semvec/pypi-packages huggingface dataset:
robot-rumble 0.2.0
2D Side-Scroller Game for UF CEN4930 Performant Programming (in Japan!)
Requires Python: ==3.10.*

Robot Rumble is a 2D Side-Scroller Game for UF CEN4930 Performant Programming (in Japan!)

Installation: pip install robot_rumble

Run Command: play_robot_rumble

GitHub: https://github.com/guptat07/Robot-Rumble
torchmanager-nightly 1.2rc5
PyTorch Training Manager v1.2rc5
Requires Python: >=3.8,<4.0

# torchmanager
### A highly-wrapped PyTorch training and testing manager

## Pre-request
* Python 3.8+
* PyTorch
* tqdm
* scipy (Optional)
* tensorboard (Optional)

## Installation
* PyPi: `pip install torchmanager-nightly`
* Conda: `conda install -c kisonho torchmanager-nightly`

## The Manager
1. Start with configurations:
```
from torchmanager.configs import Configs as _Configs

# define necessary configurations
class Configs(_Configs):
    epochs: int
    lr: float

    ...

    def get_arguments(parser: Union[argparse.ArgumentParser, argparse._ArgumentGroup] = argparse.ArgumentParser()) -> Union[argparse.ArgumentParser, argparse._ArgumentGroup]:
        '''Add arguments to argument parser'''
        ...

    def show_settings(self) -> None:
        ...

# get configs from terminal arguments
configs = Configs.from_arguments()
```

2. Initialize the manager with target model, optimizer, loss function, and metrics:
```
import torch, torchmanager

# define model
class PytorchModel(torch.nn.Module):
    ...

# initialize model, optimizer, loss function, and metrics
model = PytorchModel(...)
optimizer = torch.optim.SGD(model.parameters(), lr=configs.lr)
loss_fn = torchmanager.losses.CrossEntropy()
metrics = {'accuracy': torchmanager.metrics.SparseCategoricalAccuracy()}

# initialize manager
manager = torchmanager.Manager(model, optimizer, loss_fn=loss_fn, metrics=metrics)
```

3. Train the model with fit method:
```
from torchmanager.data import Dataset

# get datasets
training_dataset: Dataset = ...
val_dataset: Dataset = ...

# train with fit method
manager.fit(training_dataset, epochs=configs.epochs, val_dataset=val_dataset)
```

- There are also some other callbacks to use:
```
...

tensorboard_callback = torchmanager.callbacks.TensorBoard('logs') # tensorboard dependency required
last_ckpt_callback = torchmanager.callbacks.LastCheckpoint(manager, 'last.model')
model = manager.fit(..., callbacks_list=[tensorboard_callback, last_ckpt_callback])
```

- Or use `callbacks.Experiment` to handle both `callbacks.TensorBoard` and `callbacks.LastCheckpoint`:
```
...

exp_callback = torchmanager.callbacks.Experiment('test.exp', manager) # tensorboard dependency required
model = manager.fit(..., callbacks_list=[exp_callback])
```

4. Test the model with test method:
```
# get dataset
testing_dataset: Dataset = ...

# test with test method
manager.test(testing_dataset)
```

5. Save final model in PyTorch format:
```
torch.save(model, "model.pth")
```

## Custom your training loop
1. Create your own manager class by extending the `Manager` class:
```
...

class CustomManager(Manager):
    ...
```

2. Override the `train_step` method:
```
class CustomManager(Manager):
    ...
    
    def train_step(x_train: torch.Tensor, y_train: torch.Tensor) -> Dict[str, float]:
        ...
```
pypeims 0.0.32
Defining classes and functions for accessing and manipulating Texas PEIMS data (including data in TAPR and statistics typically accessed through Snapshot) programmatically.
Requires Python: >=3.7

# PyPEIMS

[![PyPI - Version](https://img.shields.io/pypi/v/pypeims.svg)](https://pypi.org/project/pypeims)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pypeims.svg)](https://pypi.org/project/pypeims)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install pypeims
```

## License

`pypeims` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
tesla-ce-provider-ks-tks 0.0.10
TeSLA CE Keystroke Provider
Requires Python: >=3.8

[![PyPi Version](https://img.shields.io/pypi/v/tesla-ce-provider-ks-tks.svg)](https://pypi.python.org/pypi/tesla-ce-provider-ks-tks/)
[![codecov](https://codecov.io/gh/tesla-ce/provider-ks-tks/branch/main/graph/badge.svg?token=PJJQMW981P)](https://codecov.io/gh/tesla-ce/provider-ks-tks)
[![AGPL License](https://img.shields.io/badge/license-AGPL-blue.svg)](http://www.gnu.org/licenses/agpl-3.0)
[![FOSSA Status](https://app.fossa.com/api/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-ks-tks.svg?type=shield)](https://app.fossa.com/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-ks-tks?ref=badge_shield)
[![Quality gate](https://sonar.sunai.uoc.edu/api/project_badges/quality_gate?project=tesla-ce_provider-ks-tks)](https://sonar.sunai.uoc.edu/dashboard?id=tesla-ce_provider-ks-tks)
# TKS: TeSLA CE Keystroke Dynamics Recognition provider 

| :warning: This repository is **under construction**. Final release of TeSLA Community Edition source code is expected by the **end of 2021** |
| --- |

## Description
Description: A description of your project follows. A good description is clear, short, and to the point. Describe the importance of your project, and what it does.

## Installation
Installation: Installation is the next section in an effective README. Tell other users how to install your project locally. Optionally, include a gif to make the process even more clear for other people.

## Usage
Usage: The next section is usage, in which you instruct other people on how to use your project after they’ve installed it. This would also be a good place to include screenshots of your project in action.

## Contributing
Contributing: Larger projects often have sections on contributing to their project, in which contribution instructions are outlined. Sometimes, this is a separate file. If you have specific contribution preferences, explain them so that other developers know how to best contribute to your work. To learn more about how to help others contribute, check out the guide for setting guidelines for repository contributors.

## Credits
Credits: Include a section for credits in order to highlight and link to the authors of your project.

## License
This project is licensed under [AGPL v3 licence](http://www.gnu.org/licenses/agpl-3.0).

[![FOSSA Status](https://app.fossa.com/api/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-ks-tks.svg?type=large)](https://app.fossa.com/projects/custom%2B26246%2Fgithub.com%2Ftesla-ce%2Fprovider-ks-tks?ref=badge_large)