semvec/pypi-packages huggingface dataset:
gynumdata 0.0.3
A simple package calculating values for a single number and created in my Udemy course
Keywords: package numbers calculations

# NumData Documentation
***

## Installation
gynumdata can be installed using python pip. The package is hosted on [PyPI](https://pypi.org/) and [PyPI-test](https://test.pypi.org)

```python
pip install gynumdata
```
***
## Usage 
Initialization is not required for using methods in numdata

```python
from gynumdata import calculate

# Calculate square of a number
print(gynumdata.calculate.calculate_square(8))
# 64

# Calculate cube of a number
print(gynumdata.calculate.calculate_cube(8))
# 512
```
starfruitai 0.0.3
Starfruit AI - Deep Learning Deployments Made Simple
Keywords: python,pytorch,deployment

# Starfruit AI
***

### Starfruit AI: Deep Learning Deployments Made Simple

 Starfruit AI allows you to deploy deep learning models with one line of code.
 We handle all the deployment setup, maintenance, auto-scaling and regionalization. You just pay per call.

[Installation](#installation)<br>
[Create an account](#create-an-account)<br>
[API Key](#get-an-api-key)<br>
[Usage](#example-usage)<br>

## Installation
***

- Starfruit AI can be installed via PIP:

`python -m pip install starfruitai`

- Then used in your code with an import:

`import starfruit as sf`

## Create an account
***

- Go to https://starfruitai.com and create an account.
![](https://github.com/DeveloperHarris/starfruit_pip/blob/main/img_1.png)

## Get an API key
***

- After creating your account, your API key will be displayed at the top of the screen.
![](https://github.com/DeveloperHarris/starfruit_pip/blob/main/img_3.png)
![](https://github.com/DeveloperHarris/starfruit_pip/blob/main/img_2.png)

## Example Usage
***

- Download a sample densenet model

  ```python
    import torch

    model = torch.hub.load('pytorch/vision:v0.6.0', 'densenet121', pretrained=True)
  ```

- Use Starfruit AI to deploy the model

  ```python
    import starfruit as sf

    # Authenticate with your API key
    sf.auth('API KEY GOES HERE')

    # Deploy your model to Starfruit AI's servers
    sf.pt_deploy(model, 'modelName', 'image_classifier')

    # Sucessfully Uploaded!: modelName
    # Timestamp: 1616644525649
  ```

- Use the returned timestamp to make prediction; we'll be using [this](https://www.google.com/search?q=cute+kitten&tbm=isch&hl=en&cr=&safe=images) image of a cat. <br>
  ![](https://github.com/DeveloperHarris/starfruit_pip/blob/main/img.png)

  ```python
    # Make a prediction
    sf.predict('modelName', '1616644525649', 'cat.jpg')
  ```

- Get back a prediction in JSON format
  ```json
  {
    "data": 
    {
      "Egyptian_cat": 0.7224083542823792,
      "tabby": 0.17468667030334473,
      "Siamese_cat": 0.02608460746705532,
      "lynx": 0.02026565931737423,
      "tiger_cat": 0.01653680019080639
    }
  }
  ```