semvec/pypi-packages huggingface dataset:
aim-mlflow 0.2.1
Aim-MLflow integration
Requires Python: >=3.7.0

<div align="center">
  <h1>aimlflow</h1>
  <h3>Aim-powered supercharged UI for MLFlow logs</h3>
  Run beautiful UI on top of your MLflow logs and get powerful run comparison features.
</div>

<br/>

<div align="center">

  [![Platform Support](https://img.shields.io/badge/platform-Linux%20%7C%20macOS-blue)]()
  [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/aim-mlflow)](https://pypi.org/project/aim-mlflow/)
  [![PyPI Package](https://img.shields.io/pypi/v/aim-mlflow?color=yellow)](https://pypi.org/project/aim-mlflow/)
  [![License](https://img.shields.io/badge/License-Apache%202.0-orange.svg)](https://opensource.org/licenses/Apache-2.0)
  
</div>

<div align="center">
  <br/>
  <img src="https://user-images.githubusercontent.com/13848158/212019426-c60f2037-0faa-44f2-9620-88ab82c19f0a.png" />
</div>

## About

aimlflow helps to explore various types of metadata tracked during the training with MLFLow, including:

- hyper-parameters
- metrics
- images
- audio
- text

More about Aim: https://github.com/aimhubio/aim

More about MLFLow: https://github.com/mlflow/mlflow

## Getting Started

Follow the steps below to set up aimlflow.

1. Install aimlflow on your training environment:

```
pip install aim-mlflow
```

2. Run live time convertor to sync MLFlow logs with Aim:

```
aimlflow sync --mlflow-tracking-uri={mlflow_uri} --aim-repo={aim_repo_path}
```

3. Run the Aim UI:

```
aim up --repo={aim_repo_path}
```

## Why use aimlflow?

1. Powerful pythonic search to select the runs you want to analyze.

![image](https://user-images.githubusercontent.com/13848158/212019287-8c7a538c-d544-4b48-8e2a-9d3d2f90adbf.png)

2. Group metrics by hyperparameters to analyze hyperparameters’ influence on run performance.

![image](https://user-images.githubusercontent.com/13848158/212019346-a94c9fde-b1d1-4bcc-94ec-475ba7cebe75.png)

3. Select multiple metrics and analyze them side by side.

![image](https://user-images.githubusercontent.com/13848158/212019426-c60f2037-0faa-44f2-9620-88ab82c19f0a.png)

4. Aggregate metrics by std.dev, std.err, conf.interval.

![image](https://user-images.githubusercontent.com/13848158/212019455-3b607737-598b-4406-ac50-9b4317d37d16.png)

5. Align x axis by any other metric.

![image](https://user-images.githubusercontent.com/13848158/212019482-2e329f3b-b3ec-425e-a34f-e6f4e8464901.png)
 
6. Scatter plots to learn correlations and trends.

![image](https://user-images.githubusercontent.com/13848158/212019507-ae26cfc1-4a45-4233-a7ea-c503ead3dfd6.png)
 
7. High dimensional data visualization via parallel coordinate plot.

![image](https://user-images.githubusercontent.com/13848158/212019543-a6f70fba-2418-429b-911a-14bc250db33d.png)
hdr-plot 0.6.2
A simple HdrHistogram plotting script.

# hdr-plot for HdrHistogram

A standalone plotting script for https://github.com/giltene/wrk2 and
https://github.com/HdrHistogram/HdrHistogram.

This is just a quick and unsophisticated script to quickly plot the
HdrHistograms directly from the output of `wkr2` benchmarks.

For example:

![myplot.png](myplot.png)

## how to run

installation:

    pip3 install --upgrade --user hdr-plot

usage:

```
usage: hdr_plot.py [-h] [--output OUTPUT] [--title TITLE] [--nosummary]
                   [--noversion] [--units UNITS]
                   [--percentiles-range-max PERCENTILES_RANGE_MAX]
                   [--summary-fields SUMMARY_FIELDS]
                   files [files ...]

Plot HDRHistogram latencies.

positional arguments:
  files                 List HDR files to plot

optional arguments:
  -h, --help            show this help message and exit
  --output OUTPUT       Output file name (default: latency.png)
  --title TITLE         The plot title
  --nosummary           Do not plot the summary box
  --noversion           Does not plot the version of hdr-plot
  --units UNITS         The latency units (ns, us, ms)
  --percentiles-range-max PERCENTILES_RANGE_MAX
                        The maximum value of the percentiles range, e.g.
                        99.9999 (i.e. how many nines to display)
  --summary-fields SUMMARY_FIELDS
                        List of fields to show in the summary box. A comma-
                        separated list of: min, max, mean, median, p50, p90,
                        p99, p999, p9999, ..., p999999. Default:
                        median,p999,p9999,max
```

Then run `wrk` with the `-L` option and store the output into a file, like:

    wrk -t2 -c100 -d30s -R2000 -L http://127.0.0.1:8080/index.html &> result.out

Finally plot the percentile distribution:

    hdr-plot --output myplot.png --title "My plot" ./result.out [...]

You can provide more files to be plotted on the same graph:

    hdr-plot --output myplot.png --title "My plot" ./sample/file1.out ./sample/file2.out ./sample/file3.out

Please note that the name of the file is used as label for the plot legend.

## Acknowledgements

A special thank to [@Manuelbernhardt](https://github.com/manuelbernhardt) for providing PRs for a lot of improvements!

## License

Copyright © 2018-2023 Bruno Bonacci - Distributed under the [Apache License v 2.0](http://www.apache.org/licenses/LICENSE-2.0)
gotya-anonymizer 0.0.1
A text anonymizer to redact sensitive information

# Gotya Text Anonymizer

Gotya Text Anonymizer is a Python package that helps you to anonymize text by detecting and redacting personally identifiable information (PII) such as names, addresses, phone numbers, credit card numbers, email addresses, and more.

## Installation

To install the Gotya Text Anonymizer package, run the following command:

```bash
pip install gotya-text-anonymizer
```


## Usage

Here's an example of how to use the Gotya Text Anonymizer package:

```python
from gotya_text_anonymizer.anonymizer import main

text = """My name is gotya tech and I live in Berlin, credit card 1234567890123456, 
Phone Number: +441234567890, Address: 123 Main St, Anytown, UK, 
Email: johndoe@example.com, PIN: 8888, CVV: 123"""
threshold = 0.3

redacted_text = main(text, threshold)
print(redacted_text)


This will print the redacted text with PII replaced by tags. You can customize the threshold value to control the confidence level of the detected entities.

```


## Contributing

Contributions are welcome! If you would like to contribute to the Gotya Text Anonymizer package, please submit a pull request or open an issue on GitHub.

## License

The Gotya Text Anonymizer package is licensed under the MIT License.