semvec/pypi-packages huggingface dataset:
dicordnum 1.4.1
Dictionary ordinal number notation package
Requires Python: >=3.6

# dictionary-ordinal-number-notation

Commentary by author:  

📖 [辞書順記数法 (Dictionary Ordinal Number Notation)](https://crieit.net/posts/Dictionary-Ordinal-Number-Notation)  

# Install

```shell
pip install dicordnum
```

# Explanation

```plaintext
1
2
3
4
5
6
7
8
9
...
```

```plaintext
1
10
2
3
4
5
6
7
8
9
```

Ouch!  

Let's solve.  

```plaintext
1
2
3
4
5
6
7
8
9
...
```

```plaintext
1
2
3
4
5
6
7
8
9
A10
```

?!  

```plaintext
A91
A92
A93
A94
A95
A96
A97
A98
A99
...
```

```plaintext
A91
A92
A93
A94
A95
A96
A97
A98
A99
AA100
```

It's ok.  

👇 For snake_case

```plaintext
7
a77
aa777
aaa7777
```
pytandem 1.0.1
A multi-threaded event manager package for triggering methods in tandem groups
Requires Python: >=3.6

# PyTandem
> A multi-threaded event manager package for triggering methods in
> tandem groups.

## Installation

### Requirements
```
- python>=3.6
```

### Install using PIP
```
python3 -m pip install --upgrade pytandem
```

## Usage

First define the method(s) you would like to attach to the manager.
```
def print_num(num):
  print(num)
```

Then attach the method using the provided decorator - this will take an
event name as a paramaneter. It should look like so:
```
from pytandem import pytandem

@pytandem.attach('event-name-here')
def print_num(num):
  print(num)
```

> Whenever the above method (`print_num()`) is called, instead of being fired
> immediately it will be added to the manager threads to be started later on.

Next we can fire the method a few times:
```
for i in range(0, 3):
  print_num(i)
```

> You'll notice nothing gets printed yet.

Now we can initiate the concurrent trigger of the 3 instances of the print_num()
method we added to the threads earlier using the `trigger()` method - this, again,
takes the event name as a parameter.
```
pytandem.trigger('event-name-here')
```

We can run the methods synchronously also by passing `False` in the `threaded` argument.
This is set to True by default.
```
pytandem.trigger('event-name-here', threaded=False)
```

And lastly, we can also set the maximum number of concurrent threads that pytandem
will use when triggering an event group. The default value for this is 4.
```
pytandem.trigger('event-name-here', max_threads=16)
```

> Point to note: if max_threads is set to 4, and there are 20 methods attached on the
> same event name, then only 4 will run concurrently until one pops off the queue,
> essentially allowing the next in line to run. Methods will be fired according to
> the order in which they were first attached.

> Note that a high `max_threads` value can cause out of memory issues if not careful.
distributed-mean-estimation 0.1a2
Algorithms for Distributed Mean Estimation (DME) in PyTorch
Requires Python: >=3.9

# distributed-mean-estimation

[![PyPI](https://img.shields.io/pypi/v/distributed-mean-estimation.svg)](https://pypi.org/project/distributed-mean-estimation/)
[![Changelog](https://img.shields.io/github/v/release/amitport/distributed-mean-estimation?include_prereleases&label=changelog)](https://github.com/amitport/distributed-mean-estimation/releases)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/amitport/distributed-mean-estimation/blob/main/LICENSE)

Algorithms for Distributed Mean Estimation (DME) in PyTorch.

## Installation

Install this library using `pip`:

    pip install distributed-mean-estimation

## Usage

Usage instructions go here.

## Development

To contribute to this library, first checkout the code. Then create a new virtual environment:

    cd distributed-mean-estimation
    python -m venv venv
    source venv/bin/activate

Now install the dependencies and test dependencies:

    pip install -e ".[test]"

To run the tests:

    pytest