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docs/clearml_agent.md
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@ -13,6 +13,8 @@ title: ClearML Agent
</iframe> </iframe>
</div> </div>
<br/>
**ClearML Agent** is a virtual environment and execution manager for DL / ML solutions on GPU machines. It integrates with the **ClearML Python Package** and ClearML Server to provide a full AI cluster solution. <br/> **ClearML Agent** is a virtual environment and execution manager for DL / ML solutions on GPU machines. It integrates with the **ClearML Python Package** and ClearML Server to provide a full AI cluster solution. <br/>
Its main focus is around: Its main focus is around:
- Reproducing experiments, including their complete environments. - Reproducing experiments, including their complete environments.
@ -386,7 +388,7 @@ You can set the docker container via the UI:
1. Clone the experiment 1. Clone the experiment
2. Set the Docker in the cloned task's **Execution** tab **> Container** section 2. Set the Docker in the cloned task's **Execution** tab **> Container** section
![Container section](../img/webapp_exp_container.png) ![Container section](img/webapp_exp_container.png)
3. Enqueue the cloned task 3. Enqueue the cloned task

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docs/getting_started/ds/best_practices.md
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@ -66,7 +66,7 @@ improving your results later on!
## Visibility Matters ## Visibility Matters
While it's possible to track experiments with one tool, and pipeline them with another, we believe that having While it's possible to track experiments with one tool, and pipeline them with another, having
everything under the same roof has its benefits! everything under the same roof has its benefits!
Being able to track experiment progress and compare experiments, and based on that send experiments to execution on remote Being able to track experiment progress and compare experiments, and based on that send experiments to execution on remote

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docs/integrations/fastai.md
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@ -86,7 +86,7 @@ following command on it:
clearml-agent daemon --queue <queues_to_listen_to> [--docker] clearml-agent daemon --queue <queues_to_listen_to> [--docker]
``` ```
Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md), to help you manage cloud workloads in the Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to help you manage cloud workloads in the
cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up
and shuts down instances as needed, according to a resource budget that you set. and shuts down instances as needed, according to a resource budget that you set.