semvec/pypi-packages huggingface dataset:
torch-utilities 1.2.4
Simplifying audio and deep learning with PyTorch.
Requires Python: >=3.10

# Torch Utilities

**Torch Utilities** is a collection of tools and utilities for working with PyTorch in the audio domain.

## Disclaimer

**Torch Utilities** is developed as a personal set of tools and is provided as-it-is, without any warranties or guarantees. The API and utilities may change in the future as the module continues to evolve. While effort is made to maintain compatibility with previous versions, users are advised to carefully consider the stability of the API before using **Torch Utilities** in production environments.

## Installation

You can install **Torch Utilities** using pip.

```bash
pip install torch_utilities
```

## Running The Tests

To run the tests you need to clone the repository locally and run use `pytest`.

```bash
git clone git@github.com:FedericoDiMarzo/torch_utilities.git
pip install -e torch_utilities[dev]
pytest torch_utilities/tests
```

If any tests fail, it may indicate that there is a bug in the code or that some aspect of the API has changed. In such cases, we encourage you to open an issue on the repository so that we can help resolve the problem.

## Module Documentation

To read the API documentation of the module after cloning the repository, you can use `pdoc` to generate the documentation and serve it locally.

```bash
pdoc --docformat numpy torch_utilities/torch_utilities
```

The documentation will then be accessible at the address http://localhost:8080 .

## How to explore the tools

The most relevant function and classes are available in the main namespace of the module. You can import them directly and use them in your code.

The source code is divided into

- `audio`: Utilities working mostly on waveforms
- `augmentation`: Data augmentation for audio signals
- `io`: Input/output utilities
- `metrics`: Various metrics for audio signals
- `modules`: **PyTorch modules for audio processing**
- `utilities`: General utilities.
pfio 2.9.0
PFN IO library
Keywords: filesystem hdfs chainer development

## PFIO

![Doc Status Badge](https://readthedocs.org/projects/pfio/badge/?version=master&style=flat)

PFIO is an IO abstraction library developed by PFN, optimized for deep
learning training with batteries included. It supports

- Filesystem API abstraction with unified error semantics,
- Explicit user-land caching system,
- IO performance tracing and metrics stats, and
- Fileset container utilities to save metadata.


## Dependency

- HDFS client and libhdfs for HDFS access
- CPython >= 3.9

## Installation and Document build

Installation

```shell
$ git clone https://github.com/pfnet/pfio.git
$ cd pfio
$ pip install .
```

Documentation
```sh
$ cd pfio/docs
$ make html
$ open build/html/index.html
```

## How to use

Please refer to the [official document](https://pfio.readthedocs.io) for more information about the usage.

## Release

Check [the official document](https://packaging.python.org/tutorials/packaging-projects/) for latest release procedure.

Run tests locally:

```sh
$ pip install tox
$ tox
```

Bump version numbers in `pfio/version.py` .

Push and open a pull request to invoke CI. Once CI passed and the pull request merged,
tag a release:

```sh
$ git tag -s X.Y.Z
$ git push --tags
```

Build:

```sh
$ rm -rf dist
$ pip3 install --user build
$ python3 -m build
```

Release to PyPI:

```sh
$ python3 -m pip install --user --upgrade twine
$ python3 -m twine upload --repository testpypi dist/*
```
get-processor-codename 0.0.2
Easily get an intel processor's code name in Python.
Requires Python: >=3

# get-processor-codename
Easily get an intel processor's code name in Python

## Installation
Open your terminal and type
```bash
pip install get-processor-codename
```

Or
```bash
pip install get-processor-codename --break-system-packages
```
if you are using an externally managed python environement on linux.
easyarg 0.2.0
Help your python code have parameters and arguments

# easyarg


Create a parameter

```python
from easyarg import AGR

parser=AGR()
# create two parameters --say and --cry
parser.add_agr('--say')
parser.add_agr('--cry')
# read all parameters and arguments, this is a required function
parser.parse_agrs()
# return argument of --say and --cry
say_data=parser.get_value('--say')
cry_data=parser.get_value('--cry')
print(say_data)
print(cry_data)
# Run it on terminal like this
# python yourfile.py --say "hello this is my test" --cry huhu
# output: hello this is my test
#         huhu
```
If you want a parameter to have a default argument,this default value can be changed.
```python
from easyarg import AGR

parser=AGR()
parser.add_agr('--say',default='hello world')
parser.parse_agrs()
say_data=parser.get_value('--say')
print(say_data)
# this is default:
# output: hello world
# change value:
# python yourfile.py --say "hello this is my test"
# output: hello this is my test
```
If you just want to add parameters without arguments
```python
from easyarg import AGR
parser=AGR()
parser.add_agr('--help',action='store_true')
parser.parse_agrs()
help_data=parser.get_value('--help')
if help_data:
    print('User entered help')
# python yourfile.py --help
# output: User entered help
```
Thanks from Phan Huynh Thien Phu