If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
[Monai](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare
imaging. You can integrate ClearML into your code using Monai's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler),
and [`ModelCheckpoint`](#modelcheckpoint).
## ClearMLImageHandler and ClearMLStatsHandler
Use the `ClearMLImageHandler` and the `ClearMLStatsHandler` to log images and metrics respectively to ClearML.
`ClearMLImageHandler` extends all functionality from [`TensorBoardImageHandler`](https://docs.monai.io/en/latest/handlers.html#monai.handlers.TensorBoardImageHandler),
used for visualizing images, labels, and outputs. `ClearMLStatsHandler` extends all functionality from [`TensorBoardStatsHandler`](https://docs.monai.io/en/latest/handlers.html#monai.handlers.TensorBoardStatsHandler),
which is used to define a set of Ignite Event handlers for TensorBoard logic. ClearML automatically captures all
TensorBoard outputs.
For example, the code below attaches the ClearML image handler and stats handler to an Ignite evaluator engine:
When the code runs with either of these handlers, it creates a [ClearML Task](../fundamentals/task.md) called `monai_experiment` in the `MONAI` project. To
change the task's name or project, use the `task_name` and `project_name` parameters when instantiating either handler.
The task captures the images logged by the image handler, metrics logged with the stats handler, as well as source code,
uncommitted changes, installed packages, console output, and more.
You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
View the logged images in the WebApp, in the experiment's **Debug Samples** tab.