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docs/configs/clearml_conf.md
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@ -151,7 +151,7 @@ Compatible with Docker versions 0.6.5 and above
**`agent.docker_install_opencv_libs`** (*bool*)
* Install the required packages for opencv libraries (libsm6 libxext6 libxrender-dev libglib2.0-0), for backwards
* Install the required packages for opencv libraries (`libsm6 libxext6 libxrender-dev libglib2.0-0`), for backwards
compatibility reasons. Change to `false` to skip installation and decrease docker spin-up time.
---

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docs/deploying_clearml/clearml_server_aws_ec2_ami.md
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@ -24,7 +24,7 @@ By default, ClearML Server deploys as an open network. To restrict ClearML Serve
in the [Security](clearml_server_security.md) page.
:::
The minimum recommended amount of RAM is 8 GB. For example, a t3.large or t3a.large EC2 instance type would accommodate the recommended RAM size.
The minimum recommended amount of RAM is 8 GB. For example, a `t3.large` or `t3a.large` EC2 instance type would accommodate the recommended RAM size.
**To launch a ClearML Server AWS community AMI**, use one of the [ClearML Server AWS community AMIs](#clearml-server-aws-community-amis)
and see:

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docs/integrations/monai.md
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@ -14,7 +14,7 @@ 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,
`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.

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docs/pipelines/pipelines.md
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@ -35,7 +35,7 @@ example of a pipeline with concurrent steps.
ClearML supports multiple modes for pipeline execution:
* **Remote Mode** (default) - In this mode, the pipeline controller logic is executed through a designated queue, and all
the pipeline steps are launched remotely through their respective queues. Since each task is executed independently,
it can have control over its git repository (if needed), required python packages and specific container to be used.
it can have control over its git repository (if needed), required python packages, and the specific container to use.
* **Local Mode** - In this mode, the pipeline is executed locally, and the steps are executed as sub-processes. Each
subprocess uses the exact same Python environment as the main pipeline logic.
* **Debugging Mode** (for PipelineDecorator) - In this mode, the entire pipeline is executed locally, with the pipeline