trying something out

Author: andreatgretel

.github/workflows/pack-tutorials.yml

@@ -0,0 +1,36 @@
+name: Pack Tutorials
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+
+jobs:
+  pack-tutorials:
+    runs-on: ubuntu-latest
+
+    steps:
+      # Step 1: Checkout the repository
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # Step 2: Zip the subfolder
+      - name: Create ZIP of subfolder
+        run: |
+          SUBFOLDER="docs/tutorials"
+          ZIP_NAME="${SUBFOLDER}.zip"
+          
+          if [ ! -d "$SUBFOLDER" ]; then
+            echo "Error: Subfolder '$SUBFOLDER' does not exist."
+            exit 1
+          fi
+          
+          zip -r "$ZIP_NAME" "$SUBFOLDER"
+
+      # Step 3: Upload the ZIP as an artifact
+      - name: Upload ZIP artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: tutorials
+          path: tutorials.zip

docs/notebooks/intro.md

@@ -2,41 +2,6 @@
 
 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.
 
-## 📚 Tutorial Series
-
-The tutorials are designed to be completed in sequence, building upon concepts introduced in previous notebooks:
-
-### [1. The Basics](1-the-basics.ipynb)
-
-Learn the fundamentals of Data Designer by generating a simple product review dataset. This notebook covers:
-
-- Setting up the `DataDesigner` interface
-- Configuring models and inference parameters
-- Using built-in samplers (Category, Person, Uniform)
-- Generating LLM text columns with dependencies
-- Understanding the generation workflow
-
-**Start here if you're new to Data Designer!**
-
-### [2. Structured Outputs and Jinja Expressions](2-structured-outputs-and-jinja-expressions.ipynb)
-
-Explore more advanced data generation capabilities:
-
-- Creating structured JSON outputs with schemas
-- Using Jinja expressions for derived columns
-- Combining samplers with structured data
-- Building complex data dependencies
-- Working with nested data structures
-
-### [3. Seeding with an External Dataset](3-seeding-with-a-dataset.ipynb)
-
-Learn how to leverage existing datasets to guide synthetic data generation:
-
-- Loading and using seed datasets
-- Sampling from real data distributions
-- Combining seed data with LLM generation
-- Creating realistic synthetic data based on existing patterns
-
 ## 🚀 Setting Up Your Environment
 
 ### Local Setup Best Practices
@@ -92,14 +57,40 @@ export OPENAI_API_KEY="your-api-key-here"
 
 For more information, check the [Quick Start](../quick-start.md), [Default Model Settings](../models/default-model-settings.md) and how to [Configure Model Settings Using The CLI](../models/configure-model-settings-with-the-cli.md).
 
-### Recommended Setup
+## 📚 Tutorial Series
+
+The tutorials are designed to be completed in sequence, building upon concepts introduced in previous notebooks:
 
-For the best experience with Data Designer notebooks:
+### [1. The Basics](1-the-basics.ipynb)
 
-1. **Python Version:** Use Python 3.11 or later
-2. **Virtual Environment:** Always use a virtual environment to avoid dependency conflicts
-3. **Jupyter Extensions:** Consider installing JupyterLab for a more modern interface
-4. **API Access:** Ensure you have valid API keys for your chosen LLM provider
+Learn the fundamentals of Data Designer by generating a simple product review dataset. This notebook covers:
+
+- Setting up the `DataDesigner` interface
+- Configuring models and inference parameters
+- Using built-in samplers (Category, Person, Uniform)
+- Generating LLM text columns with dependencies
+- Understanding the generation workflow
+
+**Start here if you're new to Data Designer!**
+
+### [2. Structured Outputs and Jinja Expressions](2-structured-outputs-and-jinja-expressions.ipynb)
+
+Explore more advanced data generation capabilities:
+
+- Creating structured JSON outputs with schemas
+- Using Jinja expressions for derived columns
+- Combining samplers with structured data
+- Building complex data dependencies
+- Working with nested data structures
+
+### [3. Seeding with an External Dataset](3-seeding-with-a-dataset.ipynb)
+
+Learn how to leverage existing datasets to guide synthetic data generation:
+
+- Loading and using seed datasets
+- Sampling from real data distributions
+- Combining seed data with LLM generation
+- Creating realistic synthetic data based on existing patterns
 
 ## 📖 Important Documentation Sections
 
@@ -125,24 +116,4 @@ Quick reference guides for the main configuration objects:
 - **[column_configs](../code_reference/column_configs.md)** - All column configuration types
 - **[config_builder](../code_reference/config_builder.md)** - The `DataDesignerConfigBuilder` API
 - **[data_designer_config](../code_reference/data_designer_config.md)** - Main configuration schema
-- **[validator_params](../code_reference/validator_params.md)** - Validator configuration options
-
-## 💡 Tips for Success
-
-- **Start Simple:** Begin with Tutorial 1 and work through the examples step by step
-- **Experiment:** Modify the examples to generate your own datasets
-- **Check Logs:** Data Designer provides detailed logging to help debug generation issues
-- **Read Error Messages:** Error messages are designed to be helpful and actionable
-- **Use the Registry:** Explore built-in samplers and generators using the registry system
-
-## 🆘 Getting Help
-
-If you run into issues:
-
-1. Check the [GitHub Issues](https://github.com/NVIDIA-NeMo/DataDesigner/issues) for known problems
-2. Review the [Contributing Guide](../CONTRIBUTING.md) if you'd like to report a bug or contribute
-3. Consult the [Code Reference](../code_reference/column_configs.md) for detailed API documentation
-
----
-
-Ready to get started? Head to [The Basics](1-the-basics.ipynb) to begin your journey! 🎨
+- **[validator_params](../code_reference/validator_params.md)** - Validator configuration options