semvec/pypi-packages huggingface dataset:
kernelutility 0.1.1
A Python package for managing Python kernels
Requires Python: >3.7
Keywords: jupyter kernel conda

# _kernelutility_ - A Python package for managing Python kernels

> Joseph P. Vantassel, Texas Advanced Computing Center - The University of Texas at Austin

## About _kernelutility_

_kernelutility_ was developed to allow users of the DesignSafe-CyberInfrastructure's
JupyterHub to easily manage their own Python kernels, which include a dedicated Python
interpreter and associated packages.

## Getting Started

- Start a notebook or terminal in DesignSafe's JupyterHub. Note that you must use the latest
Jupyter notebook image.
- Install _kernelutility_ with `!pip install kernelutility` (if in notebook) or
`pip install kernelutility` (if in terminal).
If you are in a notebook you must restart your kernel for the installation of _kernelutility_
to be visible to Python.
- Load kernelset with `from kernelutility import kernelset`. This will initialize your kernel
set and reactivate any prior kernels
if they are present.
- Use the methods of `kernelset` to modify your available kernels. The key methods are `add`,
`remove`, `create`, and `destroy`. See the example `kernelutility.ipynb` and the API documentation
for more information.
atomquant 0.0.1
AtomQuant: Quantization For Human.

# Atom Quant

**Atom Quant** AKA: `aq` is a easy quantization lib supports most decent and fashion quantization method through `torch.fx`. Unlike original pytorch fx quantization support, we add a fully deploy chain from PTQ and QAT quantization to exporting onnx and then shiping to target inference framework.

atomquant can be easily use to quant any model without a specific dataloader or evaluator, you can even evaluator quantization performance without any GT. 

We also support different quantization from vendor package, such as `onnxruntime`, `pytorch_quantization`, make it more easy to use and with fully examples.

There are 3 main components in atomquant:

- onnx: directly quantize on onnx model (via onnxruntime);
- atom: Our built-in quantization method;
- tensorrt: Quantization specific for convert to TensorRT engine usage;


## Install

atomquant can be installed via:

```
pip install atomquant
```


## Model Zoo

Here, we provide some models quantized for coco, it devided into CPU use, or TensorRT use. Related training code also available:



## Examples

1. Quant Classification


2. Quant GPT3


3. Quant VITS


4. Quant AlphaPose



5. Quant YOLOv7
castep-parse 0.2.3
Input file writers and output file parsers for the density functional theory code CASTEP.

[![PyPI version](https://badge.fury.io/py/castep-parse.svg)](https://badge.fury.io/py/castep-parse)

# castep-parse
Input file writers and output file readers for the density functional theory code CASTEP.

## Installation

`pip install castep-parse`

## Notes

This code has generally been used for `task=singlepoint` and `task=geometryoptimisation` simulations. Consequently, input and output files associated with other simulation types are not yet supported.

## Functionality

### Readers:

- ✅ `read_castep_file`
- ️✅ `read_geom_file`
- ✅ `read_cell_file`
- ✅ `read_output_files`

### Writers:

- ✅ `write_cell_file`
- ✅ `write_input_files`

### Utilities:

- ✅ `map_species_to_castep`
- ✅ `get_castep_cell_constraints`

## Examples

### Do something useful