Get Started
================================

To get started with RIME, first make sure your deployment is :ref:`installed<installguide>`.
Then perform the setup steps.

Set up RIME
-----------

Once you have access to a working RIME deployment, you need the following items in order
to start performing data science tasks:

* Get your RIME cluster URL
* Create an API token
* Install the RIME SDK

Cluster URL
-----------

The URL of your RIME cluster is where the webUI for RIME is available. This URL is also
used to upload results. The URL is typically in the form `rime.{}.rbst.io`. Consult your
site administrator with questions about your cluster URL.

API token
---------

You now need to create an API token. This will be used to connect programmatically to the RIME cluster.
Creating this API Token is done in the web UI. Go to `Settings` -> `API Access Tokens` and create your own 
token. Full details on API tokens are discussed in :ref:`Users, authorization, and authentication<userauth>`.

Install the RIME SDK
--------------------

The RIME SDK is a Python package for interacting with RIME programmatically. This SDK is available
on pypi, so it can easily be installed with `pip install rime-sdk`. This will install the most recent
version of the RIME SDK. If your cluster is on an older version of RIME, you can install that specific
version with `pip install rime-sdk==X.Y.Z`.


.. include:: get_started/setup.md
   :parser: myst_parser.sphinx_

Try a tutorial notebook
-----------------------

Each notebook illustrates how to use RIME to validate, protect, and monitor your ML
pipeline across a wide range of tasks, from classification to ranking, and across
tabular, natural language processing (NLP), and computer vision (CV) data modalities.

The datasets and models used in these examples come from well-known public sources such as

`UCI ML <https://www.kaggle.com/datasets/uciml/adult-census-income>`_ or
`arXiv <https://www.kaggle.com/datasets/Cornell-University/arxiv?select=arxiv-metadata-oai-snapshot.json>`_.

In these notebooks, we walk through the steps of setting up RIME with an example dataset/model;
we run Stress Testing first, then Continuous Testing.

Tabular Notebooks
^^^^^^^^^^^^^^^^^
* |Fraud_notebook|
* |NYC_notebook|
* |Movie_notebook|

NLP Notebooks
^^^^^^^^^^^^^^^^^
* |ArXiv_notebook|
* |Adversarial NLP|

CV Notebooks
^^^^^^^^^^^^^^^^^
* |Animals_notebook|

Set up a new test run
---------------------

After the tutorials, you can get familiar with details about starting a new :ref:`Continuous or Stress test<rimesetup-testing>`.

.. |Adversarial NLP| raw:: html

    <a href="how_to_guides/common_use_cases/adversarial_nlp.html">Adversarial NLP Walkthrough</a>

.. |Fraud_notebook| raw:: html

    <a class="reference external" href="https://colab.research.google.com/drive/18PXd-T7lQtGrfV00C_cMyX1PYGV6jJLF" target="_blank">Fraud Detection (Binary Classification)</a>

.. |NYC_notebook| raw:: html

    <a class="reference external" href="https://colab.research.google.com/drive/178SVfo89ni6iiC1jW_wJEYPz5znJi84j" target="_blank">NYC Taxi Dataset (Regression)</a>

.. |Movie_notebook| raw:: html

    <a class="reference external" href="https://colab.research.google.com/drive/1KrscEw_c371gegJxgu8kyC0V2TFj5nUO" target="_blank">Movie Recommendations (Ranking)</a>

.. |ArXiv_notebook| raw:: html

    <a class="reference external" href="https://colab.research.google.com/drive/1NMPgFnPdlvhJDLG0Jda0M_leJ6ClPVKD" target="_blank">ArXiv Dataset (NLP, Classification)</a>

.. |Animals_notebook| raw:: html

    <a class="reference external" href="https://colab.research.google.com/drive/1MxAiaf5OvTcT0csQUNN0Z4pjrjxnz-Ew" target="_blank">Animals with Attributes 2 (Images, Classification)</a>