semvec/pypi-packages huggingface dataset:
dsd-vps 0.1.0
A plugin for django-simple-deploy, supporting deployments to VPS providers.
Requires Python: >=3.9
Keywords: django, deployment

# dsd-vps

A plugin for deploying Django projects to any VPS provider, using django-simple-deploy.

Quick Start
---

To deploy your project to a VPS, you'll need to ...

## Prerequisites

Deployment to a VPS requires the following:

- You must be using Git to track your project.
- You need to be tracking your dependencies with a `requirements.txt` file, or be using Poetry or Pipenv.
- You'll need...

## Configuration-only deployment (NOT SUPPORTED YET)

First, install `dsd-vps` and add `django_simple_deploy` to `INSTALLED_APPS` in *settings.py*:

```sh
$ pip install dsd-vps
# Add "django_simple_deploy" to INSTALLED_APPS in settings.py.
$ git commit -am "Added django_simple_deploy to INSTALLED_APPS."
```

When you install `dsd-vps`, it will install `django-simple-deploy` as a dependency.

Now run the `deploy` command:

```sh
$ python manage.py deploy
```

This is the `deploy` command from `django-simple-deploy`, which makes all the changes you need to run your project on a VPS.

## Automated deployment

This is experimental, and you should review the codebase before running this early version on your system. It will modify local files outside of your project, such as `~/.ssh/config` and `~/.ssh/id_rsa_git`.

- Create a new VPS instance.
    - I'm Using Ubuntu 24.04 on Digital Ocean for development work; any debian-based OS on any VPS provider should work.
    - Choose SSH username/password login approach.
- Set two env vars:
    - `$ export DSD_HOST_IPADDR=<droplet-ip-address>`
    - `$ export DSD_HOST_PW=<droplet-pw>`
- Install `dsd-vps`.
    - This will be changed to `dsd-vps` shortly, as it should work for all VPS hosting platforms.
    - This plugin is not yet available on PyPI; I'm currently using an editable install of the repo.
- Add `django_simple_deploy` to `INSTALLED_APPS`.
- Run `python manage.py deploy --automate-all`.

The `deploy` command will add a new user named `django_user` to the droplet, with the same password you originally chose. It will update and configure the server, configure Git on the server, configure the project to be served from the droplet, commit changes, push the project, and open the remote project in a new browser tab.

It will add a local ssh key pair for Git, modifying `~/.ssh/config`. The key will be stored at `~/.ssh/id_rsa_git`.

The project will be served over http, which means the browser will probably flag it as insecure.
wakeword-detector 0.0.44
A Python package for wakeword detection
Requires Python: >=3.8
Keywords: wakeword, speech recognition, AI, machine learning

# Wakeword Detector

A Python package for training and running real-time wakeword detection using PyTorch and TensorFlow, with GPU acceleration support.

---

## 🚀 Features

- Real-time wakeword detection with microphone input
- Torch-based inference with optional TensorFlow model support
- CLI tools for recording audio and training models
- Supports GPU acceleration (CUDA 12.4+)

---

## 🧱 Requirements

- Python 3.8 – 3.12
- Linux (recommended for PyAudio + GPU)
- NVIDIA GPU with CUDA support (for training/inference speedup)

---

## 📦 Installation

Make sure you are not mixing environments:

python3 -m venv testenv
source testenv/bin/activate

```bash
# Use TestPyPI if installing dev builds:
pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple wakeword-detector

⚡ Enabling GPU Acceleration
If you want to use GPU-based training/inference (recommended):

1. Install the CUDA 12.4 runtime:
bash
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Edit
# Download keyring (Ubuntu 22.04 shown here)
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update

# Install CUDA runtime (no full toolkit needed)
sudo apt-get install cuda-runtime-12-4
2. Set CUDA path (if needed)
bash
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export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
You can add this line to your ~/.bashrc or ~/.zshrc for persistence.

3. Verify PyTorch sees your GPU:
bash
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python -c "import torch; print(torch.cuda.is_available())"
```

## 📮 Feedback & Issues

Submit here, but please note that this package is still under development, when version reaches 0.1.0 version is stable enough for prerelease. ETA: Early April 2025
https://github.com/larawhybrow/wakeword-detector/issues
projeto-pacotes-em-py 0.0.1
Image Processing Package using Skimage
Requires Python: >=3.5

# package_name

Description. 
The package package_name is used to:
	Processing:
		- Difference  
		- Histogram
		- Resize
	Utils
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Author
Pablo

## License
[MIT](https://choosealicense.com/licenses/mit/)