semvec/pypi-packages huggingface dataset:
agptx15 1.0.2
Project & Project Path Management library.
Requires Python: >=3

Hey, Akshat this side!
The sole purpose of this library is to help you quickly prototype your projects and deploy them on different platforms. It simplifies managing the root path of your project directory across all your Python files, allowing you to effortlessly import and export project paths on your system or server. Below are the functions you can use:



--> set_project_path(project_name:str, project_directory_path:str)

Use this function to set the path of your project directory.
    The function "get_project_path" can be used later to import the complete path of your project directory in any file.
    
    Args: 
    project_name : Name of your project.
    project_directory_path : Path to your project's root directory

    Returns: Nothing to return ehh!



--> get_project_path(project_name:str)

Fetch the path of a project.
    
    Args:
    - project_name (str): Name of the project.
    
    Returns:
    - str or None: Path of the project if found, None otherwise.



--> remove_project_path(project_name:str)

Removes the given project_name and project_path
    
    Args: 
    project_name : Name of your project that you wish to remove.

    Returns: Nothing to return ehh!



--> show_project_paths()

Shows the list of all the projects and projects paths added till date
flowlite 0.0.3
A simple, educational deep learning framework
Keywords: deep learning, auto-diff


# FlowLite

FlowLite is a lightweight deep learning framework designed for educational purposes, with an API similar to PyTorch. It simplifies the process of building, training, and deploying neural networks, making it ideal for beginners and educators in the field of AI.

## Installation

You can install FlowLite using pip:

```bash
pip install flowlite
```


## Quick Start
Here's a quick example of using FlowLite to create a simple neural network:
```python
import flowlite
import flowlite.nn as nn
import flowlite.optim as optim
import flowlite.functional as F

# Define a simple model
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Initialize the model
model = Net()

# Define a loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Dummy data for the sake of example
inputs = flowlite.randn(64, 784)  # Batch size 64, input dimension 784
labels = flowlite.randint(0, 10, (64,))  # Random labels for a batch size of 64

# Forward pass
outputs = model(inputs)
loss = loss_fn(outputs, labels)

# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()

print('Training step complete.')
```

## License
This project is licensed under the [Apache License (Version 2.0)](https://github.com/caaatch22/flowlite/blob/main/LICENSE).