🎨 NeMo Data Designer 101: The Basics

📚 What you'll learn

This notebook demonstrates the basics of Data Designer by generating a simple product review dataset.

👋 IMPORTANT – Environment Setup

• If you haven't already, follow the instructions in the README to install the necessary dependencies.

• You may need to restart your notebook's kernel after setting up the environment.

• In this notebook, we assume you have a self-hosted instance of Data Designer up and running.

• For deployment instructions, see the Installation Options section of the NeMo Data Designer documentation.

📦 Import the essentials

• The data_designer module of nemo_microservices exposes Data Designer's high-level SDK.

• The essentials module provides quick access to the most commonly used objects.

⚙️ Initialize the NeMo Data Designer Client

• NeMoDataDesignerClient is responsible for submitting generation requests to the microservice.

🎛️ Define model configurations

• Each ModelConfig defines a model that can be used during the generation process.

• The "model alias" is used to reference the model in the Data Designer config (as we will see below).

• The "model provider" is the external service that hosts the model (see the model config docs for more details).

• By default, the microservice uses build.nvidia.com as the model provider.

🏗️ Initialize the Data Designer Config Builder

• The Data Designer config defines the dataset schema and generation process.

• The config builder provides an intuitive interface for building this configuration.

• The list of model configs is provided to the builder at initialization.

🎲 Getting started with sampler columns

• Sampler columns offer non-LLM based generation of synthetic data.

• They are particularly useful for steering the diversity of the generated data, as we demonstrate below.

You can view available samplers using the config builder's info property:

Let's start designing our product review dataset by adding product category and subcategory columns.

Next, let's add samplers to generate data related to the customer and their review.

🦜 LLM-generated columns

• The real power of Data Designer comes from leveraging LLMs to generate text, code, and structured data.

• When prompting the LLM, we can use Jinja templating to reference other columns in the dataset.

• As we see below, nested json fields can be accessed using dot notation.

🔁 Iteration is key – preview the dataset!

1. Use the preview method to generate a sample of records quickly.

2. Inspect the results for quality and format issues.

3. Adjust column configurations, prompts, or parameters as needed.

4. Re-run the preview until satisfied.

📊 Analyze the generated data

• Data Designer automatically generates a basic statistical analysis of the generated data.

• This analysis is available via the analysis property of generation result objects.

🆙 Scale up!

• Happy with your preview data?

• Use the create method to submit larger Data Designer generation jobs.

⏭️ Next Steps

Now that you've seen the basics of Data Designer, check out the following notebooks to learn more about:

• Structured outputs and jinja expressions

• Seeding synthetic data generation with an external dataset