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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. 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: **PRs are always welcomed** :heart:
Good PR examples Good PR examples:
* If you see something that is inaccurate or missing * If you see something that is inaccurate or missing
* A topic that interests you is not addressed * A topic that interests you is not addressed
* You feel that a guide would have made your life easier * You feel that a guide would have made your life easier

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docs/getting_started/mlops/mlops_best_practices.md
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## Manage Your Data ## Manage Your Data
Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction. 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). 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 ## Scale Your Work
Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (remote or local) and manage 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. 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 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 ## Set up an Agent

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docs/guides/clearml_agent/reproduce_exp.md
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1. Go to the experiment page of the task you want to reproduce in the [ClearML WebApp](../../webapp/webapp_overview.md), 1. Go to the experiment page of the task you want to reproduce in the [ClearML WebApp](../../webapp/webapp_overview.md),
:::tip :::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. 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 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 :::tip
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instructions. instructions.
::: :::
[Monai](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare [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), imaging. You can integrate ClearML into your code using MONAI's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler),
and [`ModelCheckpoint`](#modelcheckpoint). and [`ModelCheckpoint`](#modelcheckpoint).
## ClearMLImageHandler and ClearMLStatsHandler ## ClearMLImageHandler and ClearMLStatsHandler