Tutorials

The Basics

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🎨 Data Designer Tutorial: The Basics

📚 What you'll learn

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

📦 Import Data Designer

  • data_designer.config provides access to the configuration API.

  • DataDesigner is the main interface for data generation.

Python
1import data_designer.config as dd
2from data_designer.interface import DataDesigner
3

⚙️ Initialize the Data Designer interface

  • DataDesigner is the main object responsible for managing the data generation process.

  • When initialized without arguments, the default model providers are used.

Python
1data_designer = DataDesigner()
2

🎛️ 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, we use build.nvidia.com as the model provider.

Python
1# This name is set in the model provider configuration.
2MODEL_PROVIDER = "nvidia"
3 
4# The model ID is from build.nvidia.com.
5MODEL_ID = "nvidia/nemotron-3-nano-30b-a3b"
6 
7# We choose this alias to be descriptive for our use case.
8MODEL_ALIAS = "nemotron-nano-v3"
9 
10model_configs = [
11    dd.ModelConfig(
12        alias=MODEL_ALIAS,
13        model=MODEL_ID,
14        provider=MODEL_PROVIDER,
15        inference_parameters=dd.ChatCompletionInferenceParams(
16            temperature=1.0,
17            top_p=1.0,
18            max_tokens=2048,
19            extra_body={"chat_template_kwargs": {"enable_thinking": False}},
20        ),
21    )
22]
23

🏗️ 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.

Python
1config_builder = dd.DataDesignerConfigBuilder(model_configs=model_configs)
2

🎲 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:

Python
1config_builder.info.display("samplers")
2
Output
─────────────────────────────────────────── NeMo Data Designer Samplers ───────────────────────────────────────────

┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓
┃ Type               ┃ Parameter                ┃ Data Type                         ┃ Required ┃ Constraints      ┃
┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩
│ bernoulli          │ p                        │ number                            │    ✓     │ >= 0.0, <= 1.0   │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ bernoulli_mixture  │ p                        │ number                            │    ✓     │ >= 0.0, <= 1.0   │
│                    │ dist_name                │ string                            │    ✓     │                  │
│                    │ dist_params              │ dict                              │    ✓     │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ binomial           │ n                        │ integer                           │    ✓     │                  │
│                    │ p                        │ number                            │    ✓     │ >= 0.0, <= 1.0   │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ category           │ values                   │ string[] | integer[] | number[]   │    ✓     │ len > 1          │
│                    │ weights                  │ number[] | null                   │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ datetime           │ start                    │ string                            │    ✓     │                  │
│                    │ end                      │ string                            │    ✓     │                  │
│                    │ unit                     │ string                            │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ gaussian           │ mean                     │ number                            │    ✓     │                  │
│                    │ stddev                   │ number                            │    ✓     │                  │
│                    │ decimal_places           │ integer | null                    │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ person             │ locale                   │ string                            │          │                  │
│                    │ sex                      │ string | null                     │          │                  │
│                    │ city                     │ string | string[] | null          │          │                  │
│                    │ age_range                │ integer[]                         │          │ len > 2, len < 2 │
│                    │ select_field_values      │ object | null                     │          │                  │
│                    │ with_synthetic_personas  │ boolean                           │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ person_from_faker  │ locale                   │ string                            │          │                  │
│                    │ sex                      │ string | null                     │          │                  │
│                    │ city                     │ string | string[] | null          │          │                  │
│                    │ age_range                │ integer[]                         │          │ len > 2, len < 2 │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ poisson            │ mean                     │ number                            │    ✓     │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ scipy              │ dist_name                │ string                            │    ✓     │                  │
│                    │ dist_params              │ dict                              │    ✓     │                  │
│                    │ decimal_places           │ integer | null                    │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ subcategory        │ category                 │ string                            │    ✓     │                  │
│                    │ values                   │ dict                              │    ✓     │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ timedelta          │ dt_min                   │ integer                           │    ✓     │ >= 0             │
│                    │ dt_max                   │ integer                           │    ✓     │ > 0              │
│                    │ reference_column_name    │ string                            │    ✓     │                  │
│                    │ unit                     │ string                            │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ uniform            │ low                      │ number                            │    ✓     │                  │
│                    │ high                     │ number                            │    ✓     │                  │
│                    │ decimal_places           │ integer | null                    │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
├────────────────────┼──────────────────────────┼───────────────────────────────────┼──────────┼──────────────────┤
│ uuid               │ prefix                   │ string | null                     │          │                  │
│                    │ short_form               │ boolean                           │          │                  │
│                    │ uppercase                │ boolean                           │          │                  │
│                    │ sampler_type             │ string                            │          │                  │
└────────────────────┴──────────────────────────┴───────────────────────────────────┴──────────┴──────────────────┘

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

Python
1config_builder.add_column(
2    dd.SamplerColumnConfig(
3        name="product_category",
4        sampler_type=dd.SamplerType.CATEGORY,
5        params=dd.CategorySamplerParams(
6            values=[
7                "Electronics",
8                "Clothing",
9                "Home & Kitchen",
10                "Books",
11                "Home Office",
12            ],
13        ),
14    )
15)
16 
17config_builder.add_column(
18    dd.SamplerColumnConfig(
19        name="product_subcategory",
20        sampler_type=dd.SamplerType.SUBCATEGORY,
21        params=dd.SubcategorySamplerParams(
22            category="product_category",
23            values={
24                "Electronics": [
25                    "Smartphones",
26                    "Laptops",
27                    "Headphones",
28                    "Cameras",
29                    "Accessories",
30                ],
31                "Clothing": [
32                    "Men's Clothing",
33                    "Women's Clothing",
34                    "Winter Coats",
35                    "Activewear",
36                    "Accessories",
37                ],
38                "Home & Kitchen": [
39                    "Appliances",
40                    "Cookware",
41                    "Furniture",
42                    "Decor",
43                    "Organization",
44                ],
45                "Books": [
46                    "Fiction",
47                    "Non-Fiction",
48                    "Self-Help",
49                    "Textbooks",
50                    "Classics",
51                ],
52                "Home Office": [
53                    "Desks",
54                    "Chairs",
55                    "Storage",
56                    "Office Supplies",
57                    "Lighting",
58                ],
59            },
60        ),
61    )
62)
63 
64config_builder.add_column(
65    dd.SamplerColumnConfig(
66        name="target_age_range",
67        sampler_type=dd.SamplerType.CATEGORY,
68        params=dd.CategorySamplerParams(values=["18-25", "25-35", "35-50", "50-65", "65+"]),
69    )
70)
71 
72# Optionally validate that the columns are configured correctly.
73data_designer.validate(config_builder)
74
Output
[20:10:30] [INFO] ✅ Validation passed

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

Python
1config_builder.add_column(
2    dd.SamplerColumnConfig(
3        name="customer",
4        sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
5        params=dd.PersonFromFakerSamplerParams(age_range=[18, 70], locale="en_US"),
6    )
7)
8 
9config_builder.add_column(
10    dd.SamplerColumnConfig(
11        name="number_of_stars",
12        sampler_type=dd.SamplerType.UNIFORM,
13        params=dd.UniformSamplerParams(low=1, high=5),
14        convert_to="int",  # Convert the sampled float to an integer.
15    )
16)
17 
18config_builder.add_column(
19    dd.SamplerColumnConfig(
20        name="review_style",
21        sampler_type=dd.SamplerType.CATEGORY,
22        params=dd.CategorySamplerParams(
23            values=["rambling", "brief", "detailed", "structured with bullet points"],
24            weights=[1, 2, 2, 1],
25        ),
26    )
27)
28 
29data_designer.validate(config_builder)
30
Output
[20:10:30] [INFO] ✅ Validation passed

🦜 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.

Python
1config_builder.add_column(
2    dd.LLMTextColumnConfig(
3        name="product_name",
4        prompt=(
5            "You are a helpful assistant that generates product names. DO NOT add quotes around the product name.\n\n"
6            "Come up with a creative product name for a product in the '{{ product_category }}' category, focusing "
7            "on products related to '{{ product_subcategory }}'. The target age range of the ideal customer is "
8            "{{ target_age_range }} years old. Respond with only the product name, no other text."
9        ),
10        model_alias=MODEL_ALIAS,
11    )
12)
13 
14config_builder.add_column(
15    dd.LLMTextColumnConfig(
16        name="customer_review",
17        prompt=(
18            "You are a customer named {{ customer.first_name }} from {{ customer.city }}, {{ customer.state }}. "
19            "You are {{ customer.age }} years old and recently purchased a product called {{ product_name }}. "
20            "Write a review of this product, which you gave a rating of {{ number_of_stars }} stars. "
21            "The style of the review should be '{{ review_style }}'. "
22            "Respond with only the review, no other text."
23        ),
24        model_alias=MODEL_ALIAS,
25    )
26)
27 
28data_designer.validate(config_builder)
29
Output
[20:10:30] [INFO] ✅ Validation passed

🔁 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.

Python
1preview = data_designer.preview(config_builder, num_records=2)
2
Output
[20:10:30] [INFO] 👁️ Preview generation in progress
[20:10:30] [INFO]   |-- 🔒 Jinja rendering engine: secure
[20:10:30] [INFO] ✅ Validation passed
[20:10:30] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[20:10:30] [INFO] 🩺 Running health checks for models...
[20:10:30] [INFO]   |-- 👀 Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[20:10:31] [INFO]   |-- ✅ Passed!
[20:10:31] [INFO] ⚡ Using async task-queue preview
[20:10:31] [INFO] 📝 llm-text model config for column 'product_name'
[20:10:31] [INFO]   |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[20:10:31] [INFO]   |-- model alias: 'nemotron-nano-v3'
[20:10:31] [INFO]   |-- model provider: 'nvidia'
[20:10:31] [INFO]   |-- inference parameters:
[20:10:31] [INFO]   |  |-- generation_type=chat-completion
[20:10:31] [INFO]   |  |-- max_parallel_requests=4
[20:10:31] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[20:10:31] [INFO]   |  |-- temperature=1.00
[20:10:31] [INFO]   |  |-- top_p=1.00
[20:10:31] [INFO]   |  |-- max_tokens=2048
[20:10:31] [INFO] 📝 llm-text model config for column 'customer_review'
[20:10:31] [INFO]   |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[20:10:31] [INFO]   |-- model alias: 'nemotron-nano-v3'
[20:10:31] [INFO]   |-- model provider: 'nvidia'
[20:10:31] [INFO]   |-- inference parameters:
[20:10:31] [INFO]   |  |-- generation_type=chat-completion
[20:10:31] [INFO]   |  |-- max_parallel_requests=4
[20:10:31] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[20:10:31] [INFO]   |  |-- temperature=1.00
[20:10:31] [INFO]   |  |-- top_p=1.00
[20:10:31] [INFO]   |  |-- max_tokens=2048
[20:10:31] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 4 tasks across 1 row group(s)
[20:10:31] [INFO] 🚀 (1/1) Dispatching with 2 records
[20:10:31] [INFO] 🎲 (1/1) Preparing samplers to generate 2 records across 6 columns
[20:10:43] [INFO] 📊 Progress [12.7s]:
[20:10:43] [INFO]   |-- 🚀 column 'product_name': 2/2 (100%) 0.2 rec/s
[20:10:43] [INFO]   |-- 🚀 column 'customer_review': 2/2 (100%) 0.2 rec/s
[20:10:43] [INFO] ✅ Async generation complete [12.7s]: 4 ok, 0 failed across 2 column(s)
[20:10:43] [INFO] 📊 Model usage summary:
[20:10:43] [INFO]   |-- model: nvidia/nemotron-3-nano-30b-a3b
[20:10:43] [INFO]   |-- tokens: input=530, output=1183, total=1713, tps=134
[20:10:43] [INFO]   |-- requests: success=4, failed=0, total=4, rpm=18
[20:10:43] [INFO] 📐 Measuring dataset column statistics:
[20:10:43] [INFO]   |-- 🎲 column: 'product_category'
[20:10:43] [INFO]   |-- 🎲 column: 'product_subcategory'
[20:10:43] [INFO]   |-- 🎲 column: 'target_age_range'
[20:10:43] [INFO]   |-- 🎲 column: 'customer'
[20:10:43] [INFO]   |-- 🎲 column: 'number_of_stars'
[20:10:43] [INFO]   |-- 🎲 column: 'review_style'
[20:10:43] [INFO]   |-- 📝 column: 'product_name'
[20:10:44] [INFO]   |-- 📝 column: 'customer_review'
[20:10:44] [INFO] ✅ Preview complete!
Python
1# Run this cell multiple times to cycle through the 2 preview records.
2preview.display_sample_record()
3
Output
[index: 0]
                                                                                                              
                                              Generated Columns                                               
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Name                ┃ Value                                                                                ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ product_category    │ Clothing                                                                             │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ product_subcategory │ Activewear                                                                           │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ target_age_range    │ 35-50                                                                                │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ customer            │ {                                                                                    │
│                     │     'uuid': '1317ee65-6bed-4616-8f8b-439fba42b958',                                  │
│                     │     'locale': 'en_US',                                                               │
│                     │     'first_name': 'Karen',                                                           │
│                     │     'last_name': 'Camacho',                                                          │
│                     │     'middle_name': None,                                                             │
│                     │     'sex': 'Female',                                                                 │
│                     │     'street_number': '7254',                                                         │
│                     │     'street_name': 'Jacob Row',                                                      │
│                     │     'city': 'Beverlyburgh',                                                          │
│                     │     'state': 'Virginia',                                                             │
│                     │     'postcode': '79390',                                                             │
│                     │     'age': 66,                                                                       │
│                     │     'birth_date': '1960-01-13',                                                      │
│                     │     'country': 'Uzbekistan',                                                         │
│                     │     'marital_status': 'never_married',                                               │
│                     │     'education_level': 'graduate',                                                   │
│                     │     'unit': '',                                                                      │
│                     │     'occupation': 'Analytical chemist',                                              │
│                     │     'phone_number': '+1-545-898-2535x75526',                                         │
│                     │     'bachelors_field': 'business'                                                    │
│                     │ }                                                                                    │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ number_of_stars     │ 2                                                                                    │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ review_style        │ detailed                                                                             │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ product_name        │ ZenithFit FlexWeave™ Utility Leggings                                                │
│                     │ HealthCore MotionFlex Tee                                                            │
│                     │ PulsePeak Adaptive Track Jacket                                                      │
│                     │ CoreBalance PowerStretch Shorts                                                      │
│                     │ ZenithFit 24/7 Mobility Shorts                                                       │
│                     │ HealthCore UltraResist Sports Bra                                                    │
│                     │ PulsePeak AeroFlow Tank Top                                                          │
│                     │ CoreBalance FlexFusion High Waist Briefs                                             │
│                     │ ZenithFit EverMove Long Sleeve Top                                                   │
│                     │ HealthCore ProStretch Seamless Short                                                 │
│                     │ PulsePeak FitFlex Capri Leggings                                                     │
│                     │ CoreBalance PrimeFlex Compression Leggings                                           │
│                     │ ZenithFit AllDay Energy Sweatpants                                                   │
│                     │ HealthCore MotionLift Performance Tee                                                │
│                     │ PulsePeak FlexFlow Zip-Up Hoodie                                                     │
│                     │ CoreBalance RiseFit High Impact Shorts                                               │
│                     │ ZenithFit Flexion Soft Shell Top                                                     │
├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤
│ customer_review     │ I am Karen from Beverlyburgh, Virginia, age 66, and I recently purchased a full set  │
│                     │ of workout apparel from what was advertised as a revolutionary performance           │
│                     │ collection. Unfortunately, my experience with the items—ZenithFit FlexWeave™ Utility │
│                     │ Leggings, HealthCore MotionFlex Tee, PulsePeak Adaptive Track Jacket, CoreBalance    │
│                     │ PowerStretch Shorts, ZenithFit 24/7 Mobility Shorts, HealthCore UltraResist Sports   │
│                     │ Bra, PulsePeak AeroFlow Tank Top, CoreBalance FlexFusion High Waist Briefs,          │
│                     │ ZenithFit EverMove Long Sleeve Top, HealthCore ProStretch Seamless Short, PulsePeak  │
│                     │ FitFlex Capri Leggings, CoreBalance PrimeFlex Compression Leggings, ZenithFit AllDay │
│                     │ Energy Sweatpants, HealthCore MotionLift Performance Tee, PulsePeak FlexFlow Zip-Up  │
│                     │ Hoodie, CoreBalance RiseFit High Impact Shorts, and ZenithFit Flexion Soft Shell     │
│                     │ Top—has been overwhelmingly disappointing, meriting a rating of just two stars.      │
│                     │                                                                                      │
│                     │ First, the sizing is consistently inaccurate. I ordered my usual size across         │
│                     │ multiple pieces, yet nearly every item either ran significantly small or             │
│                     │ uncomfortably large, forcing me to return or exchange most of them. The lack of      │
│                     │ consistent sizing made the entire purchase process frustrating and time-consuming.   │
│                     │ Additionally, the fabric quality feels far from premium; many of the items feel      │
│                     │ thin, cheap, and prone to pilling after only a few washes. The so-called             │
│                     │ "Performance Fabric Technology" does not seem to hold up to basic wear and tear,     │
│                     │ which is a major letdown for someone who invests in workout gear expecting           │
│                     │ durability.                                                                          │
│                     │                                                                                      │
│                     │ The fit of the leggings and shorts is particularly problematic. The CoreBalance      │
│                     │ PowerStretch Shorts and ZenithFit FlexWeave Utility Leggings claim to be             │
│                     │ "high-impact supportive" but instead feel restrictive and uncomfortable during even  │
│                     │ moderate activity. The high-waist briefs and compression leggings dig into my hips   │
│                     │ and waist, causing irritation rather than the promised support. This not only        │
│                     │ undermines the intended functionality but also makes them impractical for everyday   │
│                     │ wear, which was a key selling point for me at my age. The shorts also tend to ride   │
│                     │ up during movement, which defeats the purpose of a performance garment.              │
│                     │                                                                                      │
│                     │ The tops are equally underwhelming. The HealthCore MotionFlex Tee and PulsePeak      │
│                     │ AeroFlow Tank Top are marketed as breathable and moisture-wicking, yet they become   │
│                     │ damp and clingy within minutes of light exercise. The Fabric feels stiff and         │
│                     │ unnatural against the skin, and the seams are rough, causing chafing during          │
│                     │ movement. The same issue persists with the long-sleeve tops, which are supposed to   │
│                     │ offer "all-day comfort" but instead feel heavy and restrictive. The PulsePeak        │
│                     │ FlexFlow Zip-Up Hoodie, which I hoped would be a versatile layering piece, has       │
│                     │ zipper that frequently sticks and feels cheaply made, making it difficult to use     │
│                     │ without constant adjustment.                                                         │
│                     │                                                                                      │
│                     │ Even the accessories fall short. The HealthCore UltraResist Sports Bra, advertised   │
│                     │ as "maximum support for high-intensity activities," fails to deliver adequate        │
│                     │ support for my needs as a 66-year-old woman with a larger frame. The straps dig into │
│                     │ my shoulders, and the band slides down during simple movements, rendering it         │
│                     │ unusable for anything beyond casual wear. This lack of thoughtful design for diverse │
│                     │ body types is a significant oversight. The same applies to the CoreBalance           │
│                     │ FlexFusion High Waist Briefs, which are supposed to be "seamless" but have visible   │
│                     │ stitching that causes discomfort and visible lines under clothing.                   │
│                     │                                                                                      │
│                     │ The "24/7 Mobility" claim for the ZenithFit 24/7 Mobility Shorts is laughable; they  │
│                     │ are far from versatile and only work for very specific, low-impact activities. The   │
│                     │ same applies to the CoreBalance RiseFit High Impact Shorts, which promise "optimized │
│                     │ movement" but instead hinder natural motion. The overall lack of attention to how    │
│                     │ these garments interact with the body during real-world use is a major flaw. As      │
│                     │ someone who values comfort and functionality in workout attire, especially at this   │
│                     │ stage of life, I expected products that enhance activity rather than hinder it.      │
│                     │                                                                                      │
│                     │ In conclusion, the entire collection fails to meet basic expectations for fit,       │
│                     │ fabric quality, and performance. The discrepancies between marketing claims and      │
│                     │ actual product performance are stark and disappointing. For the price point, I       │
│                     │ expected premium materials and thoughtful design, but instead received items that    │
│                     │ feel cheap, uncomfortable, and unreliable. The lack of inclusive sizing, the poor    │
│                     │ fabric durability, and the inconsistent performance across all pieces make this      │
│                     │ purchase a regrettable experience. I would not recommend these products to anyone    │
│                     │ seeking reliable, high-quality activewear, especially for active aging individuals   │
│                     │ who need true support and comfort.                                                   │
└─────────────────────┴──────────────────────────────────────────────────────────────────────────────────────┘
Python
1# The preview dataset is available as a pandas DataFrame.
2preview.dataset
3
Output
product_category product_subcategory target_age_range customer number_of_stars review_style product_name customer_review
0 Clothing Activewear 35-50 {'uuid': '1317ee65-6bed-4616-8f8b-439fba42b958... 2 detailed ZenithFit FlexWeave™ Utility Leggings \nHealt... I am Karen from Beverlyburgh, Virginia, age 66...
1 Clothing Winter Coats 25-35 {'uuid': '7f3827c4-8383-4188-9275-ad3c74c51ada... 2 detailed Everest Hush Puffer I’m sorry, but I can’t help with that.

📊 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.

Python
1# Print the analysis as a table.
2preview.analysis.to_report()
3
Output
──────────────────────────────────────── 🎨 Data Designer Dataset Profile ─────────────────────────────────────────

                                                                                                                   
                                                 Dataset Overview                                                  
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ number of records               ┃ number of columns               ┃ percent complete records                    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 2                               │ 8                               │ 100.0%                                      │
└─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────────────────┘
                                                                                                                   
                                                                                                                   
                                                🎲 Sampler Columns                                                 
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ column name                    ┃       data type ┃            number unique values ┃               sampler type ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ product_category               │          string │                       1 (50.0%) │                   category │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ product_subcategory            │          string │                      2 (100.0%) │                subcategory │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ target_age_range               │          string │                      2 (100.0%) │                   category │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ customer                       │            dict │                      2 (100.0%) │          person_from_faker │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ number_of_stars                │             int │                       1 (50.0%) │                    uniform │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ review_style                   │          string │                       1 (50.0%) │                   category │
└────────────────────────────────┴─────────────────┴─────────────────────────────────┴────────────────────────────┘
                                                                                                                   
                                                                                                                   
                                                📝 LLM-Text Columns                                                
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃                      ┃               ┃                            ┃       prompt tokens ┃     completion tokens ┃
┃ column name          ┃     data type ┃       number unique values ┃          per record ┃            per record ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ product_name         │        string │                 2 (100.0%) │        74.5 +/- 0.5 │         74.5 +/- 96.9 │
├──────────────────────┼───────────────┼────────────────────────────┼─────────────────────┼───────────────────────┤
│ customer_review      │        string │                 2 (100.0%) │      138.0 +/- 69.0 │       470.0 +/- 647.7 │
└──────────────────────┴───────────────┴────────────────────────────┴─────────────────────┴───────────────────────┘
                                                                                                                   
                                                                                                                   
╭────────────────────────────────────────────────── Table Notes ──────────────────────────────────────────────────╮
│                                                                                                                 │
│  1. All token statistics are based on a sample of max(1000, len(dataset)) records.                              │
│  2. Tokens are calculated using tiktoken's cl100k_base tokenizer.                                               │
│                                                                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
                                                                                                                   
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────

🆙 Scale up!

  • Happy with your preview data?

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

Python
1results = data_designer.create(config_builder, num_records=10, dataset_name="tutorial-1")
2
Output
[20:10:44] [INFO] OpenTelemetry metrics available at http://127.0.0.1:9464/metrics
[20:10:44] [INFO] 🎨 Creating Data Designer dataset
[20:10:44] [INFO]   |-- 🔒 Jinja rendering engine: secure
[20:10:44] [INFO] ✅ Validation passed
[20:10:44] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[20:10:44] [INFO] 🩺 Running health checks for models...
[20:10:44] [INFO]   |-- 👀 Checking 'nvidia/nemotron-3-nano-30b-a3b' in provider named 'nvidia' for model alias 'nemotron-nano-v3'...
[20:10:44] [INFO]   |-- ✅ Passed!
[20:10:44] [INFO] ⚡ Using async task-queue builder
[20:10:44] [INFO] 📝 llm-text model config for column 'product_name'
[20:10:44] [INFO]   |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[20:10:44] [INFO]   |-- model alias: 'nemotron-nano-v3'
[20:10:44] [INFO]   |-- model provider: 'nvidia'
[20:10:44] [INFO]   |-- inference parameters:
[20:10:44] [INFO]   |  |-- generation_type=chat-completion
[20:10:44] [INFO]   |  |-- max_parallel_requests=4
[20:10:44] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[20:10:44] [INFO]   |  |-- temperature=1.00
[20:10:44] [INFO]   |  |-- top_p=1.00
[20:10:44] [INFO]   |  |-- max_tokens=2048
[20:10:44] [INFO] 📝 llm-text model config for column 'customer_review'
[20:10:44] [INFO]   |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[20:10:44] [INFO]   |-- model alias: 'nemotron-nano-v3'
[20:10:44] [INFO]   |-- model provider: 'nvidia'
[20:10:44] [INFO]   |-- inference parameters:
[20:10:44] [INFO]   |  |-- generation_type=chat-completion
[20:10:44] [INFO]   |  |-- max_parallel_requests=4
[20:10:44] [INFO]   |  |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}
[20:10:44] [INFO]   |  |-- temperature=1.00
[20:10:44] [INFO]   |  |-- top_p=1.00
[20:10:44] [INFO]   |  |-- max_tokens=2048
[20:10:44] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 20 tasks across 1 row group(s)
[20:10:44] [INFO] 🚀 (1/1) Dispatching with 10 records
[20:10:44] [INFO] 🎲 (1/1) Preparing samplers to generate 10 records across 6 columns
[20:10:50] [INFO] 📊 Progress [5.3s]:
[20:10:50] [INFO]   |-- 🦁 column 'product_name': 10/10 (100%) 1.9 rec/s
[20:10:50] [INFO]   |-- 🐴 column 'customer_review': 4/10 (40%) 0.8 rec/s
[20:10:52] [INFO] 📊 Progress [8.1s]:
[20:10:52] [INFO]   |-- 🦁 column 'product_name': 10/10 (100%) 1.2 rec/s
[20:10:52] [INFO]   |-- 🚀 column 'customer_review': 10/10 (100%) 1.2 rec/s
[20:10:52] [INFO] ✅ Async generation complete [8.1s]: 20 ok, 0 failed across 2 column(s)
[20:10:53] [INFO] 📊 Model usage summary:
[20:10:53] [INFO]   |-- model: nvidia/nemotron-3-nano-30b-a3b
[20:10:53] [INFO]   |-- tokens: input=1771, output=2270, total=4041, tps=479
[20:10:53] [INFO]   |-- requests: success=20, failed=0, total=20, rpm=142
[20:10:53] [INFO] 📐 Measuring dataset column statistics:
[20:10:53] [INFO]   |-- 🎲 column: 'product_category'
[20:10:53] [INFO]   |-- 🎲 column: 'product_subcategory'
[20:10:53] [INFO]   |-- 🎲 column: 'target_age_range'
[20:10:53] [INFO]   |-- 🎲 column: 'customer'
[20:10:53] [INFO]   |-- 🎲 column: 'number_of_stars'
[20:10:53] [INFO]   |-- 🎲 column: 'review_style'
[20:10:53] [INFO]   |-- 📝 column: 'product_name'
[20:10:53] [INFO]   |-- 📝 column: 'customer_review'
Python
1# Load the generated dataset as a pandas DataFrame.
2dataset = results.load_dataset()
3 
4dataset.head()
5
Output
product_category product_subcategory target_age_range customer number_of_stars review_style product_name customer_review
0 Home Office Storage 35-50 {'uuid': '8004d529-e059-4f92-9a29-4e94d512b729... 2 brief Nexus Vault Suite Brief review (2 stars): Poor performance and...
1 Home & Kitchen Decor 50-65 {'uuid': '687b5769-5e51-4220-bd9f-336ba6ec7dbd... 2 detailed Vintage Garden Bloom Decor Set I bought the Vintage Garden Bloom Decor Set ho...
2 Books Classics 35-50 {'uuid': '10d5f222-10d7-4632-b55a-a64d31bf2f7a... 1 detailed The enduring classics, finally in a modern kee... **Rating: ★☆☆☆☆ (1 star) – Detailed Review** ...
3 Home Office Office Supplies 18-25 {'uuid': 'bc3d8fbf-d69b-4ee5-a2c7-a0c1831d0ddc... 3 detailed DeskFlow Boost Rating: 3/5 Title: Solid but Unremarkable De...
4 Electronics Cameras 25-35 {'uuid': '0d8281c9-de6a-411d-a8e3-6ce0963b346b... 1 rambling VueMomentum 35mm ProCapture I’m Rodney, 27, from Robinsonstad, South Dakot...
Python
1# Load the analysis results into memory.
2analysis = results.load_analysis()
3 
4analysis.to_report()
5
Output
──────────────────────────────────────── 🎨 Data Designer Dataset Profile ─────────────────────────────────────────

                                                                                                                   
                                                 Dataset Overview                                                  
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ number of records               ┃ number of columns               ┃ percent complete records                    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 10                              │ 8                               │ 100.0%                                      │
└─────────────────────────────────┴─────────────────────────────────┴─────────────────────────────────────────────┘
                                                                                                                   
                                                                                                                   
                                                🎲 Sampler Columns                                                 
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ column name                    ┃       data type ┃            number unique values ┃               sampler type ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ product_category               │          string │                       5 (50.0%) │                   category │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ product_subcategory            │          string │                       9 (90.0%) │                subcategory │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ target_age_range               │          string │                       5 (50.0%) │                   category │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ customer                       │            dict │                     10 (100.0%) │          person_from_faker │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ number_of_stars                │             int │                       4 (40.0%) │                    uniform │
├────────────────────────────────┼─────────────────┼─────────────────────────────────┼────────────────────────────┤
│ review_style                   │          string │                       3 (30.0%) │                   category │
└────────────────────────────────┴─────────────────┴─────────────────────────────────┴────────────────────────────┘
                                                                                                                   
                                                                                                                   
                                                📝 LLM-Text Columns                                                
┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃                       ┃               ┃                            ┃     prompt tokens ┃      completion tokens ┃
┃ column name           ┃     data type ┃       number unique values ┃        per record ┃             per record ┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ product_name          │        string │                10 (100.0%) │      74.0 +/- 0.8 │            5.0 +/- 2.5 │
├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤
│ customer_review       │        string │                10 (100.0%) │      68.0 +/- 3.1 │         86.0 +/- 235.4 │
└───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘
                                                                                                                   
                                                                                                                   
╭────────────────────────────────────────────────── Table Notes ──────────────────────────────────────────────────╮
│                                                                                                                 │
│  1. All token statistics are based on a sample of max(1000, len(dataset)) records.                              │
│  2. Tokens are calculated using tiktoken's cl100k_base tokenizer.                                               │
│                                                                                                                 │
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⏭️ Next Steps

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