The Basics
🎨 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
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data_designer.configprovides access to the configuration API. -
DataDesigneris the main interface for data generation.
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⚙️ Initialize the Data Designer interface
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DataDesigneris the main object responsible for managing the data generation process. -
When initialized without arguments, the default model providers are used.
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🎛️ Define model configurations
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Each
ModelConfigdefines 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).
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The "model provider" is the external service that hosts the model (see the model config docs for more details).
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By default, we use build.nvidia.com as the model provider.
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🏗️ Initialize the Data Designer Config Builder
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The Data Designer config defines the dataset schema and generation process.
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The config builder provides an intuitive interface for building this configuration.
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The list of model configs is provided to the builder at initialization.
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🎲 Getting started with sampler columns
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Sampler columns offer non-LLM based generation of synthetic data.
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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:
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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.
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Output
[17:18:49] [INFO] ✅ Validation passed
Next, let's add samplers to generate data related to the customer and their review.
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Output
[17:18:49] [INFO] ✅ Validation passed
🦜 LLM-generated columns
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The real power of Data Designer comes from leveraging LLMs to generate text, code, and structured data.
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When prompting the LLM, we can use Jinja templating to reference other columns in the dataset.
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As we see below, nested json fields can be accessed using dot notation.
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Output
[17:18:49] [INFO] ✅ Validation passed
🔁 Iteration is key – preview the dataset!
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Use the
previewmethod to generate a sample of records quickly. -
Inspect the results for quality and format issues.
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Adjust column configurations, prompts, or parameters as needed.
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Re-run the preview until satisfied.
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Output
[17:18:49] [INFO] 🖼️ Preview generation in progress
[17:18:49] [INFO] |-- 🔒 Jinja rendering engine: secure
[17:18:49] [INFO] ✅ Validation passed
[17:18:49] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[17:18:49] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[17:18:49] [INFO] ⚡ Using async task-queue preview
[17:18:49] [INFO] 📝 llm-text model config for column 'product_name'
[17:18:49] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[17:18:49] [INFO] |-- model alias: 'nemotron-nano-v3'
[17:18:49] [INFO] |-- model provider: 'nvidia'
[17:18:49] [INFO] |-- inference parameters:
[17:18:49] [INFO] | |-- generation_type=chat-completion
[17:18:49] [INFO] | |-- max_parallel_requests=4
[17:18:49] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[17:18:49] [INFO] | |-- temperature=1.00
[17:18:49] [INFO] | |-- top_p=1.00
[17:18:49] [INFO] | |-- max_tokens=2048
[17:18:49] [INFO] 📝 llm-text model config for column 'customer_review'
[17:18:49] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[17:18:49] [INFO] |-- model alias: 'nemotron-nano-v3'
[17:18:49] [INFO] |-- model provider: 'nvidia'
[17:18:49] [INFO] |-- inference parameters:
[17:18:49] [INFO] | |-- generation_type=chat-completion
[17:18:49] [INFO] | |-- max_parallel_requests=4
[17:18:49] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[17:18:49] [INFO] | |-- temperature=1.00
[17:18:49] [INFO] | |-- top_p=1.00
[17:18:49] [INFO] | |-- max_tokens=2048
[17:18:49] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 4 tasks across 1 row group(s)
[17:18:49] [INFO] 🚀 (1/1) Dispatching with 2 records
[17:18:49] [INFO] 🎲 (1/1) Preparing samplers to generate 2 records across 6 columns
[17:19:02] [INFO] 📊 Progress [13.4s]:
[17:19:02] [INFO] |-- 🐔 column 'product_name': 2/2 (100%) 0.1 rec/s
[17:19:02] [INFO] |-- 🚗 column 'customer_review': 1/2 (50%) 0.1 rec/s
[17:19:02] [INFO] 📊 Progress [13.8s]:
[17:19:02] [INFO] |-- 🐔 column 'product_name': 2/2 (100%) 0.1 rec/s
[17:19:02] [INFO] |-- 🚀 column 'customer_review': 2/2 (100%) 0.1 rec/s
[17:19:03] [INFO] ✅ Async generation complete [13.8s]: 4 ok, 0 failed across 2 column(s)
[17:19:03] [INFO] 📊 Model usage summary:
[17:19:03] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[17:19:03] [INFO] |-- tokens: input=359, output=1109, total=1468, tps=105
[17:19:03] [INFO] |-- requests: success=4, failed=0, total=4, rpm=17
[17:19:03] [INFO] 📐 Measuring dataset column statistics:
[17:19:03] [INFO] |-- 🎲 column: 'product_category'
[17:19:03] [INFO] |-- 🎲 column: 'product_subcategory'
[17:19:03] [INFO] |-- 🎲 column: 'target_age_range'
[17:19:03] [INFO] |-- 🎲 column: 'customer'
[17:19:03] [INFO] |-- 🎲 column: 'number_of_stars'
[17:19:03] [INFO] |-- 🎲 column: 'review_style'
[17:19:03] [INFO] |-- 📝 column: 'product_name'
[17:19:03] [INFO] |-- 📝 column: 'customer_review'
[17:19:03] [INFO] ☀️ Preview complete!
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Output
[index: 0]
Generated Columns ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Name ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ product_category │ Books │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ product_subcategory │ Non-Fiction │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ target_age_range │ 35-50 │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ customer │ { │ │ │ 'uuid': '3b224ef2-d1f0-486a-842e-b48a6eebeb8b', │ │ │ 'locale': 'en_US', │ │ │ 'first_name': 'Ashley', │ │ │ 'last_name': 'Avery', │ │ │ 'middle_name': None, │ │ │ 'sex': 'Female', │ │ │ 'street_number': '47655', │ │ │ 'street_name': 'Kaitlin Shores', │ │ │ 'city': 'New Jamesborough', │ │ │ 'state': 'Maine', │ │ │ 'postcode': '22895', │ │ │ 'age': 51, │ │ │ 'birth_date': '1975-02-03', │ │ │ 'country': 'Netherlands Antilles', │ │ │ 'marital_status': 'divorced', │ │ │ 'education_level': 'graduate', │ │ │ 'unit': '', │ │ │ 'occupation': 'Armed forces training and education officer', │ │ │ 'phone_number': '945.649.2510', │ │ │ 'bachelors_field': 'education' │ │ │ } │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ number_of_stars │ 4 │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ review_style │ detailed │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ product_name │ Mindful Mastery Lifecycle │ ├─────────────────────┼──────────────────────────────────────────────────────────────────────────────────────┤ │ customer_review │ **Product:** Mindful Mastery Lifecycle │ │ │ **Rating:** ★★★★☆ (4 stars) │ │ │ │ │ │ I am a 51‑year‑old resident of New Jamesborough, Maine, and after carefully │ │ │ evaluating the Mindful Mastery Lifecycle, I decided to purchase it last month. │ │ │ Overall, the experience has been positive, though there are a few nuances worth │ │ │ noting. │ │ │ │ │ │ **First Impressions & Design** │ │ │ The packaging is clean and professional, featuring a sleek blue‑gray box with │ │ │ embossed lettering that conveys a sense of calm authority. Inside, the layout is │ │ │ intuitive: the workbook, a set of guided meditation cards, and a QR‑code tutorial │ │ │ booklet are neatly organized. The paper quality is solid—thick, matte-finished pages │ │ │ that resist creasing during repeated use. │ │ │ │ │ │ **Content & Usability** │ │ │ The program’s structure follows a logical 12‑week progression, each week building on │ │ │ the previous one with a blend of short video lessons (5–10 minutes), reflective │ │ │ journaling prompts, and practical exercises. I appreciate how the curriculum │ │ │ integrates mindfulness principles with performance enhancement techniques—something │ │ │ that stood out to me given my background in project management and community │ │ │ volunteering. │ │ │ │ │ │ - **Weekly Themes:** From “Foundations of Presence” to “Sustaining Flow,” the themes │ │ │ are thoughtfully sequenced and provide clear, incremental goals. │ │ │ - **Exercises:** The guided visualizations are well‑written, and the accompanying │ │ │ audio tracks (downloadable via the QR code) are soothing without being overly │ │ │ ambient. │ │ │ - **Tracking Tools:** The included progress tracker is simple yet effective; marking │ │ │ achieved milestones with stickers reinforced a sense of accomplishment. │ │ │ │ │ │ **What I Liked Most** │ │ │ - **Tailored for Adult Learners:** The language is clear, avoiding jargon that would │ │ │ alienate a more mature audience. │ │ │ - **Community Support:** Access to an online forum where participants share │ │ │ reflections was valuable; hearing how others in similar life stages navigated │ │ │ challenges added depth to my own practice. │ │ │ - **Time Flexibility:** Each module can be completed within a week, fitting nicely │ │ │ into my busy schedule of family commitments and part‑time work at the local library. │ │ │ │ │ │ **Areas for Improvement** │ │ │ 1. **Depth of Advanced Modules:** While the initial weeks are rich, the final two │ │ │ modules feel compressed, offering limited space for deeper exploration—particularly │ │ │ in integrating mindfulness into long‑term habit formation. │ │ │ 2. **Pricing:** The program’s price point sits at the higher end of the market for a │ │ │ self‑guided course. A modest discount for early adopters would have made it feel │ │ │ more accessible. │ │ │ 3. **Technical Support:** Occasionally, the video streaming platform lagged on older │ │ │ devices, requiring a restart to resume playback. │ │ │ │ │ │ **Final Thoughts** │ │ │ Overall, Mindful Mastery Lifecycle delivers a solid, well‑structured experience │ │ │ tailored to adult learners seeking both mindfulness practice and performance │ │ │ enhancement. The strengths—clear progression, varied resources, and an engaging │ │ │ community—outweigh the minor drawbacks. For anyone in mid‑life looking to cultivate │ │ │ presence while advancing personal or professional goals, this program is a │ │ │ worthwhile investment, especially when approached with realistic expectations about │ │ │ pacing and cost. │ │ │ │ │ │ **Bottom Line:** A thoughtful, well‑designed course that merits its four‑star │ │ │ rating; with a few refinements, it could easily become a five‑star staple in the │ │ │ mindfulness‑training market. │ └─────────────────────┴──────────────────────────────────────────────────────────────────────────────────────┘
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Output
| product_category | product_subcategory | target_age_range | customer | number_of_stars | review_style | product_name | customer_review | |
|---|---|---|---|---|---|---|---|---|
| 0 | Books | Non-Fiction | 35-50 | {'uuid': '3b224ef2-d1f0-486a-842e-b48a6eebeb8b... | 4 | detailed | Mindful Mastery Lifecycle | **Product:** Mindful Mastery Lifecycle \n**Ra... |
| 1 | Clothing | Women's Clothing | 18-25 | {'uuid': '1c0cebfc-5b28-45f1-8bfe-f9874955ae33... | 4 | detailed | VibeCraft Lounge Set | I’m Erin from Lisaborough, Missouri, and I’m 4... |
📊 Analyze the generated data
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Data Designer automatically generates a basic statistical analysis of the generated data.
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This analysis is available via the
analysisproperty of generation result objects.
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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 │ 2 (100.0%) │ 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 │ 4.5 +/- 0.7 │ ├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤ │ customer_review │ string │ 2 (100.0%) │ 69.5 +/- 0.5 │ 519.5 +/- 224.2 │ └───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘ ╭────────────────────────────────────────────────── 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!
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Happy with your preview data?
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Use the
createmethod to submit larger Data Designer generation jobs.
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Output
[17:19:03] [INFO] OpenTelemetry metrics available at http://127.0.0.1:9464/metrics
[17:19:03] [INFO] 🎨 Creating Data Designer dataset
[17:19:03] [INFO] |-- 🔒 Jinja rendering engine: secure
[17:19:03] [INFO] ✅ Validation passed
[17:19:03] [INFO] ⛓️ Sorting column configs into a Directed Acyclic Graph
[17:19:03] [INFO] Skipping model health checks because DATA_DESIGNER_SKIP_MODEL_HEALTH_CHECKS=1
[17:19:03] [INFO] ⚡ Using async task-queue builder
[17:19:03] [INFO] 📝 llm-text model config for column 'product_name'
[17:19:03] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[17:19:03] [INFO] |-- model alias: 'nemotron-nano-v3'
[17:19:03] [INFO] |-- model provider: 'nvidia'
[17:19:03] [INFO] |-- inference parameters:
[17:19:03] [INFO] | |-- generation_type=chat-completion
[17:19:03] [INFO] | |-- max_parallel_requests=4
[17:19:03] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[17:19:03] [INFO] | |-- temperature=1.00
[17:19:03] [INFO] | |-- top_p=1.00
[17:19:03] [INFO] | |-- max_tokens=2048
[17:19:03] [INFO] 📝 llm-text model config for column 'customer_review'
[17:19:03] [INFO] |-- model: 'nvidia/nemotron-3-nano-30b-a3b'
[17:19:03] [INFO] |-- model alias: 'nemotron-nano-v3'
[17:19:03] [INFO] |-- model provider: 'nvidia'
[17:19:03] [INFO] |-- inference parameters:
[17:19:03] [INFO] | |-- generation_type=chat-completion
[17:19:03] [INFO] | |-- max_parallel_requests=4
[17:19:03] [INFO] | |-- extra_body={'chat_template_kwargs': {'enable_thinking': False}}[17:19:03] [INFO] | |-- temperature=1.00
[17:19:03] [INFO] | |-- top_p=1.00
[17:19:03] [INFO] | |-- max_tokens=2048
[17:19:03] [INFO] ⚡️ Async generation: 2 column(s) (column 'product_name', column 'customer_review'), 20 tasks across 1 row group(s)
[17:19:03] [INFO] 🚀 (1/1) Dispatching with 10 records
[17:19:03] [INFO] 🎲 (1/1) Preparing samplers to generate 10 records across 6 columns
[17:19:10] [INFO] 📊 Progress [6.5s]:
[17:19:10] [INFO] |-- 🌑 column 'product_name': 2/10 (20%) 0.3 rec/s
[17:19:10] [INFO] |-- 🌑 column 'customer_review': 1/10 (10%) 0.2 rec/s
[17:19:18] [INFO] 📊 Progress [15.1s]:
[17:19:18] [INFO] |-- 🌖 column 'product_name': 9/10 (90%) 0.6 rec/s
[17:19:18] [INFO] |-- 🌘 column 'customer_review': 4/10 (40%) 0.3 rec/s
[17:19:29] [INFO] 📊 Progress [25.6s]:
[17:19:29] [INFO] |-- 🌕 column 'product_name': 10/10 (100%) 0.4 rec/s
[17:19:29] [INFO] |-- 🌖 column 'customer_review': 9/10 (90%) 0.4 rec/s
[17:19:33] [INFO] 📊 Progress [29.2s]:
[17:19:33] [INFO] |-- 🌕 column 'product_name': 10/10 (100%) 0.3 rec/s
[17:19:33] [INFO] |-- 🌕 column 'customer_review': 10/10 (100%) 0.3 rec/s
[17:19:33] [INFO] ✅ Async generation complete [29.2s]: 20 ok, 0 failed across 2 column(s)
[17:19:33] [INFO] 📊 Model usage summary:
[17:19:33] [INFO] |-- model: nvidia/nemotron-3-nano-30b-a3b
[17:19:33] [INFO] |-- tokens: input=1813, output=3952, total=5765, tps=194
[17:19:33] [INFO] |-- requests: success=20, failed=0, total=20, rpm=40
[17:19:33] [INFO] 📐 Measuring dataset column statistics:
[17:19:33] [INFO] |-- 🎲 column: 'product_category'
[17:19:33] [INFO] |-- 🎲 column: 'product_subcategory'
[17:19:33] [INFO] |-- 🎲 column: 'target_age_range'
[17:19:33] [INFO] |-- 🎲 column: 'customer'
[17:19:33] [INFO] |-- 🎲 column: 'number_of_stars'
[17:19:33] [INFO] |-- 🎲 column: 'review_style'
[17:19:33] [INFO] |-- 📝 column: 'product_name'
[17:19:33] [INFO] |-- 📝 column: 'customer_review'
Python
Output
| product_category | product_subcategory | target_age_range | customer | number_of_stars | review_style | product_name | customer_review | |
|---|---|---|---|---|---|---|---|---|
| 0 | Clothing | Winter Coats | 18-25 | {'uuid': '8756324b-b0e3-4f8a-861c-38450b1d10ba... | 2 | rambling | Arctic Pulse Jacket | I bought the Arctic Pulse Jacket from JacketGr... |
| 1 | Clothing | Winter Coats | 25-35 | {'uuid': '82e1b2ca-5032-4646-ad0c-7003fbe6f330... | 3 | structured with bullet points | StormChaser Insulated Overcoat | **StormChaser Insulated Overcoat – 3 Stars** ... |
| 2 | Home Office | Chairs | 25-35 | {'uuid': '4a195f1d-3480-4c4f-8ed0-3d1723a13100... | 4 | brief | Zenith Swivel Chair | I'm 52 and bought the Zenith Swivel Chair from... |
| 3 | Books | Fiction | 35-50 | {'uuid': '081100a4-5df8-42c0-b90a-4d88050dbcd2... | 3 | brief | Midlife Myths: Unfolding Contemporary Fiction ... | 3 stars—I enjoyed the clever stories but felt ... |
| 4 | Electronics | Laptops | 50-65 | {'uuid': 'f343e886-4e86-4bd9-b9a7-8bc40afdc31c... | 4 | detailed | GoldenLite Ultrabook | I’m Brandi from South Pamelamouth, South Dakot... |
Python
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 │ 8 (80.0%) │ 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 │ 4 (40.0%) │ 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 +/- 0.7 │ 5.0 +/- 6.6 │ ├───────────────────────┼───────────────┼────────────────────────────┼───────────────────┼────────────────────────┤ │ customer_review │ string │ 10 (100.0%) │ 69.5 +/- 7.3 │ 244.5 +/- 355.0 │ └───────────────────────┴───────────────┴────────────────────────────┴───────────────────┴────────────────────────┘ ╭────────────────────────────────────────────────── 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. │ │ │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
⏭️ Next Steps
Now that you've seen the basics of Data Designer, check out the following notebooks to learn more about: