semvec/pypi-packages huggingface dataset:
kerasAndMatplotlib 0.1
Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks. Keras was developed with a focus on simplicity and ease of use, allowing researchers and developers to quickly prototype and build deep learning models.

Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks. Keras was developed with a focus on simplicity and ease of use, allowing researchers and developers to quickly prototype and build deep learning models.Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks. Keras was developed with a focus on simplicity and ease of use, allowing researchers and developers to quickly prototype and build deep learning models.Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks. Keras was developed with a focus on simplicity and ease of use, allowing researchers and developers to quickly prototype and build deep learning models.Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks. Keras was developed with a focus on simplicity and ease of use, allowing researchers and developers to quickly prototype and build deep learning models.Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks. Keras was developed with a focus on simplicity and ease of use, allowing researchers and developers to quickly prototype and build deep learning models.
fedbase 0.10.3
An easy, silly, DIY Federated Learning framework with many baselines for individual researchers.

# FedBase
An easy, modularized, DIY Federated Learning framework with many baselines for individual researchers.

## Installation
[fedbase @ pypi](https://pypi.org/project/fedbase/)
```python
pip install --upgrade fedbase
```

## Baselines
1. Centralized training
2. Local training
3. FedAvg, [Communication-Efficient Learning of Deep Networksfrom Decentralized Data](https://arxiv.org/abs/1602.05629)
4. FedAvg + Finetune
5. Fedprox, [Federated Optimization in Heterogeneous Networks](https://arxiv.org/abs/1812.06127)
5. Ditto, [Ditto: Fair and Robust Federated Learning Through Personalization](https://arxiv.org/abs/2012.04221)
6. WeCFL, [On the Convergence of Clustered Federated Learning](https://arxiv.org/abs/2202.06187)
7. IFCA, [An Efficient Framework for Clustered Federated Learning](https://arxiv.org/abs/2006.04088)
8. FeSEM, [Multi-Center Federated Learning](https://arxiv.org/abs/2005.01026)
8. To be continued...

## Three steps to achieve FedAvg!
1. Data partition
2. Nodes and server simulation
3. Train and test

## Design philosophy
1. Dataset
    1. Dataset
        1. MNIST
        2. CIFAR-10
        3. Fashion-MNIST
        4. ...
    2. Dataset partition
        1. IID
        2. Non-IID
            1. Dirichlet distribution
            2. N-class
            3. ...
        3. Fake data
        4. ...
    <!-- 3. Batch_size -->
2. Node
    1. Local dataset
    2. Model
    3. Objective
    4. Optimizer
    5. Local update
    6. Test
3. Server
    1. Model
    2. Aggregate
    3. Distribute
4. Server & Node
    1. Topology
    2. Client sampling
    3. Exchange message
5. Baselines
    1. Global
    2. Local
    3. FedAvg
6. Visualization

## How to develop your own FL with fedbase?
easy-gpt-utils 0.1.10
Easy GPT utils include 1. chat completion 2. embedding and 3. vector database

# easy_gpt_utils
Easy GPT Utils include 1. chat completion 2. embedding and 3. vector database and others to help create app based on GPT and vector search
(vector database is not implemented yet)

## gpt
gpt is a simple util to use OpenAI's chat modles such as gpt-3.5-terbo gpt-4 etc. It has functions:
1. calculate token of a string
2. split string by paragraph and sentences within maximum token limit
3. set model, temprature
4. set system prompt and post prompt
5. add context infomation to query
6. remember talking history(buggy, will exceed token limit)

## embedding
embedding is a simple util to create embedding (1536 dimension victor) for strings and paragraphs, It has functions:
1. create embedding from string ,paragrahp and file
2. save embeddings to file

## vector_base
not implemented yet


## In summary, easy_gpt_utils is now on early stage and hope it could help to create more powerful AI Apps in the future