Merge branch 'main' of https://github.com/allegroai/clearml-docs into edits_3

2025-06-26 18:17:44 +00:00 · 2025-03-11 18:03:33 +02:00
parent 9a6e6d18c1 f36b9be758
commit eceacf4ffe
141 changed files with 170 additions and 104 deletions
--- a/docs/integrations/autokeras.md
+++ b/docs/integrations/autokeras.md
@@ -77,7 +77,7 @@ See more information about explicitly logging information to a ClearML Task:
 See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -93,7 +93,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/catboost.md
+++ b/docs/integrations/catboost.md
@@ -31,7 +31,8 @@ You can view all the task details in the [WebApp](../webapp/webapp_overview.md).

 See an example of CatBoost and ClearML in action [here](../guides/frameworks/catboost/catboost.md).

-![Task scalars](../img/examples_catboost_scalars.png)
+![Task scalars](../img/examples_catboost_scalars.png#light-mode-only)
+![Task scalars](../img/examples_catboost_scalars_dark.png#dark-mode-only)

 ## Automatic Logging Control 
 By default, when ClearML is integrated into your CatBoost script, it captures models, and 
@@ -75,7 +76,7 @@ See more information about explicitly logging information to a ClearML Task:
 See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -91,7 +92,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/click.md
+++ b/docs/integrations/click.md
@@ -42,7 +42,8 @@ if __name__ == '__main__':
 When this code is executed, ClearML logs your command-line arguments, which you can view in the 
 [WebApp](../webapp/webapp_overview.md), in the task's **Configuration > Hyperparameters > Args** section. 

-![click configuration](../img/integrations_click_configs.png)
+![click configuration](../img/integrations_click_configs.png#light-mode-only)
+![click configuration](../img/integrations_click_configs_dark.png#dark-mode-only)

 In the UI, you can clone the task multiple times and set the clones' parameter values for execution by the [ClearML Agent](../clearml_agent.md).
 When the clone is executed, the executing agent will use the new parameter values as if set by the command-line.
--- a/docs/integrations/fastai.md
+++ b/docs/integrations/fastai.md
@@ -30,7 +30,8 @@ You can view all the task details in the [WebApp](../webapp/webapp_overview.md).

 See an example of `fastai` and ClearML in action [here](../guides/frameworks/fastai/fastai_with_tensorboard.md).

-![Task scalars](../img/examples_reporting_fastai_01.png)
+![Task scalars](../img/examples_reporting_fastai_01.png#light-mode-only)
+![Task scalars](../img/examples_reporting_fastai_01_dark.png#dark-mode-only)

 ## Automatic Logging Control 
 By default, when ClearML is integrated into your `fastai` script, it captures models and 
@@ -74,7 +75,7 @@ See more information about explicitly logging information to a ClearML Task:
 See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -90,7 +91,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/hydra.md
+++ b/docs/integrations/hydra.md
@@ -22,7 +22,8 @@ task = Task.init(task_name="<task_name>", project_name="<project_name>")
 ClearML logs the OmegaConf as a blob and can be viewed in the 
 [WebApp](../webapp/webapp_overview.md), in the task's **CONFIGURATION > CONFIGURATION OBJECTS > OmegaConf** section. 

-![Hydra configuration](../img/integrations_hydra_configs.png)
+![Hydra configuration](../img/integrations_hydra_configs.png#light-mode-only)
+![Hydra configuration](../img/integrations_hydra_configs_dark.png#dark-mode-only)

 ## Modifying Hydra Values

--- a/docs/integrations/keras.md
+++ b/docs/integrations/keras.md
@@ -87,7 +87,7 @@ and debug samples, plots, and scalars logged to TensorBoard


 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -103,7 +103,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/keras_tuner.md
+++ b/docs/integrations/keras_tuner.md
@@ -53,16 +53,19 @@ You can view all the task details in the [WebApp](../webapp/webapp_exp_track_vis

 ClearML logs the scalars from training each network. They appear in the task's **SCALARS** tab in the Web UI.

-![Optimization scalars](../img/integration_keras_tuner_06.png)
+![Optimization scalars](../img/integration_keras_tuner_06.png#light-mode-only)
+![Optimization scalars](../img/integration_keras_tuner_06_dark.png#dark-mode-only)

 ClearML automatically logs the parameters of each task run in the hyperparameter search. They appear in tabular 
 form in the task's **PLOTS**.

-![Optimization plot](../img/integration_keras_tuner_07.png)
+![Optimization plot](../img/integration_keras_tuner_07.png#light-mode-only)
+![Optimization plot](../img/integration_keras_tuner_07_dark.png#dark-mode-only)

 ClearML automatically stores the output model. It appears in the task's **ARTIFACTS** **>** **Output Model**.

-![output model](../img/integration_keras_tuner_03.png)
+![output model](../img/integration_keras_tuner_03.png#light-mode-only)
+![output model](../img/integration_keras_tuner_03_dark.png#dark-mode-only)

 ## Example 

--- a/docs/integrations/lightgbm.md
+++ b/docs/integrations/lightgbm.md
@@ -76,7 +76,7 @@ See more information about explicitly logging information to a ClearML Task:
 See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -92,7 +92,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/megengine.md
+++ b/docs/integrations/megengine.md
@@ -72,7 +72,7 @@ See more information about explicitly logging information to a ClearML Task:
 See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -88,7 +88,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/pytorch.md
+++ b/docs/integrations/pytorch.md
@@ -28,7 +28,8 @@ And that's it! This creates a [ClearML Task](../fundamentals/task.md) which capt

 You can view all the task details in the [WebApp](../webapp/webapp_overview.md). 

-![Pytorch webapp](../img/examples_pytorch_distributed_example_08.png)
+![Pytorch webapp](../img/examples_pytorch_distributed_example_08.png#light-mode-only)
+![Pytorch webapp](../img/examples_pytorch_distributed_example_08_dark.png#dark-mode-only)

 ## Automatic Logging Control 
 By default, when ClearML is integrated into your PyTorch script, it captures PyTorch models. But, you may want to have 
@@ -96,7 +97,7 @@ additional tools, like argparse, TensorBoard, and matplotlib:
 * [PyTorch Distributed](../guides/frameworks/pytorch/pytorch_distributed_example.md) - Demonstrates using ClearML with the [PyTorch Distributed Communications Package (`torch.distributed`)](https://pytorch.org/tutorials/beginner/dist_overview.html)

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -112,7 +113,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/pytorch_lightning.md
+++ b/docs/integrations/pytorch_lightning.md
@@ -102,7 +102,7 @@ See more information about explicitly logging information to a ClearML Task:
 See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -118,7 +118,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md), to
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/scikit_learn.md
+++ b/docs/integrations/scikit_learn.md
@@ -78,7 +78,7 @@ additional tools, like Matplotlib:


 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -94,7 +94,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/tao.md
+++ b/docs/integrations/tao.md
@@ -94,7 +94,7 @@ You can view all of this captured information in the [ClearML Web UI](../webapp/
 ![TAO UI plots](../img/integrations_nvidia_tao_plots.png)

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is 
 enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -111,7 +111,7 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents:
 and shuts down instances as needed, according to a resource budget that you set.


-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/tensorboard.md
+++ b/docs/integrations/tensorboard.md
@@ -22,9 +22,11 @@ uncommitted code, Python environment, your TensorBoard metrics, plots, images, a

 View the TensorBoard outputs in the [WebApp](../webapp/webapp_overview.md), in the task's page.

-![TensorBoard WebApp scalars](../img/examples_pytorch_tensorboard_07.png)
+![TensorBoard WebApp scalars](../img/examples_pytorch_tensorboard_07.png#light-mode-only)
+![TensorBoard WebApp scalars](../img/examples_pytorch_tensorboard_07_dark.png#dark-mode-only)

-![Tensorboard WebApp debug samples](../img/examples_tensorboard_toy_pytorch_02.png)
+![Tensorboard WebApp debug samples](../img/examples_tensorboard_toy_pytorch_02.png#light-mode-only)
+![Tensorboard WebApp debug samples](../img/examples_tensorboard_toy_pytorch_02_dark.png#dark-mode-only)

 ## Automatic Logging Control 
 By default, when ClearML is integrated into your script, it captures all of your TensorBoard plots, images, and metrics. 
--- a/docs/integrations/tensorboardx.md
+++ b/docs/integrations/tensorboardx.md
@@ -22,7 +22,8 @@ uncommitted code, Python environment, your TensorboardX metrics, plots, images,

 View the TensorboardX outputs in the [WebApp](../webapp/webapp_overview.md), in the task's page.

-![TensorboardX WebApp scalars](../img/examples_pytorch_tensorboardx_03.png)
+![TensorboardX WebApp scalars](../img/examples_pytorch_tensorboardx_03.png#light-mode-only)
+![TensorboardX WebApp scalars](../img/examples_pytorch_tensorboardx_03_dark.png#dark-mode-only)

 ## Automatic Logging Control 
 By default, when ClearML is integrated into your script, it captures all of your TensorboardX plots, images, metrics, videos, and text. 
--- a/docs/integrations/tensorflow.md
+++ b/docs/integrations/tensorflow.md
@@ -89,7 +89,7 @@ TensorBoard scalars, histograms, images, and text, as well as all console output
 ClearML's automatic logging of parameters defined using `absl.flags` 

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -105,7 +105,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/transformers.md
+++ b/docs/integrations/transformers.md
@@ -60,7 +60,7 @@ You can also select multiple tasks and directly [compare](../webapp/webapp_exp_c
 See an example of Transformers and ClearML in action [here](../guides/frameworks/huggingface/transformers.md). 

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is 
 enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
--- a/docs/integrations/xgboost.md
+++ b/docs/integrations/xgboost.md
@@ -51,7 +51,8 @@ except ImportError:

 You can view all the task details in the [WebApp](../webapp/webapp_overview.md). 

-![Task scalars](../img/examples_xgboost_metric_scalars.png)
+![Task scalars](../img/examples_xgboost_metric_scalars.png#light-mode-only)
+![Task scalars](../img/examples_xgboost_metric_scalars_dark.png#dark-mode-only)

 ## Automatic Logging Control 
 By default, when ClearML is integrated into your XGBoost script, it captures models, and 
@@ -102,7 +103,7 @@ additional tools, like Matplotlib and scikit-learn:
 * [XGBoost and scikit-learn](../guides/frameworks/xgboost/xgboost_sample.md) - Demonstrates ClearML automatic logging of XGBoost scalars and models 

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is enqueued, 
 the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -118,7 +119,7 @@ Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to h
 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.

-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/yolov5.md
+++ b/docs/integrations/yolov5.md
@@ -150,7 +150,7 @@ python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_i


 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is 
 enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -167,7 +167,7 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents:
 and shuts down instances as needed, according to a resource budget that you set.


-### Reproducing Tasks
+### Reproducing Task Runs

 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
 ![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
--- a/docs/integrations/yolov8.md
+++ b/docs/integrations/yolov8.md
@@ -95,7 +95,7 @@ Add custom columns to the table, such as mAP values, so you can easily sort and
 You can also select multiple tasks and directly [compare](../webapp/webapp_exp_comparing.md) them.   

 ## Remote Execution
-ClearML logs all the information required to reproduce a task on a different machine (installed packages, 
+ClearML logs all the information required to reproduce a task run on a different machine (installed packages, 
 uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is 
 enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
 task manager.
@@ -112,9 +112,9 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents:
 shuts down instances as needed, according to a resource budget that you set.


-### Reproducing Tasks
+### Reproducing Task Runs

-ClearML logs all the information required to reproduce a task, but you may also want to change a few parameters 
+ClearML logs all the information required to reproduce a task run, but you may also want to change a few parameters 
 and task details when you re-run it, which you can do through ClearML's UI.

 In order to be able to override parameters via the UI,