================ Getting Started ================ 💻 Installation ================= Check out our :doc:`Installation ` instructions to install it locally and continue from there. 🏃‍♀️ See It in Action ==================== For a quick start, check out the following examples in the tutorials section: Tabular Data ------------- - :doc:`Quickstart in 5 minutes ` Computer Vision ---------------- **Beta Release** - :doc:`Deepchecks Example - Simple Image Classification Tutorial ` - :doc:`Deepchecks for Object Detection Tutorial ` - :doc:`Deepchecks for Classification Tutorial` .. note:: Deepchecks' Computer Vision subpackage is in beta release. It is :doc:`available for installation ` from PyPi, use at your own discretion. `Github Issues `_ are welcome! 🧐 How Does it Work? ======================== Deepchecks is built of checks, each designated to help to identify a specific issue. Some checks relate only to the data and labels and some require also the model. Suites are composed of checks. Each check contains outputs to display in a notebook and/or conditions with a pass/fail/warning output. For more information about deepchecks structure and components head over to our :doc:`/user-guide/general/deepchecks_hierarchy` in the User Guide. 📊 Which Types of Checks Exist? ================================= Check out our :doc:`/checks_gallery/tabular/index` to see all the available checks for Tabular and :doc:`/checks_gallery/vision/index` for CV. They are divided in the following categories: - Data Integrity - Data Distribution - Methodology - Model Evaluation ❓ What Do You Need in Order to Start? ======================================= Depending on your phase and what you wish to validate, you'll need **a subset** of the following: - **Raw data** (before pre-processing such as OHE, string processing, etc.), with optional labels - The model's **training data with labels** - **Test data** (which the model isn't exposed to) with labels - | A **supported model** that you wish to validate. | For tabular data, see :doc:`supported models `. | For computer vision, we currently support the pytorch framework. See :doc:`/user-guide/vision/data-classes/index` to understand how to integrate your data. 🙋🏼 When Should You Use Deepchecks? ===================================== While you're in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. .. image:: /_static/pipeline_when_to_validate.svg :alt: When To Validate - ML Pipeline Schema :align: center See the :doc:`When Should You Use ` Section for an elaborate explanation of the typical scenarios. 👀 Viewing Check and Suite Results ===================================== The package's output can be consumed in various formats: - Viewed inline in Jupyter (default behavior) - :doc:`Exported as an HTML Report / JSON / Sent to W&B ` 🔢 Suported Data Types ========================= Deepchecks currently supports Tabular Data (:mod:`deepchecks.tabular`) and is in beta release for Computer Vision (:mod:`deepchecks.vision`).