semvec/pypi-packages huggingface dataset:
rhino-unicorn2 0.0.1
Defensive package registration.
License: MIT
Requires Python: >=3.6

# rhino-unicorn2

**NOTICE: Defensive Package Registration**

To provide internal services and prevent new enterprise users from accidentally installing the wrong package, this package is treated as a placeholder package and a friendly reminder is given during installation.

## Contact

For inquiries about this package, please contact the insiders.
rhino-keymouse-simulator 0.0.1
Defensive package registration.
License: MIT
Requires Python: >=3.6

# rhino-keymouse-simulator

**NOTICE: Defensive Package Registration**

To provide internal services and prevent new enterprise users from accidentally installing the wrong package, this package is treated as a placeholder package and a friendly reminder is given during installation.

## Contact

For inquiries about this package, please contact the insiders.
AioSnooper 1.0.0
Asynchronous PySnooper
License: MIT
Keywords: async PySnooper debug debugger logging

## AioSnooper
**AioSnooper** is the asynchronous version of **Pysnooper**, incorporating most of its functionality for easy **debugging** in async functions as an alternative to print statements.


## Installation
```console
$ pip install AioSnooper
```

## Example

Here is a base common func. While adding the `@snoop()` decorator:

```Python
import asyncio
from AioSnooper import snoop


@snoop(prefix="name")
async def print_name(name: str) -> str:
    await asyncio.sleep(2)    # io

    name = f"hello {name}"    # modify var

    return name


async def main():
    await print_name("Ling")

if __name__ == '__main__':
    asyncio.run(main())
```

The output is:
```
name  11:53:19     call    5 print_name()
name           var: name = 'Ling'
name  11:53:19     line    7 print_name() await asyncio.sleep(2)    # io
name  11:53:21     call    7 print_name()
name           var: name = 'Ling'
name  11:53:21     line    9 print_name() name = f"hello {name}"    # modify var
name  11:53:21     line   11 print_name() return name
name      modified: name = 'hello Ling'
name  11:53:21   return   11 print_name()
name         watch: return = 'hello Ling'
name  Elapsed time: 2001.539ms
```

Or if you don't want to trace an entire function, you can use `async with snoop_context()`:
```Python
import asyncio
from AioSnooper import snoop_context


async def print_name(name: str) -> str:
    await asyncio.sleep(2)    # io

    name = f"hello {name}"    # modify var | no trace

    async with snoop_context(prefix="name-context"):
        name += f"-context"   # modify var | trace

    return name


async def main():
    await print_name("Ling")

if __name__ == '__main__':
    asyncio.run(main())
```

The output is:
```
name-context 14:11:24     line   11 print_name() name += f"-context"   # modify var, will trace
name-context          var: name = 'hello Ling'
name-context 14:11:24     line   10 print_name() async with snoop_context(prefix="name-context"):
name-context     modified: name = 'hello Ling-context'
```

## More
Output stderr to file:
```Python
@snoop(output='debug.log')
```
See values of some expressions that aren't local variables:
```Python
@snoop(watch=('user.id',))
```
No color output:
```Python
@snoop(color=False)
```
rhino-cad-similarity 0.0.1
Defensive package registration.
License: MIT
Requires Python: >=3.6

# rhino-cad-similarity

**NOTICE: Defensive Package Registration**

To provide internal services and prevent new enterprise users from accidentally installing the wrong package, this package is treated as a placeholder package and a friendly reminder is given during installation.

## Contact

For inquiries about this package, please contact the insiders.
gradboard 8.0.0
Easily snowboard down gnarly loss gradients
License: MIT
Requires Python: >=3.8

# gradboard
![snowboarder](snowboarder.png "Image of a snowboarder")

Easily snowboard down gnarly loss gradients

## Getting started

You can install gradboard with

```
pip install gradboard
```

PyTorch is a peer dependency of `gradboard`, which means
  * You will need to make sure you have PyTorch installed in order to use `gradboard`
  * PyTorch will **not** be installed automatically when you install `gradboard`

We take this approach because PyTorch versioning is environment-specific and
    we don't know where you will want to use `gradboard`. If we automatically install
    PyTorch for you, there's a good chance we would get it wrong!

Therefore, please also make sure you install PyTorch.

## Usage examples

### Decent model training outcomes without tuning hyperparameters

`gradboard` includes

  * An implementation of AdamS as proposed in Xie et al. (2023) "On the Overlooked
        Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm
        Perspective" (https://openreview.net/pdf?id=vnGcubtzR1), which in practice
        makes model training more robust to the weight decay setting.
  * Utilities for implementing popular learning rate schedules
  * An implementation of an automatic max/min learning rate finder based on Smith
        (2017) "Cyclical Learning Rates for Training Neural Networks"
        (https://arxiv.org/abs/1506.01186)
  * Sensible defaults

In practice this means that you can train a neural network and get decent performance
    right out of the box, just by using the `PASS` (point-and-shoot scheduler), even
    for unfamiliar architectures or problem domains.