semvec/pypi-packages huggingface dataset:
aioprogress 0.2.2.3
A high-performance, flexible async file downloader built with aiohttp.
Requires Python: >=3.7

# Async File Downloader Package

A high-performance, flexible async file downloader built with aiohttp.
Supports resume downloads, progress tracking, cancellation, proxy support, and concurrent downloads.

## Proxy Support:
1. HTTP/HTTPS proxies with authentication
1. SOCKS4/SOCKS5 proxies (requires aiohttp-socks)
1. Environment variable proxy configuration
1. Custom proxy headers

## Installation:
### Basic:
```
pip install aioprogress
```
### With SOCK5 Support:
```
pip install aioprogress aiohttp-socks
```
rendarr 0.2.1
Render a slice of a .npy array as an image.
Requires Python: >=3.6

# RendArr

Render a slice of a .npy array as an image.

Installation:

```sh
pip install rendarr
```

## Usage

```
rendarr <array.npy> [options]
```

This will open a window displaying the array as an image.
If an interactive axis was marked,
you can use the arrow keys to navigate along the interactive axis.

Run `rendarr --help` to see all options.
druta 0.0.2
druta (द्रुत) - A fast video dataset format for PyTorch (for when storage isn't a problem)

# druta (द्रुत)

A fast video dataset format for PyTorch (for when storage is cheap, but time is not)

```
pip install druta
```

```python
import druta

druta.prep_dataset(
    video="video.mp4",
    save_as="video.druta",
    num_threads=4,
)

dataset = druta.Dataset(
    filename="video.druta",
)

for i in range(len(dataset)):
    frame = dataset[i]
    ## (height, width, 3)
    print(f"Frame {i} shape: {frame.shape}")
```

## Why druta?
<p align="center">
    <img src="images/explainer.png" width="45%" style="display:inline-block;">
    <img src="images/decord_vs_druta_benchmark.png" width="45%" style="display:inline-block;">
</p>

When training a model on video data using something like decord, we end up performing the video decoding gymnastics thousands of times redundantly. Druta skips this redundancy by decoding the video once and storing it as a memory mapped file with raw `uint8` tensor data.

But there's no free lunch. The speedup comes at a cost of a massive disk-size, but this trade-off is well worth it for some folks. (The speed-tests were run on an M3 Max macbook pro on 2048 frames)

## Running tests

```
pytest -vvx --capture=no tests/
```