From c154ac8d9c447c31dc6f1d32dfe755eb435e1c40 Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 22:14:23 +0700 Subject: [PATCH 01/13] Update READEM - use uv --- README.md | 96 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 94 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index fccd0db..1a52d76 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Get started with Data Version Control (DVC) +# ๐Ÿš€ Tutorial: Data and Model Versioning with DVC -The project is outdated.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 MLOps journey! We hope this starter project helps you learn and experiment. ๐Ÿš€** From 3340e0059c0210713f8b253c3f474c5409e7788f Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 22:26:55 +0700 Subject: [PATCH 02/13] Update project setup --- .dvcignore | 3 + .github/workflows/ci.yml | 34 + .gitignore | 58 +- .pre-commit-config.yaml | 63 ++ .python-version | 1 + .secrets.baseline | 127 +++ Makefile | 262 +++++ data/.gitignore | 8 - docs/DEVELOPMENT.md | 154 +++ pyproject.toml | 95 ++ requirements.txt | 4 - src/evaluate.py | 30 - src/featurize.py | 25 - src/load_data.py | 5 - src/split_dataset.py | 21 - src/train.py | 19 - tutorial.md | 1069 ++++++++++++++++++ uv.lock | 2272 ++++++++++++++++++++++++++++++++++++++ 18 files changed, 4114 insertions(+), 136 deletions(-) create mode 100644 .dvcignore create mode 100644 .github/workflows/ci.yml create mode 100644 .pre-commit-config.yaml create mode 100644 .python-version create mode 100644 .secrets.baseline create mode 100644 Makefile delete mode 100644 data/.gitignore create mode 100644 docs/DEVELOPMENT.md create mode 100644 pyproject.toml delete mode 100644 requirements.txt delete mode 100755 src/evaluate.py delete mode 100755 src/featurize.py delete mode 100644 src/load_data.py delete mode 100755 src/split_dataset.py delete mode 100755 src/train.py create mode 100644 tutorial.md create mode 100644 uv.lock 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 ${{ matrix.python-version }} + + - name: Install dependencies + run: make install + + - name: Run code quality checks (Linting, Formatting Check, Type Check, Pre-commit hooks) + run: make check-all + + - name: Run tests with coverage + run: make test-cov diff --git a/.gitignore b/.gitignore index 5824097..7d6f9f2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,34 +1,16 @@ -# folders - -## IDEs -.idea/ -.vscode/ - -## Cache -__pycache__ -.ipynb_checkpoints - -## Project -experiments/* +# Data files +data/* +!data/.gitignore models/* - - -# files +!models/.gitignore ## Mac OS .DS_Store -## Configs -.env -config/*.json - - # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] -*$py.classdvc get https://github.com/iterative/dataset-registry \ - use-cases/cats-dogs - +*$py.class # C extensions *.so @@ -121,6 +103,12 @@ 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 +121,10 @@ 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 +175,23 @@ 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..ac00229 --- /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/) + + # 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..b5c8c60 --- /dev/null +++ b/Makefile @@ -0,0 +1,262 @@ +.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 +CODE_DIRS = src/ tests/ + +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 + + +format: ## Format code using Ruff formatter + @echo "Formatting code..." + uv run ruff format $(CODE_DIRS) + +format-check: ## Check if code is formatted correctly (for CI) + @echo "Checking code formatting..." + uv run ruff format --check $(CODE_DIRS) + +lint: ## Check for linting issues using Ruff (no fixes) + @echo "Linting code..." + uv run ruff check $(CODE_DIRS) + +lint-fix: ## Attempt to automatically fix linting issues using Ruff + @echo "Attempting to fix linting issues..." + uv run ruff check --fix $(CODE_DIRS) + +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." + + +# ============================================================================= +# DVC TUTORIAL TARGETS +# ============================================================================= + +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 + @echo "โœ… DVC tutorial cleanup complete! Repository reset to initial state." + +tutorial-init: ## Initialize DVC in the project (Step 1.4) + @echo "๐Ÿš€ Step 1.4: Initialize DVC in Your Project" + dvc init + @echo "๐Ÿ“ DVC initialized! Let's see what was created:" + ls -la .dvc/ + @echo "๐Ÿ“ Adding DVC files to Git..." + git add .dvc/ + git commit -m "Initialize DVC" + @echo "โœ… DVC initialization complete!" + +tutorial-file: tutorial-init ## Version your first dataset file (Section 2) + @echo "โญ Section 2: Versioning Your First Dataset (File)" + @echo "๐Ÿ“ฅ Step 2.1: Download Raw Data" + mkdir -p data + dvc get https://github.com/iterative/dataset-registry get-started/data.xml -o data/data.xml + @echo "๐Ÿ“ Step 2.2: Add the File to DVC" + dvc add data/data.xml -v + @echo "๐Ÿ‘€ Let's inspect the .dvc file:" + cat data/data.xml.dvc + @echo "๐Ÿ“ Step 2.3: Commit DVC-file to Git" + git add data/.gitignore data/data.xml.dvc + git commit -m "Add raw data.xml" + @echo "โœ… File versioning complete!" + +tutorial-directory: tutorial-file ## Version a directory dataset (Section 3) + @echo "๐Ÿ“ Section 3: Versioning a Directory (Cats & Dogs Dataset)" + @echo "๐ŸŒฟ Step 3.1: Create a New Git Branch" + git checkout -b cats-dogs-v1 + @echo "๐Ÿ“ฅ Step 3.2: Download the Cats & Dogs Dataset" + dvc get --rev cats-dogs-v1 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir + @echo "๐Ÿ“ Step 3.3: Add the Directory to DVC" + dvc add datadir + @echo "๐Ÿ‘€ Let's inspect the directory .dvc file:" + cat datadir.dvc + @echo "๐Ÿ“ Step 3.4: Commit the DVC-file and Tag the Version" + git add .gitignore datadir.dvc + git commit -m "Add cats-dogs dataset (v1)" + git tag -a cats-dogs-v1 -m "Dataset version v1.0" + @echo "โœ… Directory versioning complete!" + +tutorial-changes: tutorial-directory ## Track changes and update data (Section 4) + @echo "โ™ป๏ธ Section 4: Tracking Changes & Updating Data" + @echo "๐Ÿ” Step 4.1: Check Current DVC Status" + dvc status + @echo "๐ŸŒฟ Step 4.2: Introduce a New Data Version" + git checkout -b cats-dogs-v2 + dvc get --rev cats-dogs-v2 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir + @echo "๐Ÿ” Check status after data change:" + dvc status + @echo "๐Ÿ“ Step 4.3: Capture the New Data Version with DVC" + dvc add datadir + git add datadir.dvc + git commit -m "Updated cats-dogs dataset (v2)" + git tag -a cats-dogs-v2 -m "Dataset version v2.0" + @echo "โœ… Change tracking complete!" + +tutorial-versions: tutorial-changes ## Switch between data versions (Section 5) + @echo "โช Section 5: Switching Between Data Versions" + @echo "๐Ÿ”„ Step 5.1: Switch to Different Branch" + git checkout tutorial || git checkout main || git checkout master + @echo "๐Ÿ“ Current files:" + ls + @echo "๐Ÿ”„ DVC checkout to sync data:" + dvc checkout + @echo "๐Ÿ”„ Step 5.2: Restore Specific Data Version" + git checkout cats-dogs-v1 + @echo "๐Ÿ“ Files after git checkout:" + ls + @echo "๐Ÿ”„ DVC checkout to get v1 data:" + dvc checkout + @echo "โœ… Version switching complete!" + +tutorial-remotes: tutorial-versions ## Set up and use remotes (Section 6) + @echo "โ˜๏ธ Section 6: Storing and Sharing Data with Remotes" + @echo "๐Ÿ“ Step 6.1: Set Up a Local Remote Storage" + mkdir -p /tmp/dvc_remote_storage + dvc remote add -d local_storage /tmp/dvc_remote_storage + @echo "๐Ÿ‘€ Let's check the config:" + cat .dvc/config + git add .dvc/config + git commit -m "Add local DVC remote storage" + @echo "๐Ÿ“ค Step 6.2: Push Data to Remote Storage" + dvc push -v + @echo "๐Ÿ‘€ Check remote storage:" + ls /tmp/dvc_remote_storage + @echo "๐Ÿงน Step 6.3: Simulate clean environment and pull data" + @echo "Removing local cache and data..." + rm -rf .dvc/cache datadir data/data.xml + @echo "๐Ÿ“ฅ Pull data from remote:" + dvc pull -v + @echo "๐Ÿ“ Verify data is restored:" + ls datadir data + @echo "โœ… Remote storage setup complete!" + +tutorial-advanced: tutorial-remotes ## Advanced data access commands (Section 7) + @echo "๐Ÿ“ฅ Section 7: Advanced Data Access Commands" + @echo "๐Ÿ” Step 7.1: Explore Remote DVC Repositories" + dvc list https://github.com/iterative/dataset-registry use-cases + @echo "๐Ÿ“ฅ Step 7.2: Download Data Without Tracking (dvc get)" + dvc get https://github.com/iterative/dataset-registry use-cases/cats-dogs -o cats-dogs-untracked + @echo "๐Ÿ“ Check if it's tracked:" + ls cats-dogs-untracked + dvc status + @echo "๐Ÿ“ฅ Step 7.3: Download and Track Data (dvc import)" + -dvc import git@github.com:iterative/example-get-started data/data.xml -o imported_data.xml || \ + dvc import https://github.com/iterative/example-get-started data/data.xml -o imported_data.xml + @echo "๐Ÿ“ฅ Step 7.4: Track External URLs (dvc import-url)" + dvc import-url https://data.dvc.org/get-started/data.xml -o external_data.xml + @echo "๐Ÿ“ Check what was created:" + ls *.xml* | head -10 + @echo "โœ… Advanced data access complete!" + +tutorial-pipelines: tutorial-advanced ## Automate model versioning with pipelines (Section 8) + @echo "๐Ÿค– Section 8: Automating Model Versioning with DVC Pipelines" + @echo "๐Ÿ”„ Ensure we're on cats-dogs-v2 branch with data" + git checkout cats-dogs-v2 + dvc checkout + @echo "๐Ÿงน Clean up any existing pipeline artifacts" + -dvc remove model.weights.h5.dvc --force 2>/dev/null || true + -git rm --cached model.weights.h5.dvc 2>/dev/null || true + @echo "๐Ÿ“ Step 8.2: Define an ML Pipeline Stage" + @echo "Creating a simple train.py script for demonstration..." + @echo '#!/usr/bin/env python3' > train.py + @echo 'import json' >> train.py + @echo 'import os' >> train.py + @echo 'print("Training model with cats-dogs dataset...")' >> train.py + @echo 'print(f"Dataset size: {len(os.listdir("datadir/data/train/cats")) if os.path.exists("datadir/data/train/cats") else 0} cats")' >> train.py + @echo 'with open("model.weights.h5", "w") as f: f.write("fake_model_weights")' >> train.py + @echo 'with open("metrics.csv", "w") as f: f.write("accuracy,0.85\\nloss,0.23")' >> train.py + @echo 'print("Model saved to model.weights.h5, metrics to metrics.csv")' >> train.py + chmod +x train.py + dvc stage add -n train -d train.py -d datadir -o model.weights.h5 -M metrics.csv python train.py + @echo "๐Ÿ‘€ Check the generated dvc.yaml:" + cat dvc.yaml + git add dvc.yaml + git commit -m "Define train pipeline stage" + @echo "๐Ÿ”„ Step 8.3: Reproduce the Pipeline" + dvc repro + @echo "๐Ÿ“ Check generated files:" + ls model.weights.h5 metrics.csv + @echo "๐Ÿ”„ Run dvc repro again (should show up to date):" + dvc repro + @echo "โœ… Pipeline automation complete!" + +tutorial-full: tutorial-pipelines ## Run the complete tutorial from start to finish + @echo "๐ŸŽ‰ Complete DVC Tutorial finished!" + @echo "๐Ÿ“‹ Summary of what was accomplished:" + @echo " โœ… Initialized DVC" + @echo " โœ… Versioned files and directories" + @echo " โœ… Tracked data changes" + @echo " โœ… Switched between versions" + @echo " โœ… Set up remote storage" + @echo " โœ… Used advanced data access" + @echo " โœ… Created ML pipelines" + @echo "" + @echo "๐Ÿ” Current project state:" + @echo "Git branches:" + git branch -a + @echo "" + @echo "Git tags:" + git tag + @echo "" + @echo "DVC status:" + dvc status + @echo "" + @echo "๐ŸŽ“ Tutorial complete! Check tutorial.md for detailed explanations." + +tutorial-reset: tutorial-clean tutorial-init ## Reset and restart tutorial from beginning + @echo "๐Ÿ”„ Tutorial reset complete! Ready to start fresh with DVC initialized." + +tutorial-setup: ## Show tutorial setup and available commands + @echo "๐Ÿš€ DVC Tutorial Setup" + @echo "" + @echo "Available tutorial commands:" + @echo " make tutorial-clean - Clean up all DVC files and data" + @echo " make tutorial-init - Initialize DVC (Step 1.4)" + @echo " make tutorial-file - Version first dataset file (Section 2)" + @echo " make tutorial-directory - Version directory dataset (Section 3)" + @echo " make tutorial-changes - Track changes and updates (Section 4)" + @echo " make tutorial-versions - Switch between versions (Section 5)" + @echo " make tutorial-remotes - Set up remote storage (Section 6)" + @echo " make tutorial-advanced - Advanced data access (Section 7)" + @echo " make tutorial-pipelines - ML pipelines (Section 8)" + @echo " make tutorial-full - Run complete tutorial" + @echo " make tutorial-reset - Clean and restart from beginning" + @echo "" + @echo "๐Ÿ’ก Tip: Run 'make tutorial-full' to execute the entire tutorial automatically!" + @echo "๐Ÿ“– For detailed explanations, follow along with tutorial.md" diff --git a/data/.gitignore b/data/.gitignore deleted file mode 100644 index b164211..0000000 --- a/data/.gitignore +++ /dev/null @@ -1,8 +0,0 @@ -/iris.csv -/iris2.csv -/hymenoptera_data -/features_iris.csv -/train.csv -/test.csv -/model.joblib -/eval.txt 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..ae98fdb --- /dev/null +++ b/tutorial.md @@ -0,0 +1,1069 @@ +## ๐Ÿš€ Tutorial: Data and Model Versioning with 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. +* `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. +* `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 1.1: Prerequisites](#-step-11-prerequisites) + * [### 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 (Without `datadir`)](#-step-51-switch-to-a-different-branch-without-datadir) + * [### 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) +* [๐Ÿค– 8. Automating Model Versioning with DVC Pipelines](#-8-automating-model-versioning-with-dvc-pipelines) + * [### Step 8.1: Understand the `train.py` Script](#-step-81-understand-the-trainpy-script) + * [### Step 8.2: Define an ML Pipeline Stage (`dvc stage add`)](#-step-82-define-an-ml-pipeline-stage-dvc-stage-add) + * [### Step 8.3: Reproduce the Pipeline (`dvc repro`)](#-step-83-reproduce-the-pipeline-dvc-repro) +* [๐Ÿ” 9. Understanding DVC File and Cache Internals](#-9-understanding-dvc-file-and-cache-internals) + * [### Step 9.1: The `.dvc` File](#-step-91-the-dvc-file) + * [### Step 9.2: The DVC Cache (`.dvc/cache`)](#-step-92-the-dvc-cache-dvc/cache) + * [### Step 9.3: The `.dvc` Directory Structure](#-step-93-the-dvc-directory-structure) +* [๐Ÿ”— Additional Resources](#-additional-resources) +* [๐ŸŽ‰ Conclusion & Next Steps](#-conclusion--next-steps) + +--- + +## โš™๏ธ 1. Setting Up Your Environment + +Setup Instructions: + +For detailed, step-by-step instructions on how to: + +* Clone the project repository, +* Create a Python virtual environment using uv, and +* Install all required dependencies, + +please refer to the ๐Ÿ‘ฉโ€๐Ÿ’ป Quick Start: Installation & Setup section in the project's README.md file. + +Once you have successfully completed the setup steps outlined in the README.md, your environment will be ready, and you can proceed with this tutorial. + +### 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 (Without `datadir`) + +Let's switch back to our initial `tutorial` branch, which does *not* contain the `datadir.dvc` file. + +```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 doesn't have the `datadir.dvc` file in its Git history. + +To restore the DVC-tracked data that *should* be present for the current Git commit, you need to run `dvc checkout`. + +```bash +dvc checkout +``` + +**Output:** + +``` +M data/data.xml +A datadir +``` + +Wait, `datadir` suddenly appeared! That's because when you switched branches, `dvc checkout` detected that the `tutorial` branch's `data/data.xml.dvc` file points to a different version than what's currently in your workspace (it brought back the `data.xml` content) and also noticed `datadir` is not tracked there so it deleted it from your workspace to match the `tutorial` branch's Git state. This is an artifact of mixing different experiments on different branches. + +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, HDFS, 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. + +--- + +## ๐Ÿค– 8. Automating Model Versioning with DVC Pipelines + +While `dvc add` is great for raw data and final models, DVC also offers pipelines (`dvc stage add` and `dvc repro`) to version the *entire workflow* that produces a model, tying inputs, code, and outputs together. This is crucial for ML experiment reproducibility. + +### Step 8.1: Understand the `train.py` Script + +The `example-versioning` repository you cloned contains a `train.py` script. This script: + +1. Loads data from the `datadir` (the cats & dogs dataset). +2. Performs a pre-processing step (feature extraction). +3. Trains a simple image classification model. +4. Saves the trained model as `model.weights.h5`. +5. Saves training metrics to `metrics.csv`. + +For this tutorial, consider `train.py` as a "black box" โ€“ the specific ML algorithm or Keras knowledge is not required. Our focus is on how DVC helps manage its inputs and outputs. + +Let's ensure we're on a clean Git state, and remove any old `model.weights.h5.dvc` files if they exist from previous runs. + +```bash +git checkout cats-dogs-v2 # Ensure we're on a branch with datadir +dvc checkout # Ensure datadir is present +dvc remove model.weights.h5.dvc --force 2>/dev/null || true # Ignore error if file doesn't exist +git rm --cached model.weights.h5.dvc 2>/dev/null || true # Remove from Git index if needed +``` + +This prepares our workspace for defining the pipeline. + +### Step 8.2: Define an ML Pipeline Stage (`dvc stage add`) + +Instead of manually adding `model.weights.h5` and `metrics.csv` after `train.py` runs, we can define a DVC pipeline stage that encapsulates the entire process. + +```bash +dvc stage add -n train -d train.py -d datadir \ + -o model.weights.h5 -M metrics.csv \ + python train.py +``` + +**Output (example):** + +``` +'train' was added to your pipeline. To reproduce it, run 'dvc repro'. +``` + +What this command does: + +* **`dvc stage add -n train`**: Creates a new pipeline stage named `train`. +* **`-d train.py -d datadir`**: Declares `train.py` (the code) and `datadir` (the input data) as **dependencies** of this stage. If either changes, DVC knows the stage needs to be re-run. +* **`-o model.weights.h5`**: Declares `model.weights.h5` as an **output** of this stage. DVC will version this file automatically. +* **`-M metrics.csv`**: Declares `metrics.csv` as a **metric file** output. This allows DVC to track and compare metrics across experiments. +* **`python train.py`**: Specifies the **command** that DVC should run to execute this stage. + +This command also generates a `dvc.yaml` file (or updates it if it exists), which defines your ML pipeline. + +```bash +cat dvc.yaml +``` + +**Output (example):** + +```yaml +stages: + train: + cmd: python train.py + deps: + - train.py + - datadir + outs: + - model.weights.h5 + metrics: + - metrics.csv +``` + +This `dvc.yaml` file is plain text and versioned by Git, just like your code. It's the blueprint of your ML workflow. + +Commit the `dvc.yaml` file to Git: + +```bash +git add dvc.yaml +git commit -m "Define train pipeline stage" +``` + +**Output:** + +``` +[cats-dogs-v2 1f2g3h4] Define train pipeline stage + 1 file changed, 8 insertions(+) + create mode 100644 dvc.yaml +``` + +### Step 8.3: Reproduce the Pipeline (`dvc repro`) + +Now that the pipeline stage is defined, you can run it using `dvc repro`. + +```bash +dvc repro +``` + +**Output (example):** + +``` +Running stage 'train': + python train.py +... (output from train.py script, including Keras/TensorFlow logs) ... +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] +Reproduced stage 'train'. +``` + +`dvc repro` executes the `train` stage. It checks the dependencies (`train.py`, `datadir`). If any dependency has changed since the last run, it re-runs the command (`python train.py`) and captures the outputs (`model.weights.h5`, `metrics.csv`) into the DVC cache, updating their `.dvc` file pointers implicitly. + +If you run `dvc repro` again without changing anything, it will report: + +```bash +dvc repro +``` + +**Output:** + +``` +Data and pipelines are up to date. +``` + +This shows its ability to track and only re-run what's necessary, saving computation. + +๐Ÿ‘‰ **Real-World MLOps Relevance:** Using `dvc stage add` and `dvc repro` for pipelines is a cornerstone of reproducible ML. It means that anyone (your future self, a teammate, a CI/CD system) can execute `dvc repro` and get the *exact same* data and model outputs, even if they only have the Git repository. This is critical for reliable deployments and debugging. + +--- + +## ๐Ÿ” 9. 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 9.1: 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 9.2: 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 9.3: 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 seamlessly 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) + +--- + +## DVC Commands API Analysis and Suggestions for Updates + +The provided tutorial uses a very solid set of DVC commands that are still highly relevant and foundational. DVC's CLI has remained remarkably stable in its core functionality, but there have been refinements, new features, and changes in recommended best practices, especially around pipeline management. + +Here's an analysis of the commands used in the context of current DVC versions (e.g., 3.x and above, as of late 2023/early 2024): + +### Commands Analyzed + +* `dvc init` +* `dvc status` +* `dvc add` +* `dvc get` +* `dvc remote add` +* `dvc push` +* `dvc pull` +* `dvc list` +* `dvc import` +* `dvc import-url` +* `dvc remove` +* `dvc stage add` (formerly `dvc run`) +* `dvc repro` + +### Analysis and Suggestions + +1. **`dvc init`**: + * **Current State:** Remains the same. It's the essential first step. + * **Improvements/Tips:** None for the command itself. Emphasize committing `.dvc/` to Git immediately. + +2. **`dvc status`**: + * **Current State:** Works as described, showing discrepancies between workspace, cache, and `.dvc` files. + * **Improvements/Tips:** Can be used with `--jobs N` for larger projects, though not typically necessary in a tutorial. No major API changes. + +3. **`dvc add `**: + * **Current State:** Core command for data versioning. Behavior for files vs. directories is consistent. + * **Improvements/Tips:** + * The tutorial correctly introduces `dvc add` for `data.xml` and `datadir`. It's important to clarify that this is for *source data* or *external models* that you don't generate with a script *within* DVC. + * For outputs of scripts (like `model.weights.h5` or `metrics.csv`), `dvc stage add` (or the older `dvc run`) is indeed the **preferred and recommended method** as it provides lineage. The tutorial's "Automating Capturing" section correctly pivots to this. + +4. **`dvc get -o `**: + * **Current State:** Remains the command for downloading data from another DVC repository. + * **Improvements/Tips:** None for the command itself. The `--rev` flag used in the tutorial is excellent for fetching specific versions, which is highly valuable. + +5. **`dvc remote add [-d] `**: + * **Current State:** Essential for configuring remotes. + * **Improvements/Tips:** + * The tutorial uses a local path `/tmp/dvc_remote_storage`. For a real-world scenario, explicitly mention cloud providers (S3, GCS, Azure Blob, Rucio, WebDAV etc.) as the common use cases for `url`. Example: `dvc remote add my_s3_remote s3://my-bucket/dvc-store`. + * Briefly mention credential management for cloud remotes (e.g., DVC uses boto3 for AWS, google-cloud-storage for GCS, etc., relying on environment variables or config files). + +6. **`dvc push [-v]`**: + * **Current State:** Uploads cached data to the remote. + * **Improvements/Tips:** No major changes. It's robust. + +7. **`dvc pull [-v]`**: + * **Current State:** Downloads missing data to the local cache and links it to the workspace. + * **Improvements/Tips:** No major changes. It's robust. + +8. **`dvc list [path]`**: + * **Current State:** Useful for exploring DVC repositories remotely. + * **Improvements/Tips:** None for the command itself. + +9. **`dvc import -o `**: + * **Current State:** Imports data from another DVC repository and adds it to your project. + * **Improvements/Tips:** None. It's a convenient shorthand for `dvc get` + `dvc add` for external DVC-tracked data. + +10. **`dvc import-url -o `**: + * **Current State:** Imports data from *any* URL (not necessarily a DVC repo) and tracks it. + * **Improvements/Tips:** None. This is highly useful for integrating publicly available datasets or large external files directly into your DVC project. + +11. **`dvc remove `**: + * **Current State:** Removes a `.dvc` file and its corresponding data from the workspace, optionally from the cache. + * **Improvements/Tips:** The `--force` flag is often needed to delete the actual `.dvc` file from the workspace if it's already in the Git index. It's properly demonstrated for cleanup. + +12. **`dvc stage add [-n ] -d -o [-M ] `**: + * **Current State:** This command is the **modern and highly recommended way** to define pipeline stages. It replaces the older `dvc run` command. The tutorial correctly uses `dvc stage add`. + * **Improvements/Tips:** + * The tutorial uses it perfectly to define dependencies and outputs. + * Emphasize that this command creates/modifies `dvc.yaml`, the standard for DVC pipelines. + * For metrics, `dvc metrics show` and `dvc metrics diff` can be powerful follow-up commands to demonstrate after defining a metric output. This is hinted at in the original "What's next?" section and could be briefly demonstrated. + * Could mention `--live` flag for real-time metric tracking during training in more advanced scenarios (but out of scope for basic versioning). + +13. **`dvc repro [stage_name]`**: + * **Current State:** The core command to reproduce pipeline stages. + * **Improvements/Tips:** No major changes. Crucial for reproducibility. + +### Overall API Consistency and Evolution + +DVC's command-line interface is generally very consistent and intuitive, often mirroring Git commands where applicable (`add`, `checkout`, `push`, `pull`). The evolution from `dvc run` to `dvc stage add` was a positive step, making the terminology clearer and better aligned with pipeline concepts (`dvc.yaml`). + +The tutorial covers the essential commands comprehensively and explains their functionality well. No critical API syntax changes would render the tutorial commands obsolete. 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------ .pre-commit-config.yaml | 2 +- Makefile | 18 ------------------ data/.gitignore | 1 + data/data.xml.dvc | 5 +++++ 5 files changed, 7 insertions(+), 25 deletions(-) create mode 100644 data/.gitignore create mode 100644 data/data.xml.dvc diff --git a/.gitignore b/.gitignore index 7d6f9f2..45a9df9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,3 @@ -# Data files -data/* -!data/.gitignore -models/* -!models/.gitignore - ## Mac OS .DS_Store diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ac00229..aeb65fc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,7 +51,7 @@ repos: hooks: - id: detect-secrets args: ['--baseline', '.secrets.baseline'] - exclude: ^(\.venv/|\.git/|htmlcov/|\.pytest_cache/) + exclude: ^(\.venv/|\.git/|htmlcov/|\.pytest_cache/|.*\.dvc$) # Additional Python checks - repo: https://github.com/pycqa/bandit diff --git a/Makefile b/Makefile index b5c8c60..d6d1eff 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,4 @@ .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 -CODE_DIRS = src/ tests/ help: ## Show this help message @echo "Available commands:" @@ -16,23 +15,6 @@ install: ## Install dependencies pre-commit: ## Run pre-commit on all files uv run pre-commit run --all-files - -format: ## Format code using Ruff formatter - @echo "Formatting code..." - uv run ruff format $(CODE_DIRS) - -format-check: ## Check if code is formatted correctly (for CI) - @echo "Checking code formatting..." - uv run ruff format --check $(CODE_DIRS) - -lint: ## Check for linting issues using Ruff (no fixes) - @echo "Linting code..." - uv run ruff check $(CODE_DIRS) - -lint-fix: ## Attempt to automatically fix linting issues using Ruff - @echo "Attempting to fix linting issues..." - uv run ruff check --fix $(CODE_DIRS) - autofix: lint-fix format ## Automatically fix linting issues and then format code @echo "Code auto-fixing and formatting complete." diff --git a/data/.gitignore b/data/.gitignore new file mode 100644 index 0000000..fc12be5 --- /dev/null +++ b/data/.gitignore @@ -0,0 +1 @@ +/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 From 5b740d02addcf5b234fa499a27b3d5e487f33932 Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 22:59:37 +0700 Subject: [PATCH 05/13] Add cats-dogs dataset (v1) --- .gitignore | 1 + datadir.dvc | 6 ++++++ 2 files changed, 7 insertions(+) create mode 100644 datadir.dvc diff --git a/.gitignore b/.gitignore index 45a9df9..81a62b1 100644 --- a/.gitignore +++ b/.gitignore @@ -189,3 +189,4 @@ cython_debug/ # refer to https://docs.cursor.com/context/ignore-files .cursorignore .cursorindexingignore +/datadir diff --git a/datadir.dvc b/datadir.dvc new file mode 100644 index 0000000..e4b0a8b --- /dev/null +++ b/datadir.dvc @@ -0,0 +1,6 @@ +outs: +- md5: b6923e1e4ad16ea1a7e2b328842d56a2.dir + size: 41149064 + nfiles: 1800 + hash: md5 + path: datadir From 6fca5749fa8ea6bf247bd6f58acafa13b389ce6f Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:13:25 +0700 Subject: [PATCH 06/13] Update Makefile --- .dvc/.gitignore | 3 --- .dvc/config | 0 .gitignore | 1 - Makefile | 3 +++ data/.gitignore | 1 - data/data.xml.dvc | 5 ----- datadir.dvc | 6 ------ 7 files changed, 3 insertions(+), 16 deletions(-) delete mode 100644 .dvc/.gitignore delete mode 100644 .dvc/config delete mode 100644 data/.gitignore delete mode 100644 data/data.xml.dvc delete mode 100644 datadir.dvc diff --git a/.dvc/.gitignore b/.dvc/.gitignore deleted file mode 100644 index 528f30c..0000000 --- a/.dvc/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/config.local -/tmp -/cache diff --git a/.dvc/config b/.dvc/config deleted file mode 100644 index e69de29..0000000 diff --git a/.gitignore b/.gitignore index 81a62b1..45a9df9 100644 --- a/.gitignore +++ b/.gitignore @@ -189,4 +189,3 @@ cython_debug/ # refer to https://docs.cursor.com/context/ignore-files .cursorignore .cursorindexingignore -/datadir diff --git a/Makefile b/Makefile index d6d1eff..26924cc 100644 --- a/Makefile +++ b/Makefile @@ -52,6 +52,9 @@ tutorial-clean: ## Clean up all DVC-related files and data (reset to initial sta 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 + git checkout main + git branch -D cats-dogs-v1 cats-dogs-v2 tutorial @echo "โœ… DVC tutorial cleanup complete! Repository reset to initial state." tutorial-init: ## Initialize DVC in the project (Step 1.4) diff --git a/data/.gitignore b/data/.gitignore deleted file mode 100644 index fc12be5..0000000 --- a/data/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/data.xml diff --git a/data/data.xml.dvc b/data/data.xml.dvc deleted file mode 100644 index 1fd0f22..0000000 --- a/data/data.xml.dvc +++ /dev/null @@ -1,5 +0,0 @@ -outs: -- md5: 22a1a2931c8370d3aeedd7183606fd7f - size: 14445097 - hash: md5 - path: data.xml diff --git a/datadir.dvc b/datadir.dvc deleted file mode 100644 index e4b0a8b..0000000 --- a/datadir.dvc +++ /dev/null @@ -1,6 +0,0 @@ -outs: -- md5: b6923e1e4ad16ea1a7e2b328842d56a2.dir - size: 41149064 - nfiles: 1800 - hash: md5 - path: datadir From 095f88a24d1e4c46c3c94919b60755469f309f5f Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:16:16 +0700 Subject: [PATCH 07/13] Update Makefile --- Makefile | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 26924cc..881869c 100644 --- a/Makefile +++ b/Makefile @@ -53,11 +53,13 @@ tutorial-clean: ## Clean up all DVC-related files and data (reset to initial sta grep -v "^/datadir" .gitignore > .gitignore.tmp && mv .gitignore.tmp .gitignore || true; \ fi @# Remove Git branches created during tutorial - git checkout main + git checkout update git branch -D cats-dogs-v1 cats-dogs-v2 tutorial @echo "โœ… DVC tutorial cleanup complete! Repository reset to initial state." tutorial-init: ## Initialize DVC in the project (Step 1.4) + @echo "๐ŸŒฟ Step 1.4: Init project in 'tutorial' branch" + git checkout -b tutorial @echo "๐Ÿš€ Step 1.4: Initialize DVC in Your Project" dvc init @echo "๐Ÿ“ DVC initialized! Let's see what was created:" From 6df1c894ae75645ce5be1393551078a89ded66f2 Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:17:37 +0700 Subject: [PATCH 08/13] Initialize DVC --- .dvc/.gitignore | 3 +++ .dvc/config | 0 2 files changed, 3 insertions(+) create mode 100644 .dvc/.gitignore create mode 100644 .dvc/config diff --git a/.dvc/.gitignore b/.dvc/.gitignore new file mode 100644 index 0000000..528f30c --- /dev/null +++ b/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/.dvc/config b/.dvc/config new file mode 100644 index 0000000..e69de29 From 830ea05d4448f6b9798a3cf215d4223f2626fa68 Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:20:46 +0700 Subject: [PATCH 09/13] Add raw data.xml --- data/.gitignore | 1 + data/data.xml.dvc | 5 +++++ 2 files changed, 6 insertions(+) create mode 100644 data/.gitignore create mode 100644 data/data.xml.dvc diff --git a/data/.gitignore b/data/.gitignore new file mode 100644 index 0000000..fc12be5 --- /dev/null +++ b/data/.gitignore @@ -0,0 +1 @@ +/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 From 52c7c59e3e79737193663f4bd6545ccd67c021a9 Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:33:26 +0700 Subject: [PATCH 10/13] . --- .dvc/.gitignore | 3 - .dvc/config | 0 Makefile | 117 +++++++++++------------ tutorial.md | 245 +++--------------------------------------------- 4 files changed, 67 insertions(+), 298 deletions(-) delete mode 100644 .dvc/.gitignore delete mode 100644 .dvc/config diff --git a/.dvc/.gitignore b/.dvc/.gitignore deleted file mode 100644 index 528f30c..0000000 --- a/.dvc/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/config.local -/tmp -/cache diff --git a/.dvc/config b/.dvc/config deleted file mode 100644 index e69de29..0000000 diff --git a/Makefile b/Makefile index 881869c..c51dcac 100644 --- a/Makefile +++ b/Makefile @@ -53,13 +53,32 @@ tutorial-clean: ## Clean up all DVC-related files and data (reset to initial sta grep -v "^/datadir" .gitignore > .gitignore.tmp && mv .gitignore.tmp .gitignore || true; \ fi @# Remove Git branches created during tutorial - git checkout update - git branch -D cats-dogs-v1 cats-dogs-v2 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." -tutorial-init: ## Initialize DVC in the project (Step 1.4) +dvc-init: ## Initialize DVC in the project (Step 1.4) @echo "๐ŸŒฟ Step 1.4: Init project in 'tutorial' branch" - git checkout -b tutorial + @if git show-ref --verify --quiet refs/heads/tutorial; then \ + echo "Branch 'tutorial' exists, switching to it"; \ + git checkout tutorial; \ + else \ + echo "Creating new branch 'tutorial'"; \ + git checkout -b tutorial; \ + fi @echo "๐Ÿš€ Step 1.4: Initialize DVC in Your Project" dvc init @echo "๐Ÿ“ DVC initialized! Let's see what was created:" @@ -69,7 +88,7 @@ tutorial-init: ## Initialize DVC in the project (Step 1.4) git commit -m "Initialize DVC" @echo "โœ… DVC initialization complete!" -tutorial-file: tutorial-init ## Version your first dataset file (Section 2) +dvc-track-file: ## Version your first dataset file (Section 2) @echo "โญ Section 2: Versioning Your First Dataset (File)" @echo "๐Ÿ“ฅ Step 2.1: Download Raw Data" mkdir -p data @@ -83,10 +102,16 @@ tutorial-file: tutorial-init ## Version your first dataset file (Section 2) git commit -m "Add raw data.xml" @echo "โœ… File versioning complete!" -tutorial-directory: tutorial-file ## Version a directory dataset (Section 3) +dvc-track-directory: ## Version a directory dataset (Section 3) @echo "๐Ÿ“ Section 3: Versioning a Directory (Cats & Dogs Dataset)" @echo "๐ŸŒฟ Step 3.1: Create a New Git Branch" - git checkout -b cats-dogs-v1 + @if git show-ref --verify --quiet refs/heads/cats-dogs-v1; then \ + echo "Branch 'cats-dogs-v1' exists, switching to it"; \ + git checkout cats-dogs-v1; \ + else \ + echo "Creating new branch 'cats-dogs-v1'"; \ + git checkout -b cats-dogs-v1; \ + fi @echo "๐Ÿ“ฅ Step 3.2: Download the Cats & Dogs Dataset" dvc get --rev cats-dogs-v1 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir @echo "๐Ÿ“ Step 3.3: Add the Directory to DVC" @@ -99,12 +124,18 @@ tutorial-directory: tutorial-file ## Version a directory dataset (Section 3) git tag -a cats-dogs-v1 -m "Dataset version v1.0" @echo "โœ… Directory versioning complete!" -tutorial-changes: tutorial-directory ## Track changes and update data (Section 4) +dvc-track-changes: ## Track changes and update data (Section 4) @echo "โ™ป๏ธ Section 4: Tracking Changes & Updating Data" @echo "๐Ÿ” Step 4.1: Check Current DVC Status" dvc status @echo "๐ŸŒฟ Step 4.2: Introduce a New Data Version" - git checkout -b cats-dogs-v2 + @if git show-ref --verify --quiet refs/heads/cats-dogs-v2; then \ + echo "Branch 'cats-dogs-v2' exists, switching to it"; \ + git checkout cats-dogs-v2; \ + else \ + echo "Creating new branch 'cats-dogs-v2'"; \ + git checkout -b cats-dogs-v2; \ + fi dvc get --rev cats-dogs-v2 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir @echo "๐Ÿ” Check status after data change:" dvc status @@ -115,10 +146,18 @@ tutorial-changes: tutorial-directory ## Track changes and update data (Section 4 git tag -a cats-dogs-v2 -m "Dataset version v2.0" @echo "โœ… Change tracking complete!" -tutorial-versions: tutorial-changes ## Switch between data versions (Section 5) +dvc-switch-versions: ## Switch between data versions (Section 5) @echo "โช Section 5: Switching Between Data Versions" @echo "๐Ÿ”„ Step 5.1: Switch to Different Branch" - git checkout tutorial || git checkout main || git checkout master + @if git show-ref --verify --quiet refs/heads/tutorial; then \ + git checkout tutorial; \ + elif 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 \ + echo "No suitable branch found, staying on current branch"; \ + fi @echo "๐Ÿ“ Current files:" ls @echo "๐Ÿ”„ DVC checkout to sync data:" @@ -131,7 +170,7 @@ tutorial-versions: tutorial-changes ## Switch between data versions (Section 5) dvc checkout @echo "โœ… Version switching complete!" -tutorial-remotes: tutorial-versions ## Set up and use remotes (Section 6) +dvc-add-remotes: ## Set up and use remotes (Section 6) @echo "โ˜๏ธ Section 6: Storing and Sharing Data with Remotes" @echo "๐Ÿ“ Step 6.1: Set Up a Local Remote Storage" mkdir -p /tmp/dvc_remote_storage @@ -153,7 +192,7 @@ tutorial-remotes: tutorial-versions ## Set up and use remotes (Section 6) ls datadir data @echo "โœ… Remote storage setup complete!" -tutorial-advanced: tutorial-remotes ## Advanced data access commands (Section 7) +dvc-advanced: ## Advanced data access commands (Section 7) @echo "๐Ÿ“ฅ Section 7: Advanced Data Access Commands" @echo "๐Ÿ” Step 7.1: Explore Remote DVC Repositories" dvc list https://github.com/iterative/dataset-registry use-cases @@ -171,39 +210,8 @@ tutorial-advanced: tutorial-remotes ## Advanced data access commands (Section 7) ls *.xml* | head -10 @echo "โœ… Advanced data access complete!" -tutorial-pipelines: tutorial-advanced ## Automate model versioning with pipelines (Section 8) - @echo "๐Ÿค– Section 8: Automating Model Versioning with DVC Pipelines" - @echo "๐Ÿ”„ Ensure we're on cats-dogs-v2 branch with data" - git checkout cats-dogs-v2 - dvc checkout - @echo "๐Ÿงน Clean up any existing pipeline artifacts" - -dvc remove model.weights.h5.dvc --force 2>/dev/null || true - -git rm --cached model.weights.h5.dvc 2>/dev/null || true - @echo "๐Ÿ“ Step 8.2: Define an ML Pipeline Stage" - @echo "Creating a simple train.py script for demonstration..." - @echo '#!/usr/bin/env python3' > train.py - @echo 'import json' >> train.py - @echo 'import os' >> train.py - @echo 'print("Training model with cats-dogs dataset...")' >> train.py - @echo 'print(f"Dataset size: {len(os.listdir("datadir/data/train/cats")) if os.path.exists("datadir/data/train/cats") else 0} cats")' >> train.py - @echo 'with open("model.weights.h5", "w") as f: f.write("fake_model_weights")' >> train.py - @echo 'with open("metrics.csv", "w") as f: f.write("accuracy,0.85\\nloss,0.23")' >> train.py - @echo 'print("Model saved to model.weights.h5, metrics to metrics.csv")' >> train.py - chmod +x train.py - dvc stage add -n train -d train.py -d datadir -o model.weights.h5 -M metrics.csv python train.py - @echo "๐Ÿ‘€ Check the generated dvc.yaml:" - cat dvc.yaml - git add dvc.yaml - git commit -m "Define train pipeline stage" - @echo "๐Ÿ”„ Step 8.3: Reproduce the Pipeline" - dvc repro - @echo "๐Ÿ“ Check generated files:" - ls model.weights.h5 metrics.csv - @echo "๐Ÿ”„ Run dvc repro again (should show up to date):" - dvc repro - @echo "โœ… Pipeline automation complete!" - -tutorial-full: tutorial-pipelines ## Run the complete tutorial from start to finish + +dvc-full: dvc-init dvc-track-file dvc-track-directory dvc-track-changes dvc-switch-versions dvc-add-remotes dvc-advanced @echo "๐ŸŽ‰ Complete DVC Tutorial finished!" @echo "๐Ÿ“‹ Summary of what was accomplished:" @echo " โœ… Initialized DVC" @@ -212,22 +220,7 @@ tutorial-full: tutorial-pipelines ## Run the complete tutorial from start to fin @echo " โœ… Switched between versions" @echo " โœ… Set up remote storage" @echo " โœ… Used advanced data access" - @echo " โœ… Created ML pipelines" - @echo "" - @echo "๐Ÿ” Current project state:" - @echo "Git branches:" - git branch -a - @echo "" - @echo "Git tags:" - git tag - @echo "" - @echo "DVC status:" - dvc status - @echo "" - @echo "๐ŸŽ“ Tutorial complete! Check tutorial.md for detailed explanations." -tutorial-reset: tutorial-clean tutorial-init ## Reset and restart tutorial from beginning - @echo "๐Ÿ”„ Tutorial reset complete! Ready to start fresh with DVC initialized." tutorial-setup: ## Show tutorial setup and available commands @echo "๐Ÿš€ DVC Tutorial Setup" @@ -241,9 +234,7 @@ tutorial-setup: ## Show tutorial setup and available commands @echo " make tutorial-versions - Switch between versions (Section 5)" @echo " make tutorial-remotes - Set up remote storage (Section 6)" @echo " make tutorial-advanced - Advanced data access (Section 7)" - @echo " make tutorial-pipelines - ML pipelines (Section 8)" @echo " make tutorial-full - Run complete tutorial" - @echo " make tutorial-reset - Clean and restart from beginning" @echo "" @echo "๐Ÿ’ก Tip: Run 'make tutorial-full' to execute the entire tutorial automatically!" @echo "๐Ÿ“– For detailed explanations, follow along with tutorial.md" diff --git a/tutorial.md b/tutorial.md index ae98fdb..dc3f2ba 100644 --- a/tutorial.md +++ b/tutorial.md @@ -51,14 +51,10 @@ * [### 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) -* [๐Ÿค– 8. Automating Model Versioning with DVC Pipelines](#-8-automating-model-versioning-with-dvc-pipelines) - * [### Step 8.1: Understand the `train.py` Script](#-step-81-understand-the-trainpy-script) - * [### Step 8.2: Define an ML Pipeline Stage (`dvc stage add`)](#-step-82-define-an-ml-pipeline-stage-dvc-stage-add) - * [### Step 8.3: Reproduce the Pipeline (`dvc repro`)](#-step-83-reproduce-the-pipeline-dvc-repro) -* [๐Ÿ” 9. Understanding DVC File and Cache Internals](#-9-understanding-dvc-file-and-cache-internals) - * [### Step 9.1: The `.dvc` File](#-step-91-the-dvc-file) - * [### Step 9.2: The DVC Cache (`.dvc/cache`)](#-step-92-the-dvc-cache-dvc/cache) - * [### Step 9.3: The `.dvc` Directory Structure](#-step-93-the-dvc-directory-structure) +* [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) @@ -474,10 +470,14 @@ dvc checkout ``` M data/data.xml -A datadir +D datadir ``` -Wait, `datadir` suddenly appeared! That's because when you switched branches, `dvc checkout` detected that the `tutorial` branch's `data/data.xml.dvc` file points to a different version than what's currently in your workspace (it brought back the `data.xml` content) and also noticed `datadir` is not tracked there so it deleted it from your workspace to match the `tutorial` branch's Git state. This is an artifact of mixing different experiments on different branches. +The `datadir` disappeared! That's because when you switched to the `tutorial` branch, `dvc checkout` detected that: +1. The `tutorial` branch's `data/data.xml.dvc` file points to a different version than what's currently in your workspace (it brought back the `data.xml` content - hence `M` for modified) +2. The `tutorial` branch doesn't have a `datadir.dvc` file, so DVC removed `datadir` from your workspace to match the `tutorial` branch's Git state (hence `D` for deleted) + +This demonstrates how DVC keeps your workspace synchronized with the data versions defined in your current Git commit. Let's specifically try to restore the `cats-dogs-v1` version. @@ -747,134 +747,7 @@ You will find `external_data.xml` and `external_data.xml.dvc` in your project. I --- -## ๐Ÿค– 8. Automating Model Versioning with DVC Pipelines - -While `dvc add` is great for raw data and final models, DVC also offers pipelines (`dvc stage add` and `dvc repro`) to version the *entire workflow* that produces a model, tying inputs, code, and outputs together. This is crucial for ML experiment reproducibility. - -### Step 8.1: Understand the `train.py` Script - -The `example-versioning` repository you cloned contains a `train.py` script. This script: - -1. Loads data from the `datadir` (the cats & dogs dataset). -2. Performs a pre-processing step (feature extraction). -3. Trains a simple image classification model. -4. Saves the trained model as `model.weights.h5`. -5. Saves training metrics to `metrics.csv`. - -For this tutorial, consider `train.py` as a "black box" โ€“ the specific ML algorithm or Keras knowledge is not required. Our focus is on how DVC helps manage its inputs and outputs. - -Let's ensure we're on a clean Git state, and remove any old `model.weights.h5.dvc` files if they exist from previous runs. - -```bash -git checkout cats-dogs-v2 # Ensure we're on a branch with datadir -dvc checkout # Ensure datadir is present -dvc remove model.weights.h5.dvc --force 2>/dev/null || true # Ignore error if file doesn't exist -git rm --cached model.weights.h5.dvc 2>/dev/null || true # Remove from Git index if needed -``` - -This prepares our workspace for defining the pipeline. - -### Step 8.2: Define an ML Pipeline Stage (`dvc stage add`) - -Instead of manually adding `model.weights.h5` and `metrics.csv` after `train.py` runs, we can define a DVC pipeline stage that encapsulates the entire process. - -```bash -dvc stage add -n train -d train.py -d datadir \ - -o model.weights.h5 -M metrics.csv \ - python train.py -``` - -**Output (example):** - -``` -'train' was added to your pipeline. To reproduce it, run 'dvc repro'. -``` - -What this command does: - -* **`dvc stage add -n train`**: Creates a new pipeline stage named `train`. -* **`-d train.py -d datadir`**: Declares `train.py` (the code) and `datadir` (the input data) as **dependencies** of this stage. If either changes, DVC knows the stage needs to be re-run. -* **`-o model.weights.h5`**: Declares `model.weights.h5` as an **output** of this stage. DVC will version this file automatically. -* **`-M metrics.csv`**: Declares `metrics.csv` as a **metric file** output. This allows DVC to track and compare metrics across experiments. -* **`python train.py`**: Specifies the **command** that DVC should run to execute this stage. - -This command also generates a `dvc.yaml` file (or updates it if it exists), which defines your ML pipeline. - -```bash -cat dvc.yaml -``` - -**Output (example):** - -```yaml -stages: - train: - cmd: python train.py - deps: - - train.py - - datadir - outs: - - model.weights.h5 - metrics: - - metrics.csv -``` - -This `dvc.yaml` file is plain text and versioned by Git, just like your code. It's the blueprint of your ML workflow. - -Commit the `dvc.yaml` file to Git: - -```bash -git add dvc.yaml -git commit -m "Define train pipeline stage" -``` - -**Output:** - -``` -[cats-dogs-v2 1f2g3h4] Define train pipeline stage - 1 file changed, 8 insertions(+) - create mode 100644 dvc.yaml -``` - -### Step 8.3: Reproduce the Pipeline (`dvc repro`) - -Now that the pipeline stage is defined, you can run it using `dvc repro`. - -```bash -dvc repro -``` - -**Output (example):** - -``` -Running stage 'train': - python train.py -... (output from train.py script, including Keras/TensorFlow logs) ... -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] -Reproduced stage 'train'. -``` - -`dvc repro` executes the `train` stage. It checks the dependencies (`train.py`, `datadir`). If any dependency has changed since the last run, it re-runs the command (`python train.py`) and captures the outputs (`model.weights.h5`, `metrics.csv`) into the DVC cache, updating their `.dvc` file pointers implicitly. - -If you run `dvc repro` again without changing anything, it will report: - -```bash -dvc repro -``` - -**Output:** - -``` -Data and pipelines are up to date. -``` - -This shows its ability to track and only re-run what's necessary, saving computation. - -๐Ÿ‘‰ **Real-World MLOps Relevance:** Using `dvc stage add` and `dvc repro` for pipelines is a cornerstone of reproducible ML. It means that anyone (your future self, a teammate, a CI/CD system) can execute `dvc repro` and get the *exact same* data and model outputs, even if they only have the Git repository. This is critical for reliable deployments and debugging. - ---- - -## ๐Ÿ” 9. Understanding DVC File and Cache Internals +## 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. @@ -967,103 +840,11 @@ Congratulations! You've successfully completed this hands-on tutorial on Data an ### Where to go from here? -* **Experiment Management (MLflow):** Explore how DVC integrates seamlessly with MLflow for comprehensive experiment tracking, logging metrics, and managing models in a registry. +* **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) - ---- - -## DVC Commands API Analysis and Suggestions for Updates - -The provided tutorial uses a very solid set of DVC commands that are still highly relevant and foundational. DVC's CLI has remained remarkably stable in its core functionality, but there have been refinements, new features, and changes in recommended best practices, especially around pipeline management. - -Here's an analysis of the commands used in the context of current DVC versions (e.g., 3.x and above, as of late 2023/early 2024): - -### Commands Analyzed - -* `dvc init` -* `dvc status` -* `dvc add` -* `dvc get` -* `dvc remote add` -* `dvc push` -* `dvc pull` -* `dvc list` -* `dvc import` -* `dvc import-url` -* `dvc remove` -* `dvc stage add` (formerly `dvc run`) -* `dvc repro` - -### Analysis and Suggestions - -1. **`dvc init`**: - * **Current State:** Remains the same. It's the essential first step. - * **Improvements/Tips:** None for the command itself. Emphasize committing `.dvc/` to Git immediately. - -2. **`dvc status`**: - * **Current State:** Works as described, showing discrepancies between workspace, cache, and `.dvc` files. - * **Improvements/Tips:** Can be used with `--jobs N` for larger projects, though not typically necessary in a tutorial. No major API changes. - -3. **`dvc add `**: - * **Current State:** Core command for data versioning. Behavior for files vs. directories is consistent. - * **Improvements/Tips:** - * The tutorial correctly introduces `dvc add` for `data.xml` and `datadir`. It's important to clarify that this is for *source data* or *external models* that you don't generate with a script *within* DVC. - * For outputs of scripts (like `model.weights.h5` or `metrics.csv`), `dvc stage add` (or the older `dvc run`) is indeed the **preferred and recommended method** as it provides lineage. The tutorial's "Automating Capturing" section correctly pivots to this. - -4. **`dvc get -o `**: - * **Current State:** Remains the command for downloading data from another DVC repository. - * **Improvements/Tips:** None for the command itself. The `--rev` flag used in the tutorial is excellent for fetching specific versions, which is highly valuable. - -5. **`dvc remote add [-d] `**: - * **Current State:** Essential for configuring remotes. - * **Improvements/Tips:** - * The tutorial uses a local path `/tmp/dvc_remote_storage`. For a real-world scenario, explicitly mention cloud providers (S3, GCS, Azure Blob, Rucio, WebDAV etc.) as the common use cases for `url`. Example: `dvc remote add my_s3_remote s3://my-bucket/dvc-store`. - * Briefly mention credential management for cloud remotes (e.g., DVC uses boto3 for AWS, google-cloud-storage for GCS, etc., relying on environment variables or config files). - -6. **`dvc push [-v]`**: - * **Current State:** Uploads cached data to the remote. - * **Improvements/Tips:** No major changes. It's robust. - -7. **`dvc pull [-v]`**: - * **Current State:** Downloads missing data to the local cache and links it to the workspace. - * **Improvements/Tips:** No major changes. It's robust. - -8. **`dvc list [path]`**: - * **Current State:** Useful for exploring DVC repositories remotely. - * **Improvements/Tips:** None for the command itself. - -9. **`dvc import -o `**: - * **Current State:** Imports data from another DVC repository and adds it to your project. - * **Improvements/Tips:** None. It's a convenient shorthand for `dvc get` + `dvc add` for external DVC-tracked data. - -10. **`dvc import-url -o `**: - * **Current State:** Imports data from *any* URL (not necessarily a DVC repo) and tracks it. - * **Improvements/Tips:** None. This is highly useful for integrating publicly available datasets or large external files directly into your DVC project. - -11. **`dvc remove `**: - * **Current State:** Removes a `.dvc` file and its corresponding data from the workspace, optionally from the cache. - * **Improvements/Tips:** The `--force` flag is often needed to delete the actual `.dvc` file from the workspace if it's already in the Git index. It's properly demonstrated for cleanup. - -12. **`dvc stage add [-n ] -d -o [-M ] `**: - * **Current State:** This command is the **modern and highly recommended way** to define pipeline stages. It replaces the older `dvc run` command. The tutorial correctly uses `dvc stage add`. - * **Improvements/Tips:** - * The tutorial uses it perfectly to define dependencies and outputs. - * Emphasize that this command creates/modifies `dvc.yaml`, the standard for DVC pipelines. - * For metrics, `dvc metrics show` and `dvc metrics diff` can be powerful follow-up commands to demonstrate after defining a metric output. This is hinted at in the original "What's next?" section and could be briefly demonstrated. - * Could mention `--live` flag for real-time metric tracking during training in more advanced scenarios (but out of scope for basic versioning). - -13. **`dvc repro [stage_name]`**: - * **Current State:** The core command to reproduce pipeline stages. - * **Improvements/Tips:** No major changes. Crucial for reproducibility. - -### Overall API Consistency and Evolution - -DVC's command-line interface is generally very consistent and intuitive, often mirroring Git commands where applicable (`add`, `checkout`, `push`, `pull`). The evolution from `dvc run` to `dvc stage add` was a positive step, making the terminology clearer and better aligned with pipeline concepts (`dvc.yaml`). - -The tutorial covers the essential commands comprehensively and explains their functionality well. No critical API syntax changes would render the tutorial commands obsolete. The proposed tutorial flow and explanations are highly effective for teaching DVC fundamentals. +[โฌ†๏ธ Back to Table of Contents](#-table-of-contents)รฅ From 63024b7bf2fac900b7d9c5a479809cc6e87db03d Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:34:21 +0700 Subject: [PATCH 11/13] Update --- .gitignore | 1 + tutorial.md | 1 + 2 files changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 45a9df9..f429457 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ ## Mac OS .DS_Store +.venv/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/tutorial.md b/tutorial.md index dc3f2ba..f318d43 100644 --- a/tutorial.md +++ b/tutorial.md @@ -474,6 +474,7 @@ D datadir ``` The `datadir` disappeared! That's because when you switched to the `tutorial` branch, `dvc checkout` detected that: + 1. The `tutorial` branch's `data/data.xml.dvc` file points to a different version than what's currently in your workspace (it brought back the `data.xml` content - hence `M` for modified) 2. The `tutorial` branch doesn't have a `datadir.dvc` file, so DVC removed `datadir` from your workspace to match the `tutorial` branch's Git state (hence `D` for deleted) From de547bfe03722904f8490b8e48e4c5f00ce2b1b7 Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:46:41 +0700 Subject: [PATCH 12/13] Update tutorial --- Makefile | 173 ------------------------ tutorial.md | 380 +++++++++++++++++++++++++++++++--------------------- 2 files changed, 226 insertions(+), 327 deletions(-) diff --git a/Makefile b/Makefile index c51dcac..6b32554 100644 --- a/Makefile +++ b/Makefile @@ -25,10 +25,6 @@ check-all: lint format-check type-check test pre-commit ## Run all checks (lint @echo "All checks complete." -# ============================================================================= -# DVC TUTORIAL TARGETS -# ============================================================================= - 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 @@ -69,172 +65,3 @@ tutorial-clean: ## Clean up all DVC-related files and data (reset to initial sta fi; \ done @echo "โœ… DVC tutorial cleanup complete! Repository reset to initial state." - -dvc-init: ## Initialize DVC in the project (Step 1.4) - @echo "๐ŸŒฟ Step 1.4: Init project in 'tutorial' branch" - @if git show-ref --verify --quiet refs/heads/tutorial; then \ - echo "Branch 'tutorial' exists, switching to it"; \ - git checkout tutorial; \ - else \ - echo "Creating new branch 'tutorial'"; \ - git checkout -b tutorial; \ - fi - @echo "๐Ÿš€ Step 1.4: Initialize DVC in Your Project" - dvc init - @echo "๐Ÿ“ DVC initialized! Let's see what was created:" - ls -la .dvc/ - @echo "๐Ÿ“ Adding DVC files to Git..." - git add .dvc/ - git commit -m "Initialize DVC" - @echo "โœ… DVC initialization complete!" - -dvc-track-file: ## Version your first dataset file (Section 2) - @echo "โญ Section 2: Versioning Your First Dataset (File)" - @echo "๐Ÿ“ฅ Step 2.1: Download Raw Data" - mkdir -p data - dvc get https://github.com/iterative/dataset-registry get-started/data.xml -o data/data.xml - @echo "๐Ÿ“ Step 2.2: Add the File to DVC" - dvc add data/data.xml -v - @echo "๐Ÿ‘€ Let's inspect the .dvc file:" - cat data/data.xml.dvc - @echo "๐Ÿ“ Step 2.3: Commit DVC-file to Git" - git add data/.gitignore data/data.xml.dvc - git commit -m "Add raw data.xml" - @echo "โœ… File versioning complete!" - -dvc-track-directory: ## Version a directory dataset (Section 3) - @echo "๐Ÿ“ Section 3: Versioning a Directory (Cats & Dogs Dataset)" - @echo "๐ŸŒฟ Step 3.1: Create a New Git Branch" - @if git show-ref --verify --quiet refs/heads/cats-dogs-v1; then \ - echo "Branch 'cats-dogs-v1' exists, switching to it"; \ - git checkout cats-dogs-v1; \ - else \ - echo "Creating new branch 'cats-dogs-v1'"; \ - git checkout -b cats-dogs-v1; \ - fi - @echo "๐Ÿ“ฅ Step 3.2: Download the Cats & Dogs Dataset" - dvc get --rev cats-dogs-v1 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir - @echo "๐Ÿ“ Step 3.3: Add the Directory to DVC" - dvc add datadir - @echo "๐Ÿ‘€ Let's inspect the directory .dvc file:" - cat datadir.dvc - @echo "๐Ÿ“ Step 3.4: Commit the DVC-file and Tag the Version" - git add .gitignore datadir.dvc - git commit -m "Add cats-dogs dataset (v1)" - git tag -a cats-dogs-v1 -m "Dataset version v1.0" - @echo "โœ… Directory versioning complete!" - -dvc-track-changes: ## Track changes and update data (Section 4) - @echo "โ™ป๏ธ Section 4: Tracking Changes & Updating Data" - @echo "๐Ÿ” Step 4.1: Check Current DVC Status" - dvc status - @echo "๐ŸŒฟ Step 4.2: Introduce a New Data Version" - @if git show-ref --verify --quiet refs/heads/cats-dogs-v2; then \ - echo "Branch 'cats-dogs-v2' exists, switching to it"; \ - git checkout cats-dogs-v2; \ - else \ - echo "Creating new branch 'cats-dogs-v2'"; \ - git checkout -b cats-dogs-v2; \ - fi - dvc get --rev cats-dogs-v2 https://github.com/iterative/dataset-registry use-cases/cats-dogs -o datadir - @echo "๐Ÿ” Check status after data change:" - dvc status - @echo "๐Ÿ“ Step 4.3: Capture the New Data Version with DVC" - dvc add datadir - git add datadir.dvc - git commit -m "Updated cats-dogs dataset (v2)" - git tag -a cats-dogs-v2 -m "Dataset version v2.0" - @echo "โœ… Change tracking complete!" - -dvc-switch-versions: ## Switch between data versions (Section 5) - @echo "โช Section 5: Switching Between Data Versions" - @echo "๐Ÿ”„ Step 5.1: Switch to Different Branch" - @if git show-ref --verify --quiet refs/heads/tutorial; then \ - git checkout tutorial; \ - elif 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 \ - echo "No suitable branch found, staying on current branch"; \ - fi - @echo "๐Ÿ“ Current files:" - ls - @echo "๐Ÿ”„ DVC checkout to sync data:" - dvc checkout - @echo "๐Ÿ”„ Step 5.2: Restore Specific Data Version" - git checkout cats-dogs-v1 - @echo "๐Ÿ“ Files after git checkout:" - ls - @echo "๐Ÿ”„ DVC checkout to get v1 data:" - dvc checkout - @echo "โœ… Version switching complete!" - -dvc-add-remotes: ## Set up and use remotes (Section 6) - @echo "โ˜๏ธ Section 6: Storing and Sharing Data with Remotes" - @echo "๐Ÿ“ Step 6.1: Set Up a Local Remote Storage" - mkdir -p /tmp/dvc_remote_storage - dvc remote add -d local_storage /tmp/dvc_remote_storage - @echo "๐Ÿ‘€ Let's check the config:" - cat .dvc/config - git add .dvc/config - git commit -m "Add local DVC remote storage" - @echo "๐Ÿ“ค Step 6.2: Push Data to Remote Storage" - dvc push -v - @echo "๐Ÿ‘€ Check remote storage:" - ls /tmp/dvc_remote_storage - @echo "๐Ÿงน Step 6.3: Simulate clean environment and pull data" - @echo "Removing local cache and data..." - rm -rf .dvc/cache datadir data/data.xml - @echo "๐Ÿ“ฅ Pull data from remote:" - dvc pull -v - @echo "๐Ÿ“ Verify data is restored:" - ls datadir data - @echo "โœ… Remote storage setup complete!" - -dvc-advanced: ## Advanced data access commands (Section 7) - @echo "๐Ÿ“ฅ Section 7: Advanced Data Access Commands" - @echo "๐Ÿ” Step 7.1: Explore Remote DVC Repositories" - dvc list https://github.com/iterative/dataset-registry use-cases - @echo "๐Ÿ“ฅ Step 7.2: Download Data Without Tracking (dvc get)" - dvc get https://github.com/iterative/dataset-registry use-cases/cats-dogs -o cats-dogs-untracked - @echo "๐Ÿ“ Check if it's tracked:" - ls cats-dogs-untracked - dvc status - @echo "๐Ÿ“ฅ Step 7.3: Download and Track Data (dvc import)" - -dvc import git@github.com:iterative/example-get-started data/data.xml -o imported_data.xml || \ - dvc import https://github.com/iterative/example-get-started data/data.xml -o imported_data.xml - @echo "๐Ÿ“ฅ Step 7.4: Track External URLs (dvc import-url)" - dvc import-url https://data.dvc.org/get-started/data.xml -o external_data.xml - @echo "๐Ÿ“ Check what was created:" - ls *.xml* | head -10 - @echo "โœ… Advanced data access complete!" - - -dvc-full: dvc-init dvc-track-file dvc-track-directory dvc-track-changes dvc-switch-versions dvc-add-remotes dvc-advanced - @echo "๐ŸŽ‰ Complete DVC Tutorial finished!" - @echo "๐Ÿ“‹ Summary of what was accomplished:" - @echo " โœ… Initialized DVC" - @echo " โœ… Versioned files and directories" - @echo " โœ… Tracked data changes" - @echo " โœ… Switched between versions" - @echo " โœ… Set up remote storage" - @echo " โœ… Used advanced data access" - - -tutorial-setup: ## Show tutorial setup and available commands - @echo "๐Ÿš€ DVC Tutorial Setup" - @echo "" - @echo "Available tutorial commands:" - @echo " make tutorial-clean - Clean up all DVC files and data" - @echo " make tutorial-init - Initialize DVC (Step 1.4)" - @echo " make tutorial-file - Version first dataset file (Section 2)" - @echo " make tutorial-directory - Version directory dataset (Section 3)" - @echo " make tutorial-changes - Track changes and updates (Section 4)" - @echo " make tutorial-versions - Switch between versions (Section 5)" - @echo " make tutorial-remotes - Set up remote storage (Section 6)" - @echo " make tutorial-advanced - Advanced data access (Section 7)" - @echo " make tutorial-full - Run complete tutorial" - @echo "" - @echo "๐Ÿ’ก Tip: Run 'make tutorial-full' to execute the entire tutorial automatically!" - @echo "๐Ÿ“– For detailed explanations, follow along with tutorial.md" diff --git a/tutorial.md b/tutorial.md index f318d43..ccf382b 100644 --- a/tutorial.md +++ b/tutorial.md @@ -1,82 +1,154 @@ -## ๐Ÿš€ Tutorial: Data and Model Versioning with DVC - ---- +# 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. -* `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. -* `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. +`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 1.1: Prerequisites](#-step-11-prerequisites) - * [### 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 (Without `datadir`)](#-step-51-switch-to-a-different-branch-without-datadir) - * [### 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](#-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 -Setup Instructions: +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 -For detailed, step-by-step instructions on how to: +We'll start by cloning a ready-made **Git** repository that contains a `train.py` script and `requirements.txt`. -* Clone the project repository, -* Create a Python virtual environment using uv, and -* Install all required dependencies, +```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. -please refer to the ๐Ÿ‘ฉโ€๐Ÿ’ป Quick Start: Installation & Setup section in the project's README.md file. +```bash +git checkout -b tutorial +``` -Once you have successfully completed the setup steps outlined in the README.md, your environment will be ready, and you can proceed with this 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. +The first step in any **DVC** project is to initialize it within your **Git** repository. ```bash dvc init @@ -109,9 +181,9 @@ 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. +**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. +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 @@ -145,7 +217,7 @@ git commit -m "Initialize DVC" ## 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. +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 @@ -161,11 +233,11 @@ dvc get https://github.com/iterative/dataset-registry get-started/data.xml -o da 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. +`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. +Now, let's tell **DVC** to track this `data.xml` file. ```bash dvc add data/data.xml -v @@ -181,9 +253,9 @@ 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. +- **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: @@ -200,11 +272,11 @@ outs: 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. +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. +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 @@ -220,17 +292,17 @@ git commit -m "Add raw data.xml" 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. +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. +**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. +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 @@ -244,7 +316,7 @@ 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. +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 @@ -272,7 +344,7 @@ 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. +Now, add the entire `datadir` to **DVC**. ```bash dvc add datadir @@ -286,7 +358,7 @@ 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. +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: @@ -302,11 +374,11 @@ outs: path: datadir ``` -Notice the `.dir` suffix on the MD5 hash, indicating it refers to a directory hash. +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. +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 @@ -323,17 +395,17 @@ git tag -a cats-dogs-v1 -m "Dataset version v1.0" create mode 100644 datadir.dvc ``` -You now have a Git tag `cats-dogs-v1` that points to a specific version of your `datadir` dataset! +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. +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. +Use `dvc status` to see the state of your **DVC**-tracked files. ```bash dvc status @@ -345,11 +417,11 @@ dvc status 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). +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. +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 @@ -387,7 +459,7 @@ dvc status ? 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. +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 @@ -406,9 +478,9 @@ 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. +**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`. +Now, commit the updated `datadir.dvc` file to **Git** and tag it as `cats-dogs-v2`. ```bash git add datadir.dvc @@ -423,17 +495,17 @@ git tag -a cats-dogs-v2 -m "Dataset version v2.0" 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. +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. +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 (Without `datadir`) +### 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. +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 @@ -458,9 +530,9 @@ ls .dvc .env data main.py requirements.txt train.py ``` -Notice that `datadir` is gone from your workspace! This is because the `tutorial` branch doesn't have the `datadir.dvc` file in its Git history. +Notice that `datadir` is gone from your workspace! This is because the `tutorial` branch's **Git** history doesn't track `datadir.dvc`. -To restore the DVC-tracked data that *should* be present for the current Git commit, you need to run `dvc checkout`. +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 @@ -473,18 +545,18 @@ M data/data.xml D datadir ``` -The `datadir` disappeared! That's because when you switched to the `tutorial` branch, `dvc checkout` detected that: +What happened? -1. The `tutorial` branch's `data/data.xml.dvc` file points to a different version than what's currently in your workspace (it brought back the `data.xml` content - hence `M` for modified) -2. The `tutorial` branch doesn't have a `datadir.dvc` file, so DVC removed `datadir` from your workspace to match the `tutorial` branch's Git state (hence `D` for deleted) +- `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 keeps your workspace synchronized with the data versions defined in your current Git commit. +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. +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 @@ -510,7 +582,7 @@ ls .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! +`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`: @@ -530,17 +602,17 @@ M datadir 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. +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. +**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. +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 @@ -553,9 +625,9 @@ dvc remote add -d local_storage /tmp/dvc_remote_storage Setting 'local_storage' as a default remote. ``` -* **`mkdir -p /tmp/dvc_remote_storage`**: Creates a temporary directory. +- **`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`). +- **`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: @@ -572,7 +644,7 @@ cat .dvc/config url = /tmp/dvc_remote_storage ``` -Now, commit this configuration change to Git so your team knows where the data is supposed to be stored. +Now, commit this configuration change to **Git** so your team knows where the data is supposed to be stored. ```bash git add .dvc/config @@ -588,7 +660,7 @@ git commit -m "Add local DVC remote storage" ### 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`. +To upload the **DVC**-tracked data from your local **cache** to the configured **remote**, use `dvc push`. ```bash dvc push -v @@ -605,9 +677,9 @@ Pushing 'datadir' to 'local_storage' 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. +**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): +You can verify the data was pushed by checking the **remote** directory (it mirrors **DVC**'s **cache** structure): ```bash ls /tmp/dvc_remote_storage @@ -619,11 +691,11 @@ ls /tmp/dvc_remote_storage a3 b6 ``` -These are directories named after the first two characters of the data's MD5 hashes. +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. +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:** @@ -650,7 +722,7 @@ Now, let's simulate a scenario where a new team member joins or you're working o 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. + `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: @@ -659,17 +731,17 @@ Now, let's simulate a scenario where a new team member joins or you're working o 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. + 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. +**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. +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 @@ -682,13 +754,13 @@ cats-dogs/ get-started/ ``` -This shows the directories available under the `use-cases` path in that DVC dataset registry. +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. +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: +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 @@ -708,7 +780,7 @@ You'll see `cats-dogs-untracked` directory. If you check `dvc status` or look fo ### 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. +`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 @@ -728,11 +800,11 @@ 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. +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, HDFS, 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. +`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 @@ -744,29 +816,29 @@ dvc import-url https://data.dvc.org/get-started/data.xml -o external_data.xml 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. +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. +Let's take a closer look at the files and directories **DVC** creates and manages, which underpin its versioning capabilities. -### Step 9.1: The `.dvc` File +### 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. +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. +- `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 9.2: The DVC Cache (`.dvc/cache`) +### 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. +The `.dvc/cache` directory is where **DVC** stores the actual content of your data and model files. ```bash ls -la .dvc/cache @@ -781,15 +853,15 @@ 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. +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`. +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. +**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 9.3: The `.dvc` Directory Structure +### Step 8.3: The `.dvc` Directory Structure -The `.dvc/` directory is DVC's internal workspace for your project. +The `.dvc/` directory is **DVC**'s internal workspace for your project. ```bash ls -la .dvc @@ -808,44 +880,44 @@ drwxr-xr-x 2 user user 4096 Apr 1 10:00 tmp # 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. +- `.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. +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) +- **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. +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. +- 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. +- **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! +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)รฅ +[โฌ†๏ธ Back to Table of Contents](#-table-of-contents) From 82512700307546158d5c191eb214036caee7fd36 Mon Sep 17 00:00:00 2001 From: Mikhail Date: Mon, 9 Jun 2025 23:48:01 +0700 Subject: [PATCH 13/13] Update REAMDE --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1a52d76..3fe974d 100644 --- a/README.md +++ b/README.md @@ -92,4 +92,4 @@ For a deeper dive into the development workflow, tool configurations, and contri --- -**Happy MLOps journey! We hope this starter project helps you learn and experiment. ๐Ÿš€** +**Happy DVC journey! ๐Ÿš€**