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109 lines
4.5 KiB
Markdown
109 lines
4.5 KiB
Markdown
---
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title: Monai
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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[Monai](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare
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imaging. You can integrate ClearML into your code using Monai's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler),
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and [`ModelCheckpoint`](#modelcheckpoint).
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## ClearMLImageHandler and ClearMLStatsHandler
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Use the `ClearMLImageHandler` and the `ClearMLStatsHandler` to log images and metrics respectively to ClearML.
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`ClearMLImageHandler` extends all functionality from [`TensorBoardImageHandler`](https://docs.monai.io/en/latest/handlers.html#monai.handlers.TensorBoardImageHandler),
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used for visualizing images, labels, and outputs. `ClearMLStatsHandler` extends all functionality from [`TensorBoardStatsHandler`](https://docs.monai.io/en/latest/handlers.html#monai.handlers.TensorBoardStatsHandler),
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which is used to define a set of Ignite Event handlers for TensorBoard logic. ClearML automatically captures all
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TensorBoard outputs.
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For example, the code below attaches the ClearML image handler and stats handler to an Ignite evaluator engine:
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```python
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# ignite evaluator
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evaluator = ignite.engine.create_supervised_evaluator(
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net,
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val_metrics,
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device,
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True,
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output_transform=lambda x, y, y_pred: (
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[post_pred(i) for i in decollate_batch(y_pred)],
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[post_label(i) for i in decollate_batch(y)],
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),
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)
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# add handler to draw the first image and the corresponding
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# label and model output in the last batch
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# here we draw the 3D output as GIF format along Depth
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# axis, at every validation epoch
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val_clearml_image_handler = ClearMLImageHandler(
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task_name=task_name,
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project_name=project_name,
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log_dir=log_dir,
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batch_transform=lambda batch: (batch[0], batch[1]),
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output_transform=lambda output: output[0],
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global_iter_transform=lambda x: trainer.state.epoch,
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)
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evaluator.add_event_handler(
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event_name=ignite.engine.Events.EPOCH_COMPLETED,
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handler=val_clearml_image_handler,
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)
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# add handler to record metrics to clearml at every validation epoch
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val_clearml_stats_handler = ClearMLStatsHandler(
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log_dir=log_dir,
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# no need to plot loss value, so disable per iteration output
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output_transform=lambda x: None,
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# fetch global epoch number from trainer
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global_epoch_transform=lambda x: trainer.state.epoch,
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)
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val_clearml_stats_handler.attach(evaluator)
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```
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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
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change the task's name or project, use the `task_name` and `project_name` parameters when instantiating either handler.
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The task captures the images logged by the image handler, metrics logged with the stats handler, as well as source code,
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uncommitted changes, installed packages, console output, and more.
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You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
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View the logged images in the WebApp, in the experiment's **Debug Samples** tab.
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View the logged metrics in the WebApp, in the experiment's **Scalars** tab.
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## ModelCheckpoint
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ClearML automatically logs models saved using the `ModelCheckpoint` handler. Make sure a ClearML Task is instantiated in
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your script. If you're already using either `ClearMLStatsHandler` or `ClearMLImageHandler`, you don't have to add any code.
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Otherwise, all you have to is add two lines of code to create a task:
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```python
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from clearml import Task
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task = Task.init(task_name="<task name>", project_name="<project_name>")
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```
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The code below creates a handler to save models. When the script runs with a ClearML task, the task automatically
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captures the saved models.
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```python
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# adding checkpoint handler to save models (network
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# params and optimizer stats) during training and log them to ClearML
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log_dir = os.path.join(root_dir, "logs")
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checkpoint_handler = ignite.handlers.ModelCheckpoint(log_dir, "net", n_saved=10, require_empty=False)
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trainer.add_event_handler(
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event_name=ignite.engine.Evclearents.EPOCH_COMPLETED,
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handler=checkpoint_handler,
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to_save={"net": net, "opt": opt},
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)
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```
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View the captured models in the WebApp, in the experiment's **Artifacts** tab.
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