Metadata-Version: 2.1
Name: rubrix
Version: 0.1.2
Summary: Open-source tool for tracking, exploring and labelling data for AI projects.
Home-page: https://recogn.ai
Author: recognai
Author-email: contact@recogn.ai
Maintainer: recognai
Maintainer-email: contact@recogn.ai
License: Apache-2.0
Keywords: data-science natural-language-processing artificial-intelligence knowledged-graph developers-tools human-in-the-loop mlops
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
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<p align="center">
    <img src="docs/images/rubrix_logo.svg" alt="drawing" width="225"/>
</p>

### Open-source tool for tracking, exploring, and labelling data for AI

<p align="center">
    <a href="https://github.com/recognai/rubrix/actions">
        <img alt="CI" src="https://github.com/recognai/rubrix/workflows/CI/badge.svg?branch=master&event=push">
    </a>
    <!--a href="https://github.com/recognai/rubrix/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/recognai/rubrix.svg?color=blue">
    </a-->
</p>


[Rubrix](https://rubrix.ml) is a tool for tracking and iterating on data for artificial intelligence projects. 

Rubrix focuses on enabling novel, human in the loop workflows involving data scientists, subject matter experts and ML/data engineers. 

![](docs/images/rubrix_intro.svg)

With Rubrix, you can:

- **Monitor** the predictions of deployed models.
- **Label** data for starting up or evolving an existing project.
- **Iterate** on ****ground-truth**** and predictions to debug, track and improve your data and models over time.
- **Build** custom ****applications and dashboards**** on top of your model predictions.

We've tried to make working with Rubrix easy and fun, while keeping it scalable and flexible. 

Rubrix is composed of:

- a **Python library** to bridge data and models, which you can install via `pip`.
- a **web application** to explore and label data, which you can launch using Docker or directly with Python.


This is an example of Rubrix UI annotation mode:

![Rubrix Annotation Mode](https://github.com/dvsrepo/imgs/blob/main/rubrix_annotation_mode.gif)


📖 For more information, visit the [documentation](https://docs.rubrix.ml/en/stable/) or if you want to get started, keep reading.

# Get started

To get started you need to follow three steps:

1. Install the Python client
2. Launch the web app
3. Start logging data
   
## 1. Install the Python client

You can install the Python client with `pip`:

```python
pip install rubrix
```

## 2. Launch the webapp

There are two ways to launch the webapp:

- Using [docker-compose](https://docs.docker.com/compose/) (**recommended**).
- Executing the server code manually

### Using docker-compose (recommended)

Create a folder:

```bash
mkdir rubrix && cd rubrix
```

and launch the docker-contained web app with the following command:

```bash
wget -O docker-compose.yml https://raw.githubusercontent.com/recognai/rubrix/master/docker-compose.yaml && docker-compose up
```

This is the recommended way because it automatically includes an
[Elasticsearch](https://www.elastic.co/elasticsearch/) instance, Rubrix's main persistent layer.

### Executing the server code manually

When executing the server code manually you need to provide an [Elasticsearch](https://www.elastic.co/elasticsearch/) instance yourself.

1. First you need to install
   [Elasticsearch](https://www.elastic.co/guide/en/elasticsearch/reference/7.10/install-elasticsearch.html)
   (we recommend version 7.10) and launch an Elasticsearch instance.
   For MacOS and Windows there are
   [Homebrew formulae](https://www.elastic.co/guide/en/elasticsearch/reference/7.13/brew.html) and a
   [msi package](https://www.elastic.co/guide/en/elasticsearch/reference/current/windows.html), respectively.
2. Install the Rubrix Python library together with its server dependencies:

```bash
pip install rubrix[server]
```

3. Launch a local instance of the Rubrix web app

```bash
python -m rubrix.server
```

By default, the Rubrix server will look for your Elasticsearch endpoint at ``http://localhost:9200``.
If you want to customize this, you can set the ``ELASTICSEARCH`` environment variable pointing to your endpoint.

## 3. Start logging data

The following code will log one record into the `example-dataset` dataset: 

```python
import rubrix as rb

rb.log(
    rb.TextClassificationRecord(inputs="my first rubrix example"),
    name='example-dataset'
)

```

```bash
BulkResponse(dataset='example-dataset', processed=1, failed=0)
```

If you go to your Rubrix app at [http://localhost:6900/](http://localhost:6900/), you should see your first dataset.

Congratulations! You are ready to start working with Rubrix with your own data.

To better understand what's possible take a look at Rubrix's [Cookbook](https://docs.rubrix.ml/en/stable/guides/cookbook.html)

# Community
As a new open-source project, we are eager to hear your thoughts, fix bugs, and help you get started. Feel free to use the Discussion forum or the Issues and we'll be pleased to help out.


