semvec/pypi-packages huggingface dataset:
tf-serving 0.1.6
Client for OCR predictions against TensorFlow Serving
Requires Python: >=3.10

# TensorFlow Serving Client

> Client for OCR predictions against TensorFlow Serving

```bash
pip install tf-serving
```

## Model

The served model must return the following schema:

```typescript
{
  preds: StringTensor[BATCH x TOP_PATHS],
  logprobs: FloatTensor[BATCH x TOP_PATHS]
}
```

## Usage

```python
from tf.serving import predict, encode64

with open('path/to/image.jpg', 'rb') as f:
  img = encode64(f.read())

await predict([img], host='http://localhost', port=8501, endpoint='/v1/models/ocr:predict')
# Right(value=[ImagePreds(preds=['Place', 'Piace', ...], logprobs=[-0.187645972, -2.03857613, ...])], tag='right')
```
tred 0.1.4
Dimensionality reduction techniques for order-3 tensors
Requires Python: >=3.8

# tred
Dimensionality reduction algorithms for high-dimensional time-series data. 

Built on top of the `scikit-learn` library, adhering to the familiar Python
machine-learning `fit`-`transform` interface. 

See [GitHub page](https://github.com/brendanlu/tred) for more information, 
examples, and relevant literature. The library is written in pure Python, with 
a focus on efficient implementations using underlying NumPy algorithms. 

Contact brendannlu5@gmail.com if you are interested in using this for your 
research, and want some support. 

Read the docs here: [https://brendanlu.github.io/tred/tred.html](https://brendanlu.github.io/tred/tred.html)
pqdata 0.1a1
Parquet-based serialization for scverse
Requires Python: >=3.10

[![PyPi version](https://img.shields.io/pypi/v/pqdata)](https://pypi.org/project/pqdata)

# pqdata

Experimental Parquet-based I/O for [scverse](https://scverse.org) data structures.

## Installation

```
pip install pqdata
# or
pip install git+https://github.com/gtca/pqdata
```

> [!WARNING]
> This package is experimental, and API can change between versions as well as the file structure.

## Motivation

TODO

## Features and integrations

TODO

### I/O

[Example notebook](/docs/examples/pqdata-serialization-intro.ipynb). I/O with pqdata works like this:

```py
from pqdata import write_anndata, write_mudata
write_anndata(adata, "pbmc3k_anndata.pqdata")
write_mudata(mdata, "pbmc5k_citeseq_mudata.pqdata")

from pqdata import read_anndata, read_mudata
adata = read_anndata("pbmc3k_anndata.pqdata")
mdata = read_mudata("pbmc5k_citeseq_mudata.pqdata")
```
mdtablegen 1.0.2
A Markdown Table Generator
Requires Python: >=3.7


# About mdtablegen

This is a Python module to generate Markdown tables. Specifically the
Markdown dialect that HL docgen (and derivatives) speak. A few existing
Python Markdown generator libraries don't exactly do this.

# Example Output

```
+---------------+----------+-----------+----------------------+----------------------------+
| Address       | Port     | Service   | Fingerprint          | Comments                   |
+===============+==========+===========+======================+============================+
| 172.22.150.88 | 443/tcp  | ssl|http  | Some httpd           | Here's a comment           |
+---------------+----------+-----------+----------------------+----------------------------+
| 172.22.150.88 | 8089/tcp | ssl|http  | Some httpd           |                            |
+---------------+----------+-----------+----------------------+----------------------------+
| 172.22.30.10  | 53/tcp   | domain    | (unknown banner: ... | This looks interesting     |
+---------------+----------+-----------+----------------------+----------------------------+
| 172.22.30.119 | 22/tcp   | ssh       | ProFTPD mod_sftp ... |                            |
```