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---
viewer: false
tags: [uv-script]
---
# Atlas Export
Generate and deploy interactive embedding visualizations to HuggingFace Spaces with a single command using Apple's [Embedding Atlas](https://github.com/apple/embedding-atlas) library!
## Quick Start
```bash
# Create a Space from any text dataset
uv run atlas-export.py stanfordnlp/imdb --space-name my-imdb-viz
# Your Space will be live at:
# https://huggingface.co/spaces/YOUR_USERNAME/my-imdb-viz
```
**⚠️ Note:** Currently this approach is limited by storage capacity for files on Spaces so for big datasets consider using the `--sample` option to limit the number of points visualized!
## Examples
### Image Datasets
```bash
# Visualize image datasets with CLIP
uv run atlas-export.py \
beans \
--space-name bean-disease-atlas \
--image-column image \
--model openai/clip-vit-base-patch32
```
### Custom Embeddings
```bash
# Use a specific embedding model
uv run atlas-export.py \
wikipedia \
--space-name wiki-viz \
--model nomic-ai/nomic-embed-text-v1.5 \
--text-column text \
--sample 50000
```
### Pre-computed Embeddings
```bash
# If you already have embeddings in your dataset
uv run atlas-export.py \
my-dataset-with-embeddings \
--space-name my-viz \
--no-compute-embeddings \
--x-column umap_x \
--y-column umap_y
```
### GPU Acceleration (HF Jobs)
```bash
# First, get your HF token (if not already set)
python -c "from huggingface_hub import get_token; print(get_token())"
# Run on HF Jobs with GPU using experimental UV support
hf jobs uv run --flavor t4-small \
-s HF_TOKEN=your-token-here \
https://huggingface.co/datasets/uv-scripts/build-atlas/raw/main/atlas-export.py \
stanfordnlp/imdb \
--space-name imdb-viz \
--model sentence-transformers/all-mpnet-base-v2 \
--sample 10000
# With larger GPU and custom batch size for faster processing
hf jobs uv run --flavor a10g-large \
-s HF_TOKEN=your-token-here \
https://huggingface.co/datasets/uv-scripts/build-atlas/raw/main/atlas-export.py \
your-dataset \
--space-name your-atlas \
--batch-size 64 \
--sample 50000 \
--text-column output
```
Note: Replace `your-token-here` with your actual token. Available GPU flavors: `t4-small`, `t4-medium`, `l4x1`, `a10g-small`, `a10g-large`.
## Key Options
| Option | Description | Default |
| ---------------- | ----------------------------------- | ---------------------- |
| `dataset_id` | HuggingFace dataset to visualize | Required |
| `--space-name` | Name for your Space | Required |
| `--model` | Embedding model to use | Auto-selected |
| `--text-column` | Column containing text | "text" |
| `--image-column` | Column containing images | None |
| `--sample` | Number of samples to visualize | All |
| `--batch-size` | Batch size for embedding generation | 32 (text), 16 (images) |
| `--split` | Dataset split to use | "train" |
| `--local-only` | Generate locally without deploying | False |
| `--output-dir` | Local output directory | Temp dir |
| `--hf-token` | HuggingFace API token | From env/CLI |
Run without arguments to see all options and more examples.
## How It Works
1. Loads dataset from HuggingFace Hub
2. Generates embeddings (or uses pre-computed)
3. Creates static web app with embedded data
4. Deploys to HF Space
The resulting visualization runs entirely in the browser using WebGPU acceleration.
## Credits
Built on [Embedding Atlas](https://github.com/apple/embedding-atlas) by Apple. See the [documentation](https://apple.github.io/embedding-atlas/) for more details about the underlying technology.
---
Part of the [UV Scripts](https://huggingface.co/uv-scripts) collection 🚀
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