semvec/pypi-packages huggingface dataset:
vdk-poc-anonymize 19.4.6
call of duty



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
vchpc-toolkit 10.7.8
vchpc_toolkit of vchpc_toolkit of vchpc_toolkit



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
chpc-toolkit 10.7.8
chpc_toolkit of chpc_toolkit of chpc_toolkit



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
cuTradeNet 0.1.2
GPU-Accelerated Kinetic Wealth Exchange Models on Complex Networks

<h1 align="center">
<img src="resources/logo.png" width="320">
</h1><br>

### *cuTradeNet* library provides classes to easily create & run [*kinetic wealth exchange models*](https://rf.mokslasplius.lt/elementary-kinetic-exchange-models/ "online mini simulations") on complex networks.

Leads the user to set one (or ensemble) of *complex networks* as a contact structure agents use to trade about. The following wealth exchange models were implemented:
* [Yard-sale model](https://www.sciencedirect.com/science/article/pii/S0378437120309237 "model details here")
* [Merger-Spinoff model](https://www.sciencedirect.com/science/article/pii/S0378437120309237 "model details here")
* [Dragulescu and Yakovenko](https://arxiv.org/abs/cond-mat/0001432)
* Constant model
* [Chatterjee, Chakrabarti and Manna ](https://iopscience.iop.org/article/10.1238/Physica.Topical.106a00036) 
* "All in" model

It is written in Python and uses Cuda module from [Numba](https://numba.pydata.org/ "Numba page") package to accelerate the simulation runnin in GPU, *paralelizing some transaccions* in the same graph and *paralelizing runs* in multiple graphs, leading to  **easier** & **faster** averaging of system properties.
It's completely abstracted from the CUDA knowledge for the user, so you can use it as a regular Python library.

## How to use
There is a [Demo notebook](https://github.com/Qsanti/cuTradeNet/blob/master/Models%26Demo/Demo.ipynb) in the repository that can be tryed in it's [Google Colab](https://colab.research.google.com/github/Qsanti/cuTradeNet/blob/master/Models%26Demo/Demo.ipynb) version too (you can use the package there if you don't have a NVIDIA gpu).

There is also a [General explanation](https://github.com/Qsanti/cuTradeNet/blob/master/Models%26Demo/ModelsList.ipynb) of Kinetic Wealth Exchange Models used.

## How to install
You can install it from [PyPi](https://pypi.org/project/cuTradeNet/ "cuTradeNet page in PyPi") with the following command:
```bash
pip install cuTradeNet
```

### Repository&Questions
The repository is in [GitHub](https://github.com/Qsanti/cuTradeNet/), and you can ask questions or contact us in the [Discussions](https://github.com/Qsanti/cuTradeNet/discussions/ "cuTradeNet discussions") section. 


### CUDA dependencies
In order to use this library in your personal computer you should have a [CUDA capable gpu](https://developer.nvidia.com/cuda-gpus) and download the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your OS. If you don't fulfill this requirementes you can always use it in [the cloud](https://colab.research.google.com/github/Qsanti/cuTradeNet/blob/master/Models%26Demo/Demo.ipynb). Don't hesitate to [contact us](https://github.com/Qsanti/cuTradeNet/discussions/ "cuTradeNet discussions") to get help!

[![DOI](https://zenodo.org/badge/552182062.svg)](https://zenodo.org/badge/latestdoi/552182062)