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README.md
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The [ClearML documentation website](https://clear.ml/docs/latest/docs) is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator.
## Contributing (yes please!)
## Contributing (yes, please!)
**PRs are always welcomed** :heart:
Good PR examples
Good PR examples:
* If you see something that is inaccurate or missing
* A topic that interests you is not addressed
* You feel that a guide would have made your life easier

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docs/faq.md
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with a single point, x-axis for the hyperparameter value, and Y-axis for the accuracy.
In the code below, the task reports a single-point scatter plot with `number_layers` as the x-axis and
`accuracy` as the Y-axis :
`accuracy` as the Y-axis:
```python
number_layers = 10

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docs/getting_started/mlops/mlops_best_practices.md
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@ -31,7 +31,7 @@ Once you have a Task in ClearML, you can clone and edit its definitions in the U
## Manage Your Data
Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction.
Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder / S3 / Gs / Azure).
ClearML Data supports efficient Dataset storage and caching, differentiable & compressed.
ClearML Data supports efficient Dataset storage and caching, differentiable and compressed.
## Scale Your Work
Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (remote or local) and manage

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docs/getting_started/mlops/mlops_first_steps.md
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machine of choice through the ClearML WebApp with no need for additional code.
The agent will set up the environment for a specific Task’s execution (inside a Docker, or bare-metal), install the
required python packages, and execute & monitor the process.
required python packages, and execute and monitor the process.
## Set up an Agent

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docs/guides/clearml_agent/reproduce_exp.md
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@ -18,13 +18,13 @@ Sometimes, you may need to recreate your experiment environment on a different m
code.
ClearML logs everything needed to reproduce your experiment and its environment (uncommitted changes, used packages, and
more), making it easy to reproduce your experiment's execution environment using ClearML.
more), making it easy to reproduce your experiment's execution environment using ClearML.
You can reproduce the execution environment of any experiment you’ve run with ClearML on any workload:
1. Go to the experiment page of the task you want to reproduce in the [ClearML WebApp](../../webapp/webapp_overview.md),
:::tip
Use the UI's [filtering and sorting](../../webapp/webapp_exp_table.md#filtering-columns) to find the best performing experiments
Use the UI's [filtering and sorting](../../webapp/webapp_exp_table.md#filtering-columns) to find the best performing experiments.
:::
1. Copy the desired experiment's ID
1. Use the ClearML Agent's [`build`](../../clearml_agent/clearml_agent_ref.md#build) command to rebuild the experiment's

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docs/integrations/monai.md
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---
title: Monai
title: MONAI
---
:::tip
@ -7,8 +7,8 @@ If you are not already using ClearML, see [Getting Started](../getting_started/d
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),
[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

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docs/webapp/webapp_orchestration_dash.md
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The orchestration dashboard shows your workers by groups and categories, specified by the following naming
policy: `<category>:<group>:<name>`.
When no category is specified, workers are assigned the `DEFAULT` category.
When no category is specified, workers are assigned the `DEFAULT` category.
When no group is specified, workers are assigned the `Default Group` group.