semvec/pypi-packages huggingface dataset:
wait4gpu-huyiwen 0.0.1
A simple utility to execute your deep learning scripts when there are enough idle gpus
Requires Python: >=3.6

# Wait4GPU

A simple utility to execute your deep learning scripts when there are enough idle gpus.

一个在有足够的空闲gpu时执行深度学习训练的小工具

## Install

```shell script
python setup.py install
```

## Usage

### As a command wrapper

```shell script
python3 -m Wait4GPU [--num-required NUM_REQUIRED] [--no-python] training_script
```
**Example:**
```shell script
# train `atss_r50_fpn_1x_coco.py` with 1 gpu using mmdetection
python3 -m Wait4GPU tools/train.py configs/atss/atss_r50_fpn_1x_coco.py

# train `atss_r50_fpn_1x_coco.py` with 2 gpus using mmdetection
python3 -m Wait4GPU --num-required 2 --no-python ./tools/dist_train.sh configs/atss/atss_r50_fpn_1x_coco.py 2
```

You may refer to `Wait4GPU/__main__.py` for more detailed usage.

### Integrate with your code

```python
from Wait4GPU.wait4gpu import wait_until_idle
import os

available = wait_until_idle(num_req=2)
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(available)
```
wait4gpu 0.0.1
A simple utility to execute your deep learning scripts when there are enough idle gpus
Requires Python: >=3.6

# Wait4GPU

A simple utility to execute your deep learning scripts when there are enough idle gpus.

一个在有足够的空闲gpu时执行深度学习训练的小工具

## Install

```shell script
python setup.py install
```

## Usage

### As a command wrapper

```shell script
python3 -m Wait4GPU [--num-required NUM_REQUIRED] [--no-python] training_script
```
**Example:**
```shell script
# train `atss_r50_fpn_1x_coco.py` with 1 gpu using mmdetection
python3 -m Wait4GPU tools/train.py configs/atss/atss_r50_fpn_1x_coco.py

# train `atss_r50_fpn_1x_coco.py` with 2 gpus using mmdetection
python3 -m Wait4GPU --num-required 2 --no-python ./tools/dist_train.sh configs/atss/atss_r50_fpn_1x_coco.py 2
```

You may refer to `Wait4GPU/__main__.py` for more detailed usage.

### Integrate with your code

```python
from Wait4GPU.wait4gpu import wait_until_idle
import os

available = wait_until_idle(num_req=2)
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(available)
```
synthetic-research 0.0.1
AIA and MIA attack versus synthetic and real data.
Requires Python: >=3.7

# Impact of using synthetic data on MIA and AIA

## API documentation
The [documentation](docs/build/html/index.html) is generated using [sphinx](https://www.sphinx-doc.org/).
To view it, clone the repo and use your web browser to open docs/build/html/index.html.
To regenerate the API documentation: 
```bash
sphinx-apidoc -f -F -o docs src;
sphinx-build -M html docs docs/build/
```


## Tests
Unit tests are setup in the test directory.
They use the python unittest packages.
To run all tests:
```bash
python -m unittest discover -v -p "*.py" -s test
```

## Installation
Instal with pip in a venv.
In the directory containing pyproject.toml :
```bash
 pip install --editable .
 ```

## Usage 
```python
#Load data
from synthetic.fetch_data import adult
from synthetic.fetch_data import utk
data_adult = adult.load()
data_utk = utk.load()

#Train a neural network an adult 
form synthetic.predictor.adult import AdultNN
adultNN = AdultNN()
adultNN.fit(data_adult["train"])

#Evalute trained neural network
adultNN.predict(data_adult["test"])
```

## Datasets
### Adult
We are using [folktables adult](https://github.com/socialfoundations/folktables).

### UTKFaces
From Kaggle: jangedoo/utkfaces-new.
For loading and parsing files we use [aia_fairness.dataset_processing](https://pypi.org/project/aia-fairness/)
PLCP 0.1
A simple encryption and decryption library

# PLC - Simple Encryption and Decryption Library

PLC is a lightweight Python library for performing simple encryption and decryption operations. It provides easy-to-use functions for converting text into binary strings for encryption and vice versa for decryption.

## Features

- Encrypt text into binary strings
- Decrypt binary strings back into text
- Simple and easy-to-use API

## Installation

You can install PLC using pip:

```bash
pip install PLC