mirror of
https://github.com/NVIDIA-NeMo/DataDesigner
synced 2026-05-24 09:48:29 +00:00
8f7a72094a
* updates to support image->image * update notebooks * regen colab notebooks * simplify tests
141 lines
5.3 KiB
Markdown
141 lines
5.3 KiB
Markdown
# Overview
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Welcome to the Data Designer tutorial series! These hands-on notebooks will guide you through the core concepts and features of Data Designer, from basic synthetic data generation to advanced techniques like structured outputs and dataset seeding.
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## 🚀 Setting Up Your Environment
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### Local Setup Best Practices
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First, download the tutorial [from the release assets](https://github.com/NVIDIA-NeMo/DataDesigner/releases/latest/download/data_designer_tutorial.zip).
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To run the tutorial notebooks locally, we recommend using a virtual environment to manage dependencies:
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=== "uv (Recommended)"
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```bash
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# Extract tutorial notebooks
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unzip data_designer_tutorial.zip
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cd data_designer_tutorial
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# Launch Jupyter
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uv run jupyter notebook
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```
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=== "pip + venv"
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```bash
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# Extract tutorial notebooks
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unzip data_designer_tutorial.zip
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cd data_designer_tutorial
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# Create Python virtual environment and install required packages
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python -m venv venv
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source venv/bin/activate
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pip install data-designer jupyter
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# Launch Jupyter
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jupyter notebook
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```
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### API Keys and Authentication
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Data Designer is able to interface with various LLM providers. You'll need to set up API keys for the models you want to use:
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```bash
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# For NVIDIA API Catalog (build.nvidia.com)
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export NVIDIA_API_KEY="your-api-key-here"
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# For OpenAI
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export OPENAI_API_KEY="your-api-key-here"
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# For OpenRouter
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export OPENROUTER_API_KEY="your-api-key-here"
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```
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For more information, check the [Welcome](../index.md), [Default Model Settings](../concepts/models/default-model-settings.md) and how to [Configure Model Settings Using The CLI](../concepts/models/configure-model-settings-with-the-cli.md).
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## 📚 Tutorial Series
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The tutorials are designed to be completed in sequence, building upon concepts introduced in previous notebooks:
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### [1. The Basics](1-the-basics.ipynb)
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Learn the fundamentals of Data Designer by generating a simple product review dataset. This notebook covers:
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- Setting up the `DataDesigner` interface
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- Configuring models and inference parameters
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- Using built-in samplers (Category, Person, Uniform)
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- Generating LLM text columns with dependencies
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- Understanding the generation workflow
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**Start here if you're new to Data Designer!**
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### [2. Structured Outputs and Jinja Expressions](2-structured-outputs-and-jinja-expressions.ipynb)
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Explore more advanced data generation capabilities:
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- Creating structured JSON outputs with schemas
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- Using Jinja expressions for derived columns
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- Combining samplers with structured data
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- Building complex data dependencies
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- Working with nested data structures
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### [3. Seeding with an External Dataset](3-seeding-with-a-dataset.ipynb)
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Learn how to leverage existing datasets to guide synthetic data generation:
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- Loading and using seed datasets
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- Sampling from real data distributions
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- Combining seed data with LLM generation
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- Creating realistic synthetic data based on existing patterns
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### [4. Providing Images as Context](4-providing-images-as-context.ipynb)
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Learn how to use vision-language models to generate text descriptions from images:
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- Processing and converting images to base64 format for model consumption
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- Using vision-language models (VLMs) to analyze visual documents
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- Generating detailed summaries from document images
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- Inspecting and validating vision-based generation results
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### [5. Generating Images](5-generating-images.ipynb)
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Generate synthetic image data with Data Designer:
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- Configuring image-generation models with `ImageInferenceParams`
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- Adding image columns with Jinja2 prompts and sampler-driven diversity
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- Preview (base64 in dataframe) vs create (images saved to disk, paths in dataframe)
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- Displaying generated images in the notebook
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### [6. Image-to-Image Editing](6-editing-images-with-image-context.ipynb)
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Chain image generation columns to generate and then edit images:
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- Generating images from text and then editing them in a follow-up column
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- Using `ImageContext` with auto-detection to pass generated images to an editing model
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- Combining sampled accessories and settings for varied edits
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- Comparing generated vs edited images in preview and create modes
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## 📖 Important Documentation Sections
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Before diving into the tutorials, familiarize yourself with these key documentation sections:
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### Getting Started
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- **[Welcome & Installation](../index.md)** - Overview of Data Designer capabilities and installation instructions
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### Core Concepts
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Understanding these concepts will help you make the most of the tutorials:
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- **[Columns](../concepts/columns.md)** - Learn about different column types (Sampler, LLM, Expression, Validation, etc.)
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- **[Validators](../concepts/validators.md)** - Understand how to validate generated data with Python, SQL, and remote validators
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- **[Person Sampling](../concepts/person_sampling.md)** - Learn how to sample realistic person data with demographic attributes
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### Code Reference
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Quick reference guides for the main configuration objects:
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- **[column_configs](../code_reference/column_configs.md)** - All column configuration types
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- **[config_builder](../code_reference/config_builder.md)** - The `DataDesignerConfigBuilder` API
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- **[data_designer_config](../code_reference/data_designer_config.md)** - Main configuration schema
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- **[validator_params](../code_reference/validator_params.md)** - Validator configuration options
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