semvec/pypi-packages huggingface dataset:
yacare-cluster 0.2
Package to use the YACARE algorithm for clustering data
Requires Python: >=3.8

# YACARE

This package implements the YACARE algorithm for clustering data. If you want to use it, please cite "Clustering data by reordering them" by Descamps et al.
This package is currently mainly empty, and we will upload it to PyPI once the paper is accepted for publication.
The paper will be upload to arXiv in March 2025.
uniworkflow 0.1.8
A Python library for integrating with various workflow providers
Requires Python: >=3.6

# UniWorkflow

[English Version](README.md) | [中文版](README_zh.md)


UniWorkflow is a Python library that provides a unified interface for integrating with various workflow providers such as Make.com and Dify.

## Installation

You can install UniWorkflow using pip:

```
pip install uniworkflow
```

## Quick Start

### Run a make.com workflow
Here's a simple example of how to use UniWorkflow:

```python
# import the library
from uniworkflow import UniWorkflow

# prepare the kwargs
kwargs = {
    "workflow_url": "https://example.make.com/your-workflow-hook",
    "method": "GET",
    "api_key": "your_api_key_here",
    "data": {"key1": "value1", "key2": "value2", ......},
}

# Execute a workflow
result = UniWorkflow.execute("make", **kwargs)

# print the result
print(result)
```

### Run a dify workflow

```python
# import the library
from uniworkflow import UniWorkflow

# prepare the kwargs
kwargs = {
    "workflow_url": "https://api.dify.ai/v1/workflows/run",
    "method": "POST",
    "api_key": "your_api_key_here",
    "data": {"key1": "value1", "key2": "value2", ......},
}

# Execute a workflow
result = UniWorkflow.execute("dify", **kwargs) 

# print the result
print(result)
```

## Parameters

- `workflow_url`: The URL of the workflow to execute.
- `method`: The HTTP method to use (GET, POST, etc.).
- `api_key`: The API key to use for the workflow.
- `data`: The data to pass to the workflow.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Running Tests

To run the tests for UniWorkflow, you can use pytest. Make sure you have pytest installed and replace the parameters in the test cases with your own.

```
pip install pytest
```

Then, run the tests:

```
pytest -s tests/test_make.py
pytest -s tests/test_dify.py
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
aimrun 0.9.8
A simple way of integrating aim into MLOps frameworks.
Requires Python: >=3.10

# aimrun
A simple interface for integrating aim into MLOps frameworks. 

## Installation 
```bash
pip install aimrun
```

## Features
1. Multiple runs. Simply initialize multiple aimruns using ```aimrun.init()```, and track to multiple repositories at once. 
2. No need for a main-process wrapper. You do not need to make sure that only the main-process calls aimrun functions - we take care of that for you.
3. Sync project-specific (local) repositories to larger (remote) repositories. See ```python -m aimrun sync --help``` for guidance.

## Usage (Recommended)
1. Initialize one or more aimruns using ```aimrun.init()```
2. Use  ```aimrun.track()``` to track values. Parse a dictionary.
3. Use  ```aimrun.close()``` to finalize the experiments.


### Example usage
```python
import aimrun

# initalize 
aimrun.init(repo='aim://172.3.66.145:53800', experiment='my_experiment', description='description of run', args={"arg": 1}) # args=vars(args) if you use argsparse

# track 
aimrun.track({"value_0": A, "value_1": B})
# or 
aimrun.track(A, name="value_0")

# close 
aimrun.close() 
```

### Synchronizing on-going runs
```python
aimrun.init(repo=".", sync_repo='aim://172.3.66.145:53800', sync_args={"repeat": 60}, experiment='my_experiment', description='description of run' args={"arg": 1})
```
This starts a thread that incrementally synchronizes the current on-going run to a remote repo while using the current directory as the local repository.

To profit from mass updates (faster synchronization), consider installing an improved aim version:
```bash
pip install git+https://github.com/schneiderkamplab/aim
```

## Drop-in replacement Wandb (Experimental)
We experimentally offer aimrun as a drop-in replacement for wandb, making a seamless integration in your framework even easier.

1. Replace``import wandb`` with ``from aimrun import wandb``
2. Set default repository before init (e.g. right after import)  ```wandb.set_default_repo('aim://172.3.66.145:53800')```
3. Supported functions ```.init(), .log(),  .finish()```