Getting Started#
💻 Installation#
Check out our 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#
Computer Vision#
Beta Release
Note
Deepchecks’ Computer Vision subpackage is in beta release. It is 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 Deepchecks Hierarchy in the User Guide.
📊 Which Types of Checks Exist?#
Check out our Tabular Checks to see all the available checks for Tabular and Vision Checks 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 supported models.For computer vision, we currently support the pytorch framework. See Data Classes 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.
See the 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)
🔢 Suported Data Types#
Deepchecks currently supports Tabular Data (deepchecks.tabular) and is in beta release for Computer Vision (deepchecks.vision).