semvec/pypi-packages huggingface dataset:
i3-quickterm 1.2
A small drop-down terminal for i3 and sway
Requires Python: >=3.8
Keywords: i3 i3wm extensions add-ons

# i3-quickterm

[![Packaging status](https://repology.org/badge/vertical-allrepos/python:i3-quickterm.svg)](https://repology.org/project/python:i3-quickterm/versions)

A small drop-down terminal for [i3wm](https://i3wm.org/) and [sway](https://swaywm.org/)

## Features

* use your favourite terminal emulator
* can select a shell with [dmenu](http://tools.suckless.org/dmenu/) / [rofi](https://github.com/DaveDavenport/rofi)
* adapt to screen width
* multi-monitor aware

## Installation

Via pip:

```
pip install i3-quickterm
```

Or check the the repology badge above to check if it is packaged by your distribution.

## Usage

When launched, it will minimize the quickterm on the current screen if there is one.  Otherwise, it will either prompt the user for the shell to open or use the one supplied in argument.

If the requested shell is already opened on another screen, it will be moved on the current screen.

It is recommended to map it to an i3 binding:

```
# with prompt
bindsym $mod+p exec i3-quickterm
# always bring up standard shell, without the menu
bindsym $mod+b exec i3-quickterm shell
```

## Configuration

The configuration is read from `~/.config/i3-quickterm/config.json` or `~/.config/i3/i3-quickterm.json`.

* `menu`: the dmenu-compatible application used to select the shell
* `term`: the terminal emulator of choice
* `history`: a file to save the last-used shells order, last-used ordering is disabled if set to null
* `ratio`: the percentage of the screen height to use
* `pos`: where to pop the terminal (`top` or `bottom`)
* `shells`: registered shells (`{ name: command }`)

`term` can be either:
- the name of a terminal from the [supported list](#supported-terminals).
- `auto` to select the first existing terminal of the list above (only to provide friendler defaults, not recommended otherwise)
- a format string, like this one: `urxvt -t {title} -e {expanded}` with the correct arguments format of your terminal. Some terminals, like xfce4-terminal need the command argument to be passed as a string. In this case, replace `{expanded}` by `{string}`

`menu`, `term`, `history` and `shell` can contain placeholders for environment variables: `{$var}`.

Unspecified keys are inherited from the defaults:

```
{
    "menu": "rofi -dmenu -p 'quickterm: ' -no-custom -auto-select",
    "term": "auto",
    "history": "{$HOME}/.cache/i3-quickterm/shells.order",
    "ratio": 0.25,
    "pos": "top",
    "shells": {
        "js": "node",
        "python": "ipython3 --no-banner",
        "shell": "{$SHELL}"
    }
}
```

## Supported terminals

* alacritty
* foot
* gnome-terminal
* kitty
* roxterm
* st
* terminator
* termite
* urxvt
* urxvtc
* xfce4-terminal
* xterm

If you'd like to add another terminal (or correct an error), please open a pull request.

## Requirements

* python >= 3.8
* i3 >= v3.11 or sway >= 1.2
* [i3ipc-python](https://i3ipc-python.readthedocs.io/en/latest/) >= v2.0.1
* dmenu or rofi (optional)
torchversionist 0.0.1
A package for managing PyTorch model versions, metadata, and metrics.
Requires Python: >=3.10

# TorchVersionist

<p align="center">
  <img src="assets/logo.png" width="400" height="400" alt="TorchVersionist Logo">
</p>



TorchVersionist is a powerful Python library designed for PyTorch users to effortlessly manage model versions. It automates the tracking of model architectures, hyperparameters, and performance metrics, facilitating a smoother development and analysis process.

## Key Features

- **Automated Model Versioning**: Automatically assign and track versions for each of your PyTorch models.
- **Detailed Metadata Tracking**: Store and retrieve comprehensive metadata and hyperparameters for each model iteration.
- **Performance Metrics Logging**: Keep a record of model performance metrics for easy comparison and analysis.
- **Intuitive Model Retrieval**: Quickly access any version of your model with straightforward retrieval functions.

## Installation

```bash
pip install TorchVersionist
```

## Usage
Here's a quick start on how to use TorchVersionist:
```python
from torchversionist import ModelManager
# Initialize with your PyTorch model
model_manager = ModelManager(your_model, model_name="example_model")

# After training your model
model_manager.save(meta_data={'training_details': '...'}, metrics={'accuracy': 0.95})
```


## Contributing
Contributions to TorchVersionist are welcome! Open an issue or submit pull requests for any improvements or bug fixes.


## License
Licensed under the MIT License.