semvec/pypi-packages huggingface dataset:
MomSPS 1.0.0
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
Requires Python: >=3.8.0

# MomSPS
This is the official repository for the code used to run the experiments in the paper that proposed the MomSPS optimizer. The optimizer is implemented in PyTorch. 


**Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance**\
*D. Oikonomou, N. Loizou*


## Installation

You can install the package with

```
pip install momsps
```


## Usage

Import the optimizers in Python with

``` python
from momsps import MomSPS
opt = MomSPS(model.parameters(), lr=1)
```
or

``` python
from momsps import MomSPS_smooth
opt = MomSPS_smooth(model.parameters(), lr=1)
```

**Important: you only need to adapt one line of your training script, described below.** 
MomSPS needs access to the value of the (mini-batch) loss in the ``.step()`` method. The easiest way to do this is

``` python
loss = YOUR_LOSS_FUNCTION()
loss.backward()
opt.step(loss=loss)           # the line you need to change
```


## Citation

If you use MomSPS, please cite

```
@article{Oikonomou2024,
  author        = {Dimitris Oikonomou and Nicolas Loizou},
  title         = {Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance},
  year          = {2024},
}
```
py-watersmart 0.1.5
Obtain water usage data from Watersmart.com
Requires Python: >=3.11
Keywords: water meter

This library supplies a mechanism to download water meter usage data for homes
serviced by a water company that uses `<town_name>.watersmart.com` URLs 
for such data.

It supplies a simple `watersmart-cli` CLI that prints all hourly logs, but
is generally expected to be used from another tool, like Home Assistant.

## Developing

```shell
python -m venv .venv
. .venv/bin/activate
pip install -e .[test]
python -m pytest .
```
bulk-directory-tree 1.0.8
Create n node d depth bulk directory tree in any directory
Requires Python: !=3.0.*,!=3.1.*,!=3.2.*,<4,>=2.6


# Bulk Directory Tree  
[![Build Status](https://travis-ci.org/saumiko/bulk-directory-tree.svg?branch=master)](https://travis-ci.org/saumiko/bulk-directory-tree)
[![Requirements Status](https://requires.io/github/saumiko/bulk-directory-tree/requirements.svg?branch=master)](https://requires.io/github/saumiko/bulk-directory-tree/requirements/?branch=master)
[![PyPI](https://img.shields.io/pypi/v/bulk-directory-tree.svg)](https://pypi.org/project/bulk-directory-tree)
[![star this repo](https://img.shields.io/github/stars/saumiko/bulk-directory-tree)](https://github.com/saumiko/bulk-directory-tree) 
[![fork this repo](https://img.shields.io/github/forks/saumiko/bulk-directory-tree)](https://github.com/saumiko/bulk-directory-tree/fork)  
  
Creates `n` number of directories for `d` depth at any directory.  
  
- [Installation](#installation)  
- [Description](#description)  
- [Options](#options)  
- [Defaults](#defaults)  
- [Example](#example)  
  
## Installation  
You can use pip to install this command line tool:  
  
```sudo -H pip install --upgrade bulk-directory-tree```  
  
## Description  
`bulk-directory-tree` is a command-line program to create `n` number of directories for `d` depth at given directory. It requires the Python interpreter, version 2.6, 2.7, or 3.2+, and it is not platform specific. It should work on your Unix box, on Windows or on macOS. It is released to the public domain, which means you can modify it, redistribute it or use it however you like.  
  
```  
bulk-directory-tree [OPTIONS]  
```  
## Options  
```  
-p, --path TEXT Bulk Directory Creation Path  
-n, --num INTEGER Number of folders to create in each directory  
-d, --depth INTEGER Directory tree depth  
-l, --length INTEGER  Directory name length  
--help  Show this message and exit.  
```  
  
## Defaults  
```  
-p, --path        YOUR_CURRENT_DIRECTORY  
-n, --num     1  
-d, --depth       1  
-l, --length   5  
```  
  
## Example  
`$ bulk-directory-tree -p '$YOUR_DIRECTORY' -n 3 -d 3 -l 4`  
  
Running this command will create a directory tree like shown below in `YOUR_DIRECTORY`  
  
```  
YOUR_DIRECTORY
├── 0c6c
│   ├── 1036
│   │   ├── 65b3
│   │   ├── 79c2
│   │   └── 7dca
│   ├── 575d
│   │   ├── 3097
│   │   ├── 6526
│   │   └── 8567
│   └── fc27
│       ├── 50b8
│       ├── 53ad
│       └── 650e
├── 5d16
│   ├── 8880
│   │   ├── 12c7
│   │   ├── 63d4
│   │   └── 701a
│   ├── b846
│   │   ├── 6f2c
│   │   ├── 761b
│   │   └── 8ebd
│   └── ba2d
│       ├── 75da
│       ├── 8405
│       └── ccb1
└── fc02
    ├── 53b8
    │   ├── 06d8
    │   ├── 73d0
    │   └── 9bf7
    ├── 8406
    │   ├── 37d9
    │   ├── 6954
    │   └── d3d4
    └── ec6f
        ├── 7150
        ├── 9d9f
        └── b846
```
faccent 1.0.3
A pytorch tool for feature visualizations of various types
Requires Python: >=3.8

# faccent
feature accentuation: image-seeded feature visualizations


## Setup

### with PIP

pip install faccent

### or with github

clone repository

Install dependencies: `pip install -r requirements.txt`

Add the faccent package itself: `pip install -e .`

Make the virtual environment available to jupyter notebooks:  `python -m ipykernel install --user --name=faccent`


## Tutorials

See the API_demo notebook, or
<a href="https://colab.research.google.com/github/chrishamblin7/faccent/blob/master/API_demo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>


## License and Disclaimer

You may use this software under the MIT License. See [LICENSE](https://github.com/greentfrapp/lucent/blob/master/LICENSE).