semvec/pypi-packages huggingface dataset:
ezcolors 1.0.7
Easy colors and color math
License: MIT
Requires Python: >=3.6

A simple library that contains a ColorColour class for fast and easy definition of colours in python.  

you can define a colour from  

hex -- Colour(#ff00ff)  
RGB -- Colour(255, 0, 255)    
rgba -- Colour(255, 0, 255, 128)  
hsl -- Colour(hsl = [270, 100, 50])  
hsv -- Colour(hsv = [270, 100, 100])  
colour name -- Colour(Light Purple)  

with these colours you can then  

perform math upon it  
translate it to any color space  
compare it to other colours  
colour a string with ANSI escape sequences  
generate a colour palette with various different colour harmonies (see palettes module)  

This package was entirely authored on a Samsung A5 so please excuse any spelling errors etc and let me know if you find any issues
nimue 0.0.3
Database connection pooler for Python
License: MIT
Requires Python: >=3.3

Nimue is a database connection pool for Python. It aims to work with any thread-safe DBAPI 2.0 compliant SQL database driver. It is written in pure Python, and supports Python 3.3+. Connections in the pool are periodically checked for liveness by a background thread, and replaced if needed.
singular-launcher 1.0.6
Singular API For Launching Experiments Using Singularity On Slurm
License: MIT
Keywords: Deep Learning,Research,Management

# A Singular Experiment Launcher

Singular is a quality of life package that enables rapid deployment of code on a slurm cluster with singularity installed and password enabled login. Running experiments on your cluster is as simple as a single command in the terminal using singular. See below for an example and install instructions.

## Installation

Singular can be installed using the pip package.

```bash
pip install singular-launcher
```

## Usage

You may configure singular to remember the ssh credentials to your cluster using the following example.

```bash
singular set --ssh-username username --ssh-password password --ssh-host compute.example.com
```

Running your first command on your cluster is then as simple as one line in the terminal.

```bash
singular remote echo "my first command"
```

Certain workloads require uploading certain data files into the singularity image on the remote machine before running experiments. This can be done with the following command.

```bash
singular upload --recursive --exclude "*.pkl" ./local_dir remote_dir/in/image
```

Additionally, you can download files from the remote machine with a single command. The following command will download the results folder inside the remote singularity image to a location in the current local working directory. The remote path is always taken with respect to the singularity image path.

```bash
singular download --recursive --exclude "*.pkl" results ./
```

## Experimentation

A typical experiment creation pipeline involved working on code locally, testing the code locally, then running in on a server. This can be done easily using singular.

```bash
python do_my_experiment.py
```

Once your code is ready for deployment, tell singular how to install your code from github.

```bash
singular set --git-url https://github.com/username/repo
singular set --git-target /code/repo
singular set --install-command "pip install -e {git_target}"
```

Then, point singular to where your code working directory is stored locally.

```bash
singular set --sync-from /home/username/repo
```

Then test your code in a local copy of the singularity to make sure it works as expected.

```bash
singular local --sync python /code/repo/do_my_experiment.py
```

Then run it on the cluster.

```bash
singular remote --sync python /code/repo/do_my_experiment.py
```

In this example, the sync flag tells singular to copy code from your repository on the local disk to code folder in your remote singularity image. This is especially helpful when changes aren't committed.