semvec/pypi-packages huggingface dataset:
wfork-streamlit-profiler 1.6.0
(fork of) Runtime profiler for Streamlit, powered by pyinstrument
License: MIT
Requires Python: >=3.8

# wfork-streamlit-profiler &nbsp;🏄🏼

[![PyPi](https://img.shields.io/pypi/v/wfork-streamlit-profiler)](https://pypi.org/project/wfork-streamlit-profiler/) [![GitHub latest tag](https://img.shields.io/github/v/tag/wyattscarpenter/streamlit-profiler?label=GitHub&logo=github)](https://github.com/wyattscarpenter/streamlit-profiler/)

**Runtime profiler for Streamlit, powered by [pyinstrument](https://github.com/joerick/pyinstrument).**

*wfork-streamlit-profiler is a fork of streamlit-profiler 0.2.4. If there are any newer versions of the original streamlit-profiler, you should probably use those instead. See the original project's github for more: https://github.com/jrieke/streamlit-profiler*

streamlit-profiler is a [Streamlit component](https://streamlit.io/components) that
helps you find out which parts of your app are slow. It profiles the code via
[pyinstrument](https://github.com/joerick/pyinstrument) and shows the results right
within your Streamlit app.

---

<h3 align="center">
  ⏱️ <a href="https://share.streamlit.io/jrieke/streamlit-profiler/main/examples/basic.py">Live demo</a> ⏱️
</h3>

---

<p align="center">
    <a href="https://share.streamlit.io/jrieke/streamlit-profiler/main/examples/basic.py"><img src="images/demo.png" width=600></a>
</p>

## Installation

```bash
pip install wfork-streamlit-profiler
```

## Usage

```python3
import streamlit as st
from wfork_streamlit_profiler import Profiler

with Profiler():
    st.title("My app")
    # ... other code
```

Or:
```python3
import streamlit as st
from wfork_streamlit_profiler import Profiler

p = Profiler()
p.start()
# ... other code
p.stop()
```

Then start your app as usual: `streamlit run my_app.py`

The `Profiler` class is an extension of `pyinstrument.Profiler`, so you can use
[all of its functions](https://pyinstrument.readthedocs.io/en/latest/reference.html#pyinstrument.Profiler).

Don't want the profiler to immediately display when you stop profiling? Initialize it with `Profiler(auto_output_on_stop=False)` instead, and call the `.output_streamlit()` method whenever you want it to display, instead. You can also set the async_mode of the profiler in the pyinstrument in the same constructor, whatever that means.
scor 0.2.2
The library for ICANN 2025 Paper 'Stochastic Covariance Regularization for Imbalanced Datasets'
License: MIT
Requires Python: >=3.10

# Stochastic Covariance Regularization for Imbalanced Datasets - ICANN 2025 

The repository of the paper "Stochastic Covariance Regularization for Imbalanced Datasets",
accepted into [ICANN 2025](https://e-nns.org/icann2025/). Conference papers are not yet
published. When published, a link to the Springer page of the paper will be provided here.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[PyPi page](https://pypi.org/project/scor/)

[Github page](https://github.com/ahmeterdem1/scor)

_pip install scor_

## "scor" package

The "scor" package is organized as a library that could easily be imported
and used in applications. It includes all utility functions, measurement
functions, model classes and data loaders used in the paper, organized
into modules.

### scor.data

This module includes data utilities and data loader functions for datasets
that were used in the paper. Data loading functions for MNIST, CIFAR-10,
Flower102, Oxford IIIT Pets, DTD, STL-10 and LFW People are given. For
LFW People, you need to [download](https://www.kaggle.com/datasets/jessicali9530/lfw-dataset) 
it then extract it first, because the link that PyTorch provides within its own data loader
is broken.

### scor.losses

This module includes the SCoR loss function alongside other that were used in the paper.

### scor.metrics

This module has utility functions that calculate ranks and singular values of weight matrices
of models, that were interpreted as indicators of matrix perturbations in the paper.

### scor.models

This module hosts model classes used in tests. It does not include ResNet32 and ResNet50 classes,
because respective tests in the paper were adopted from other sources. Several options for ConvNeXt
is supported, but in the paper only ConvNeXt-Tiny is used.

### scor.training

This module hosts several training functions for several tests in the paper. These functions cover
first phase of experiments in the paper, where MNIST and CIFAR-10 is artificially imbalanced by
imbalancing a random class, or 9 random classes multiplicatively by a reduction factor.

## Examples

Example notebooks that use the "scor" package for training on given datasets are provided under
the "examples" directory.

## imagenet.py

This file is for training models with various loss functions on the ImageNet dataset.
Code from [here](https://github.com/pytorch/examples/tree/main/imagenet) is adapted 
to be used with loss functions defined in this repository.

A "loss" option is included to the adapted code. Run ```python imagenet.py --help```
for a proper description.
endoflife-lib 0.1.2
A client for fetching end-of-life and product data from endoflife.date
License: MIT
Requires Python: >=3.7

# endoflife-lib

A lightweight Python library for retrieving release and product end-of-life data from [endoflife.date](https://endoflife.date).

---

## Features

- Fetch product release data asynchronously using `aiohttp`
- Easy integration with Home Assistant or other async Python projects
- Custom exceptions for better error handling

---

## Installation

Install the latest release from PyPI:

```bash
pip install endoflife-lib