diff --git a/docs/clearml_agent.md b/docs/clearml_agent.md index 0750c4b8..768b8e9a 100644 --- a/docs/clearml_agent.md +++ b/docs/clearml_agent.md @@ -17,7 +17,7 @@ title: ClearML Agent **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: -- Reproducing tasks, including their complete environments. +- Reproducing task runs, including their complete environments. - Scaling workflows on multiple target machines. ClearML Agent executes a task or other workflow by reproducing the state of the code from the original machine @@ -46,7 +46,7 @@ install Python, so make sure to use a container or environment with the version While the agent is running, it continuously reports system metrics to the ClearML Server (these can be monitored in the [**Orchestration**](webapp/webapp_workers_queues.md) page). -Continue using ClearML Agent once it is running on a target machine. Reproduce tasks and execute +Continue using ClearML Agent once it is running on a target machine. Reproducing task runs and execute automated workflows in one (or both) of the following ways: * Programmatically (using [`Task.enqueue()`](references/sdk/task.md#taskenqueue) or [`Task.execute_remotely()`](references/sdk/task.md#execute_remotely)) * Through the ClearML Web UI (without working directly with code), by cloning tasks and enqueuing them to the diff --git a/docs/getting_started/remote_execution.md b/docs/getting_started/remote_execution.md index 3f7fab5f..dbb98ce6 100644 --- a/docs/getting_started/remote_execution.md +++ b/docs/getting_started/remote_execution.md @@ -14,7 +14,7 @@ powerful remote machine. This is useful for: * Managing execution through ClearML's queue system. This guide focuses on transitioning a locally executed process to a remote machine for scalable execution. To learn how -to reproduce a previously executed process on a remote machine, see [Reproducing Tasks](reproduce_tasks.md). +to reproduce a previously executed process on a remote machine, see [Reproducing Task Runs](reproduce_tasks.md). ## Running a Task Remotely diff --git a/docs/getting_started/reproduce_tasks.md b/docs/getting_started/reproduce_tasks.md index 57bb1a98..4f73077b 100644 --- a/docs/getting_started/reproduce_tasks.md +++ b/docs/getting_started/reproduce_tasks.md @@ -1,5 +1,5 @@ --- -title: Reproducing Tasks +title: Reproducing Task Runs --- :::note diff --git a/docs/integrations/autokeras.md b/docs/integrations/autokeras.md index ece401ad..86e9d7ed 100644 --- 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   diff --git a/docs/integrations/catboost.md b/docs/integrations/catboost.md index fb2841ce..fe0cbb18 100644 --- a/docs/integrations/catboost.md +++ b/docs/integrations/catboost.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   diff --git a/docs/integrations/fastai.md b/docs/integrations/fastai.md index e5ff1653..332bf75e 100644 --- a/docs/integrations/fastai.md +++ b/docs/integrations/fastai.md @@ -75,7 +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. @@ -91,7 +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   diff --git a/docs/integrations/keras.md b/docs/integrations/keras.md index e1270272..c95a631c 100644 --- 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   diff --git a/docs/integrations/lightgbm.md b/docs/integrations/lightgbm.md index b611ac22..bbb0f487 100644 --- 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   diff --git a/docs/integrations/megengine.md b/docs/integrations/megengine.md index 6cbeb627..ab0fd305 100644 --- 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   diff --git a/docs/integrations/pytorch.md b/docs/integrations/pytorch.md index 39ee2f83..24b227f3 100644 --- a/docs/integrations/pytorch.md +++ b/docs/integrations/pytorch.md @@ -97,7 +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. @@ -113,7 +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   diff --git a/docs/integrations/pytorch_lightning.md b/docs/integrations/pytorch_lightning.md index 977c545e..e91b33ec 100644 --- 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   diff --git a/docs/integrations/scikit_learn.md b/docs/integrations/scikit_learn.md index 61049811..86d6b663 100644 --- 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   diff --git a/docs/integrations/tao.md b/docs/integrations/tao.md index 07a11249..63af9a85 100644 --- 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/  ## 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   diff --git a/docs/integrations/tensorflow.md b/docs/integrations/tensorflow.md index 2f175e7b..2613ff9a 100644 --- 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   diff --git a/docs/integrations/transformers.md b/docs/integrations/transformers.md index 0c0c9a5d..c6f4ff07 100644 --- 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. diff --git a/docs/integrations/xgboost.md b/docs/integrations/xgboost.md index 5a5b326f..44dcbde0 100644 --- a/docs/integrations/xgboost.md +++ b/docs/integrations/xgboost.md @@ -103,7 +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. @@ -119,7 +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   diff --git a/docs/integrations/yolov5.md b/docs/integrations/yolov5.md index 1f353427..d574a184 100644 --- 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   diff --git a/docs/integrations/yolov8.md b/docs/integrations/yolov8.md index b6091864..3ae329c9 100644 --- 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, diff --git a/docs/webapp/webapp_exp_reproducing.md b/docs/webapp/webapp_exp_reproducing.md index 83dbec2b..308a1e10 100644 --- a/docs/webapp/webapp_exp_reproducing.md +++ b/docs/webapp/webapp_exp_reproducing.md @@ -1,8 +1,8 @@ --- -title: Reproducing Tasks +title: Reproducing Task Runs --- -Reproduce tasks on local or remote machines in one of the following ways: +Reproducing task runs on local or remote machines in one of the following ways: * Cloning any task - Make an exact copy, while maintaining the original task * Resetting a task whose status is not *Published* - Delete the previous run's logs and output