semvec/pypi-packages huggingface dataset:
vastai-sdk 0.5.0
SDK for Vast.ai GPU Cloud Service
Requires Python: >=3.9

# Vast.ai Python SDK
[![PyPI version](https://badge.fury.io/py/vastai-sdk.svg)](https://badge.fury.io/py/vastai-sdk)

The official Vast.ai SDK pip package.

## Install
```bash
pip install vastai-sdk
```
## Examples

NOTE: Ensure your Vast.ai API key is set in your working environment as `VAST_API_KEY`. Alternatively, you may pass the API key in as a parameter to either client.

### Using the VastAI CLI client

1. Create the client
```python
from vastai import VastAI
vastai = VastAI() # or, VastAI("YOUR_API_KEY")
```
2. Run commands
```python
vastai.search_offers()
```
3. Get help
```python
help(v.create_instances)
```

### Using the Serverless client

1. Create the client
```python
from vastai import Serverless
serverless = Serverless() # or, Serverless("YOUR_API_KEY")
```
2. Get an endpoint
```python
endpoint = await serverless.get_endpoint("my-endpoint")
```
3. Make a request
```python
request_body = {
    "model": "Qwen/Qwen3-8B",
    "prompt" : "Who are you?",
    "max_tokens" : 100,
    "temperature" : 0.7
}
response = await serverless.request("/v1/completions", request_body)
```
4. Read the response
```python
text = response["response"]["choices"][0]["text"]
print(text)
```

Find more examples in the `examples` directory
tvminer 0.22.1
Thin wrapper around inference: load_model and predict_batch for batch frame results.
Requires Python: >=3.10
Keywords: inference, miner, predict_batch

# tvminer

Thin PyPI package that exposes `load_model` and `predict_batch` by delegating to the bundled `inference` extension (`.so`). The wheel includes the `.so`, so it works wherever it is installed (local, venv, Chutes, PyPI).

## Build (include .so in wheel)

From the **repository root** (parent of `pypi_package/`):

```bash
./pypi_package/build_with_so.sh
```

This copies `inference.cpython-*.so` from the repo root into `pypi_package/src/tvminer/`, then runs `python -m build`. The resulting wheel in `pypi_package/dist/` contains the `.so`; any `pip install` of that wheel will have the extension available.

You must build the `.so` first (e.g. `python setup_turbo_v2_py.py build_ext --inplace` in the repo root).

## Install

```bash
pip install /path/to/pypi_package/dist/tvminer-*.whl
# or from PyPI after publishing
pip install tvminer
```

## Publish to PyPI

After building with `build_with_so.sh`:

```bash
pip install twine
twine upload pypi_package/dist/*
```

## Usage

```python
from pathlib import Path
from tvminer import load_model, Miner, TVFrameResult, BoundingBox

miner = load_model(Path("/path/to/hf_repo"))
# or
miner = Miner(Path("/path/to/hf_repo"))

results = miner.predict_batch(batch_images, offset=0, n_keypoints=32)
```

`predict_batch` runs `self.inference.predict_batch(batch_images, offset, n_keypoints)` internally.
ismember 1.2.0
Python package ismember returns array elements that are members of set array.
Requires Python: >=3
Keywords: Python, ismember, set membership, numpy, array, set, utilities

# ismember

[![Python](https://img.shields.io/pypi/pyversions/ismember)](https://img.shields.io/pypi/pyversions/ismember)
[![PyPI Version](https://img.shields.io/pypi/v/ismember)](https://pypi.org/project/ismember/)
![GitHub Repo stars](https://img.shields.io/github/stars/erdogant/ismember)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/erdogant/ismember/blob/master/LICENSE)
[![Downloads](https://pepy.tech/badge/ismember)](https://pepy.tech/project/ismember)
[![Downloads](https://pepy.tech/badge/ismember/month)](https://pepy.tech/project/ismember/)
[![Sphinx](https://img.shields.io/badge/Sphinx-Docs-Green)](https://erdogant.github.io/ismember/)
<!---[![BuyMeCoffee](https://img.shields.io/badge/buymea-coffee-yellow.svg)](https://www.buymeacoffee.com/erdogant)-->
<!---[![Coffee](https://img.shields.io/badge/coffee-black-grey.svg)](https://erdogant.github.io/donate/?currency=USD&amount=5)-->

``ismember`` is a Python library that checks whether the elements in X is present in Y. 


# 
**⭐️ Star this repo if you like it ⭐️**
# 


### [Documentation pages](https://erdogant.github.io/ismember/)

On the [documentation pages](https://erdogant.github.io/ismember/) you can find more information about ``ismember`` with examples. 

# 

##### Install ismember from PyPI
```bash
pip install ismember     # normal install
pip install -U ismember  # update if needed
```


### Import ismember package
```python
from ismember import ismember
```
<hr>

#### Quick example

Use the documentation pages for more detailed usage. Some of the most used functionalities are linked below.

```python

from ismember import ismember

# Example with lists
I, idx = ismember([1,2,3,None], [4,1,2])
I, idx = ismember(["1","2","3"], ["4","1","2"])
```


#

#### [Example: Check whether the elements of X are present in Y](https://erdogant.github.io/ismember/pages/html/Examples.html#)

#

#### [Example: Determine the corresponding location of the values that are present in Y array](https://erdogant.github.io/ismember/pages/html/Examples.html#determine-the-corresponding-location-of-the-values-that-are-present-in-y-array)

#

#### [Example: Row wise comparison](https://erdogant.github.io/ismember/pages/html/Examples.html#row-wise-comparison-1)

#

#### [Example: Elementwise comparison](https://erdogant.github.io/ismember/pages/html/Examples.html#elementwise-comparison)

<hr> 

#### ☕ Support

If you find this project useful, consider supporting me:

<a href="https://www.buymeacoffee.com/erdogant">
  <img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=erdogant&button_colour=FFDD00&font_colour=000000&font_family=Cookie&outline_colour=000000&coffee_colour=ffffff" />
</a>