## ๐ŸŽ‰ Conclusion & Next Steps + +Congratulations! You've successfully completed this hands-on tutorial on Data and Model Versioning with **DVC**. + +### Key Learnings + +- You've learned how to initialize **DVC** and version large files and directories alongside your **Git** repository. +- You now understand the crucial role of `.dvc` files and the **DVC cache** in maintaining data integrity and reproducibility. +- You've mastered how to switch between different data versions and how to efficiently share data using **DVC remotes**. +- You've gained an initial understanding of **DVC pipelines**, which allow you to track and reproduce entire **ML workflows**. + +### Where to go from here? + +- **Experiment Management (MLflow):** Explore how **DVC** integrates with **MLflow** for comprehensive experiment tracking, logging **metrics**, and managing models in a registry. +- **Continuous Integration/Delivery for ML (CI/CD for MLOps):** Learn how to set up **CI/CD pipelines** that leverage **DVC**'s `dvc repro` to automate model retraining, evaluation, and deployment upon code or data changes. +- **Advanced DVC Features:** Dive deeper into **DVC**'s capabilities for **metrics** and plots tracking, experiment versioning (`dvc exp`), and managing more complex data **dependencies**. +- **Cloud Remotes:** Transition from local **DVC remotes** to cloud storage solutions (**AWS S3**, **Google Cloud Storage**, **Azure Blob Storage**) for real-world production deployments. + +Keep practicing these **MLOps** fundamentals. The ability to version, reproduce, and share your data and models reliably is a superpower in modern **AI** development! + +[โฌ†๏ธ Back to Table of Contents](#-table-of-contents) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..f158ca0 --- /dev/null +++ b/uv.lock @@ -0,0 +1,2272 @@ +version = 1 +revision = 1 +requires-python = ">=3.11.6" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version < '3.12'", +] + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = 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