semvec/pypi-packages huggingface dataset:
zynapse 0.1.2
ZkAGI SDK
Requires Python: >=3.8

**ZkAGI SDK** 
================

> Work in Progress 👷

**Description:**
ZkAGI SDK provides a comprehensive toolkit for building scalable, secure, and privacy-preserving applications. It integrates GPU clustering, contribution tracking, ML/LLM models, and privacy-preserved infrastructure to enable developers to build robust and efficient systems.

**Features:**

* **GPU Clustering:** Efficiently distribute tasks across a GPU cluster using **Ray**, allowing for scalable and high-performance computing.
* **Contribution Tracking:** Track user contributions and usage metrics, and reward users with tokens based on their contributions. This feature enables developers to incentivize user engagement and participation.
* **ML/LLM Models:** Run ML and LLM models using popular libraries like TensorFlow, and custom model classes. This feature enables developers to build and deploy machine learning models at scale.
* **Privacy-Preserved Infrastructure:** Generate Zero-Knowledge Proofs (ZkProofs) internally to ensure privacy preservation and data confidentiality. This feature enables developers to build privacy-preserving applications that protect user data.

**Getting Started:**
-------------------

### Installation

To install the ZkAGI SDK, simply run the following command:

`pip install zynapse`

### Importing the SDK

To use the ZkAGI SDK, import it into your Python script or application:

`from zynapse import Zynapse`

### Creating a Zynapse Instance

Create a `Zynapse` instance to access the SDK's features:

`instance = Zynapse("cluster_url", "auth_token", "user_id")`

### Connecting to a GPU Cluster

Connect to a GPU cluster:

`instance.connect()`

### Running an ML Model

Run an ML model on input data:

`result, token_amount = instance.run(input)`

### Tracking a User's Contribution

Track a user's contribution:

`instance.contributions.track("compute_time", execution_time)`
ChainPipe 0.1.1
A utility package for chaining and pipeline operations with enhanced logging and timing.
Requires Python: >=3.6

# ChainPipe

ChainPipe is a Python package designed to facilitate efficient and modular handling of operations in a chain or pipeline manner. It provides two primary classes, `Chain` and `Pipeline`, which allow for sequential and independent operation executions respectively, with enhanced capabilities for logging and timing.

## Features

- **Chain**: Manage a sequence of operations where the output of one operation is the input to the next.
- **Pipeline**: Execute a sequence of independent operations.
- **Timer**: Utility for timing operations, useful for performance analysis.
- **Logger**: Enhanced logging capabilities to track operation execution and errors.

## Installation

Install ChainPipe using pip:

'pip install ChainPipe'

Or, if you want to install from source:

'git clone https://github.com/ayman3000/ChainPipe.git
cd ChainPipe
python setup.py install'

## Usage

Below are quick examples on how to use the `Chain` and `Pipeline` classes:

### Chain Example

```python
from chainpipe.chain import Chain

def multiply_by_two(x):
    return x * 2

def add_five(x):
    return x + 5

chain = Chain() | multiply_by_two | add_five
result = chain.execute(3)
print("Result:", result)
```

### Additional Chain Example Using `add_operation`

```python
from chainpipe.chain import Chain

def multiply_by_three(x):
    return x * 3

def subtract_two(x):
    return x - 2

chain = Chain()
chain.add_operation(multiply_by_three)
chain.add_operation(subtract_two)
result = chain.execute(4)
print("Result:", result)
```

### Pipeline Example

```python
from chainpipe.pipeline import Pipeline

def initialize_system():
    print("System initialized")

def perform_checks():
    print("Checks performed")

pipeline = Pipeline() | initialize_system | perform_checks
pipeline.execute()
```

### Additional Pipeline Example Using `add_operation`

```python
from chainpipe.pipeline import Pipeline

def load_configuration():
    print("Configuration loaded")

def start_services():
    print("Services started")

pipeline = Pipeline()
pipeline.add_operation(load_configuration)
pipeline.add_operation(start_services)
pipeline.execute()
```

## Contributing

Contributions are welcome! Please read the CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

## License

This project is licensed under the MIT License - see the LICENSE.md file for details.

## Contact

For any questions or feedback, please reach out to ayman3000@gmail.com.