semvec/pypi-packages huggingface dataset:
open-py-ml 0.0.3
A simple, Open source ML library.

A simple ML library.

<br>
<code>
class Brain:<br>
    """<br>
    A brain. An Artificial intelligence (AI)'s data and runner.<br>
    Data: First argument in __init__(), Data is for the training data key/values<br>
    Threshold: Second argument in __init(), Threshold is for the percentage of which the AI finds<br>
    That the data is simular enough to the input string of which it is accepted<br>
    NOTE: This only works with strings now.<br>
    """<br>
    <br>
    def __init__(self, data: dict or None, threshold: float):<br>
        """<br>
        Initializes a new brain.<br>
        """<br>
    <br>
    def forward(self, data: str) -> str:<br>
        """<br>
        Gives a string for the Artificial intelligence (AI) to process.<br>
        """<br>
</code>
<br>

As you can see, This is just the way the brain is organized.<br>
You can only just use this, and you would have A good time.<br>
But for quickly loading the brain. By quickly<br> I mean without having to load the file yourself with "open()"
<br> You can actually just use load(filename, threshold), and save(ai, threshold).<br>
Note that load and save just save with json.<br>
modoptima 0.0.0
ModOptima is a library that democratize deep learning model optimizations to developers. Letting them optimize their models with few lines of code.

# ModOptima

Modoptima is a model optimization library that aims at democratizing model deployment for software or machine learning engineers who want to deploy their systems in production. It is currently in development and it aims at optimizing and running models in  least lines of code possible.

### Models Supported.
* Tiny YOLO V7 (Supported)
* Base YOLO V7 (Will be supported in future.)

### Prerequisites.

##### Install deepsparse
`pip install deepsparse[yolo]`


### Installation

The modoptima can be installed from the source at the moment but will be available via pip real soon.

#### Build from source
You can install modoptima by following below steps:

* Clone the repo.

`git clone https://github.com/VikasOjha666/modoptima.git`

* Navigate to the cloned folder.

`cd modoptima`

* Install by running the setup.

`python setup.py install`

#### Install via pip
Coming soon.

#### USAGE

##### Optimizing Model

Modoptima follows two types of optimization i.e Training and non training based the models. 
YOLO V7 optimization is based on pruning and quantization hence the model is pruned during training to reach a certain sparsity.
If quantization option is enabled then quantization aware training is done.

For each model supported there is a notebook showing the step by step process to optimize a model and run inference.
Please check notebooks folder in the repo.