Metadata-Version: 2.4
Name: buildai-data
Version: 0.2.4
Summary: The standard API for egocentric data. $1/hr.
Project-URL: Homepage, https://build.ai
Project-URL: Documentation, https://build.ai/docs
Project-URL: Repository, https://github.com/build-ai-ego/buildai-sdk
License: MIT
Keywords: data,egocentric,imu,robotics,video
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: httpx>=0.27
Requires-Dist: numpy>=1.24
Requires-Dist: rich>=13.0
Provides-Extra: dev
Requires-Dist: pytest>=8; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Provides-Extra: torch
Requires-Dist: decord>=0.6; extra == 'torch'
Requires-Dist: torch>=2.1; extra == 'torch'
Requires-Dist: webdataset>=0.2.86; extra == 'torch'
Description-Content-Type: text/markdown

# BuildAI

The standard API for egocentric data. `$1/hr`.

## Quickstart

```bash
pip install buildai-data
```

```python
from buildai import BuildAI

client = BuildAI(api_key="build_data_xxxx")
client.download(hours=10, output_dir="./data")
```

That's it. Ten shards download to `./data/`. Each shard is one hour of egocentric video with frame-synced IMU data.

## What you get

Each shard is a tar file containing 20 three-minute clips:

```
shard-000001.tar
  ├── 000001.mp4       # H.265, 1080p, 30fps, no audio
  ├── 000001.imu.npy   # (5400, 6) float32, frame-synced
  ├── 000002.mp4
  ├── 000002.imu.npy
  └── ...              # 20 clips total
```

**Video**: H.265, 1080p, 30fps, no audio. Three minutes per clip.

**IMU**: NumPy array, shape `(5400, 6)`, float32. Columns: `[acc_x, acc_y, acc_z, gyro_x, gyro_y, gyro_z]`. Row N = frame N (30 fps x 180 seconds = 5400 frames). Units: accelerometer in m/s2, gyroscope in rad/s.

**Shard size**: ~2 GB per shard, ~1 hour of footage, 20 clips.

Shards are standard [WebDataset](https://github.com/webdataset/webdataset) format. If you already have a WebDataset pipeline, point it at the downloaded tars directly.

## Download

```python
from buildai import BuildAI

client = BuildAI(api_key="build_data_xxxx")

# Download 100 hours of data
client.download(hours=100, output_dir="/data/buildai/")

# Download more - you'll get new shards you haven't seen before
client.download(hours=1000, output_dir="/data/buildai/")
```

Every call gets new shards. You never receive the same shard twice. Shards are downloaded sequentially. Every researcher gets the same sequence.

Downloads resume on failure. If a shard already exists locally and the checksum matches, it's skipped.

| Parameter | Default | Description |
|-----------|---------|-------------|
| `hours` | required | Number of hours to download (1 shard = 1 hour) |
| `output_dir` | `./buildai-data` | Where to save shard tars |
| `workers` | 8 | Parallel download threads |
| `verify_checksum` | True | SHA-256 verification after download |

## Visualize

```python
from buildai import BuildAI

BuildAI.visualize(source="/data/buildai/")
```

Opens a local web viewer in your browser. Shows a grid of all clips across your downloaded shards. Click any clip to watch the video with accelerometer and gyroscope data graphed below, time-synced.

No data is uploaded. Everything runs locally. Press Ctrl+C to stop.

No API key needed. `visualize` is a static method that works on any directory of shard tars.

## Pricing

$1 per hour. Billed monthly. No minimum, no commitment.

## API key

Get your API key at [build.ai](https://build.ai). You can also set it via environment variable:

```bash
export BUILDAI_API_KEY=build_data_xxxx
```

```python
# Picks up BUILDAI_API_KEY automatically
client = BuildAI()
```

## Account

```python
info = client.account
print(info["total_hours"])
print(info["total_spent_usd"])
print(info["shards_remaining"])
```

## Requirements

- Python 3.10+
- httpx (HTTP client)
- numpy (IMU data)
