semvec/pypi-packages huggingface dataset:
autodiff-team44 0.1.0
This package is an automatic differentiation package which will evaluate derivatives with machine-precision almost instantaneously. The speed and precision of autodifferentiation is paramount to applications such as deep learning models, and mitigates inefficiencies that compound with larger and more complex functions.
Requires Python: >=3.7

# Team 44
### ID: team44

Team members: Leo Shao, Ibraheem Khan, Reem Ali, Yigit Sonmez, Chepchumba Choge

![test.yml](https://code.harvard.edu/CS107/team44/actions/workflows/test.yml/badge.svg)
![coverage.yml](https://code.harvard.edu/CS107/team44/actions/workflows/coverage.yml/badge.svg)
cyanDiff 0.3
AD package for CS107 final project
Requires Python: >=3.7

# cyanDiff - a simple auto-differentiation library
[![.github/workflows/coverage.yml](https://code.harvard.edu/CS107/team40/actions/workflows/coverage.yml/badge.svg)](https://code.harvard.edu/CS107/team40/actions/workflows/coverage.yml) [![.github/workflows/test.yml](https://code.harvard.edu/CS107/team40/actions/workflows/test.yml/badge.svg)](https://code.harvard.edu/CS107/team40/actions/workflows/test.yml)
Anyside 0.0.6
Anyside Python API
Keywords: python,anyside


# Anyside Python API

Welcome to the Anyside Python API!<br />
[Node.js API](https://www.npmjs.com/package/anyside)

In order to use the Anyside API you will need an API key. You can get your API key by creating an account on [anyside.com](https://anyside.com/).

## Installation

To install the Python API and get started you can simply pip install Anyside into your project.

```sh
$ pip install Anyside
```

## Query Domain

When making a request must pass a anyside domain name.

Response will contain wallest connected with anyside

```python
from anyside import Anyside

anyside = Anyside(api_key='YOUR API KEY')

domain = anyside.query_domain(domain="QUERY DOMAIN NAME Eg. John.any")
print(domain)

"""
#Response Example:
{'Bitcoin': [{'address': '0xBTC-SOMESTUFF', 'name': 'Bitcoin'}],
 'Solana': [{'address': '0x...', 'name': 'Solana'}]}
"""
```

## Wallet lookup
When making a request must pass a vaild wallet address.

Response will contain anyside domain

```python
from anyside import Anyside

anyside = Anyside(api_key='YOUR API KEY')

lookupWallet = anyside.lookup_wallet(wallet_address="0xaeA38149566430Anyside7321B04Anyside")
print(lookupWallet)

"""
#Response Example:
{'domain': 'John.any'}
"""
```
Please subscribe on [Anyside](https://anyside.com/) to receive project updates.
You can aslo follow us on [Twitter](https://twitter.com/AnysideNames) and [Discord](https://discord.com/invite/MKDBhDEtUn).
calctic 0.2
A command line tool for measuring the elapsed time of a Python file process

# calctic

A command line tool for measuring the elapsed time of a Python file process.

## Features

- Measures the time it takes to read and execute a Python file.
- Prints the elapsed time for the user, system, and overall process.
- Provides a simple evaluation of the overall elapsed time (good or bad).

## Getting Started

1. Install the library: `pip install calctic`
2. Run `calctic` followed by the name of the Python file you want to measure the elapsed time of: `calctic foo.py`

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
tensorflow-image-models 0.0.11
Tensorflow image models
Requires Python: >=3.6

Install<br />

pip install tensorflow_image_models==0.0.11

----------------------------------------------

Usage<br />

from tensorflow_image_models import list_models<br />

list_models()<br />

models list:<br />
DPN92<br />
DPN98<br />
DPN131<br />
DPN107<br />
EfficientNetB0<br />
EfficientNetB1<br />
.....<br />
.....<br />
VGG16<br />
VGG19<br />
.....<br />
.....<br />
ViTGaint_14<br />
ViTGaint_16<br />

Create Model<br />

from tensorflow_image_models import EfficientNet<br />
model = EfficientNet.EfficientNetB0(classes=10)<br />
model.summary()


from tensorflow_image_models import Inception<br />
model = Inception.InceptionV4(classes=3, input_shape=(299,299,3)<br />
model.summary()

----------------------------------------------

Here is a list of input shape expected for each model

EfficientNetB0 = (224, 224, 3)<br />
EfficientNetB1 = (240, 240, 3)<br />
EfficientNetB2 = (260, 260, 3)<br />
EfficientNetB3 = (300, 300, 3)<br />
EfficientNetB4 = (380, 380, 3)<br />
EfficientNetB5 = (456, 456, 3)<br />
EfficientNetB6 = (528, 528, 3)<br />
EfficientNetB7 = (600, 600, 3)<br />
AlexNet = (224, 224, 3)<br />
InceptionResNetV2 = (299, 299, 3)<br />
InceptionV3 = (299, 299, 3)<br />
LeNet = (32,  32,  1)<br />
MobileNetV2 = (224, 224, 3)<br />
ResNet18 = (224, 224, 3)<br />
ResNet34 = (224, 224, 3)<br />
VoVNet27 = (224, 224, 3)<br />
VoVNet39 = (224, 224, 3)<br />
VoVNet57 = (224, 224, 3)<br />
ViTTiny  = (224, 224, 3)<br />
ViTSmall_8 = (224, 224, 3)<br />
ViTSmall_16 = (224, 224, 3)<br />
ViTSmall_32 = (224, 224, 3)<br />
ViTBase_8 = (224, 224, 3)<br />
ViTBase_16 = (224, 224, 3)<br />
ViTBase_32 = (224, 224, 3)<br />
ViTLarge_14 = (224, 224, 3)<br />
ViTLarge_16 = (224, 224, 3)<br />
ViTLarge_32 = (224, 224, 3)<br />
ViTHuge_14 = (224, 224, 3)<br />
ViTHuge_16 = (224, 224, 3)<br />
ViTGaint_14 = (224, 224, 3)<br />
ViTGaint_16 = (224, 224, 3)<br />


The rest of the models receive any shape, and the default shape is (128,128,3)

----------------------------------------------

License<br />
This project is licensed under the MIT License

----------------------------------------------

Author<br />
DEEPOLOGY LAB