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/guides/frameworks/keras/jupyter.md b/docs/guides/frameworks/keras/jupyter.md index 005e2414..bbb3864a 100644 --- a/docs/guides/frameworks/keras/jupyter.md +++ b/docs/guides/frameworks/keras/jupyter.md @@ -18,22 +18,26 @@ The example does the following: The loss and accuracy metric scalar plots appear in **SCALARS**, along with the resource utilization plots, which are titled **:monitor: machine**. - + + ## Plots The example calls Matplotlib methods to create several sample plots, and TensorBoard methods to plot histograms for layer density. They appear in **PLOTS**. - + + - + + ## Debug Samples The example calls Matplotlib methods to log debug sample images. They appear in **DEBUG SAMPLES**. - + + ## Hyperparameters @@ -55,17 +59,20 @@ task_params['hidden_dim'] = 512 Parameter dictionaries appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **General**. - + + The TensorFlow Definitions appear in the **TF_DEFINE** subsection. - + + ## Console Text printed to the console for training appears in **CONSOLE**. - + + ## Artifacts @@ -74,9 +81,11 @@ created using Keras. The task info panel shows model tracking, including the model name and design in **ARTIFACTS** **>** **Output Model**. - + + Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view the model's details and access the model. - \ No newline at end of file + + \ No newline at end of file diff --git a/docs/guides/frameworks/keras/keras_tensorboard.md b/docs/guides/frameworks/keras/keras_tensorboard.md index f07f2c5e..fc9cfb6c 100644 --- a/docs/guides/frameworks/keras/keras_tensorboard.md +++ b/docs/guides/frameworks/keras/keras_tensorboard.md @@ -25,31 +25,36 @@ The example script does the following: The loss and accuracy metric scalar plots appear in **SCALARS**, along with the resource utilization plots, which are titled **:monitor: machine**. - + + ## Histograms Histograms for layer density appear in **PLOTS**. - + + ## Hyperparameters -ClearML automatically logs command line options generated with `argparse`, and TensorFlow Definitions. +ClearML automatically logs command line options generated with `argparse` and TensorFlow Definitions. Command line options appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **Args**. - + + TensorFlow Definitions appear in **TF_DEFINE**. - + + ## Console Text printed to the console for training progress, as well as all other console output, appear in **CONSOLE**. - + + ## Configuration Objects @@ -64,4 +69,5 @@ task.connect_configuration( It appears in **CONFIGURATION** **>** **CONFIGURATION OBJECTS** **>** **MyConfig**. - \ No newline at end of file + + \ No newline at end of file diff --git a/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md b/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md index 5d6e7a19..39b2f71d 100644 --- a/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md +++ b/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md @@ -12,16 +12,19 @@ and `matplotlib` to create a scatter diagram. When the script runs, it creates a ClearML automatically logs the scatter plot, which appears in the [task's page](../../../webapp/webapp_exp_track_visual.md) in the ClearML web UI, under **PLOTS**. - + + ## Artifacts Models created by the task appear in the task's **ARTIFACTS** tab. - + + Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view the model's details and access the model. - \ No newline at end of file + + \ No newline at end of file diff --git a/docs/guides/frameworks/tensorboardx/tensorboardx.md b/docs/guides/frameworks/tensorboardx/tensorboardx.md index 57dae9de..a2b2fd47 100644 --- a/docs/guides/frameworks/tensorboardx/tensorboardx.md +++ b/docs/guides/frameworks/tensorboardx/tensorboardx.md @@ -16,30 +16,35 @@ The script does the following: The loss and accuracy metric scalar plots appear in the task's page in the **ClearML web UI**, under **SCALARS**. The also includes resource utilization plots, which are titled **:monitor: machine**. - + + ## Hyperparameters ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **Args**. - + + ## Console Text printed to the console for training progress, as well as all other console output, appear in **CONSOLE**. - + + ## Artifacts Models created by the task appear in the task's **ARTIFACTS** tab. ClearML automatically logs and tracks models and any snapshots created using PyTorch. - + + Clicking on the model's name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view the model's details and access the model. - + + diff --git a/docs/guides/frameworks/tensorboardx/video_tensorboardx.md b/docs/guides/frameworks/tensorboardx/video_tensorboardx.md index 258e1eb1..fa909233 100644 --- a/docs/guides/frameworks/tensorboardx/video_tensorboardx.md +++ b/docs/guides/frameworks/tensorboardx/video_tensorboardx.md @@ -14,5 +14,6 @@ the `examples` project. ClearML automatically captures the video data that is added to the `SummaryWriter` object, using the `add_video` method. The video appears in the task's **DEBUG SAMPLES** tab. - + + diff --git a/docs/guides/frameworks/tensorflow/integration_keras_tuner.md b/docs/guides/frameworks/tensorflow/integration_keras_tuner.md index 5db4d120..8fbba5fb 100644 --- a/docs/guides/frameworks/tensorflow/integration_keras_tuner.md +++ b/docs/guides/frameworks/tensorflow/integration_keras_tuner.md @@ -44,28 +44,33 @@ When the script runs, it logs: ClearML logs the scalars from training each network. They appear in the task's page in the **ClearML web UI**, under **SCALARS**. - + + ## Summary of Hyperparameter Optimization ClearML automatically logs the parameters of each task run in the hyperparameter search. They appear in tabular form in **PLOTS**. - + + ## Artifacts ClearML automatically stores the output model. It appears in **ARTIFACTS** **>** **Output Model**. - + + Model details, such as snap locations, appear in the **MODELS** tab. - + + The model configuration is stored with the model. - + + ## Configuration Objects @@ -73,12 +78,14 @@ The model configuration is stored with the model. ClearML automatically logs the TensorFlow Definitions, which appear in **CONFIGURATION** **>** **HYPERPARAMETERS**. - + + ### Configuration The Task configuration appears in **CONFIGURATION** **>** **General**. - + + diff --git a/docs/guides/frameworks/tensorflow/tensorboard_pr_curve.md b/docs/guides/frameworks/tensorflow/tensorboard_pr_curve.md index 3a62f229..24145b65 100644 --- a/docs/guides/frameworks/tensorflow/tensorboard_pr_curve.md +++ b/docs/guides/frameworks/tensorflow/tensorboard_pr_curve.md @@ -20,24 +20,29 @@ In the **ClearML Web UI**, the PR Curve summaries appear in the task's page unde * Blue PR curves -  +  +  * Green PR curves -  +  +  * Red PR curves -  +  +  ## Hyperparameters ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **TF_DEFINE**. - + + ## Console -All other console output appears in **CONSOLE**. +All console output appears in **CONSOLE** tab. - + + diff --git a/docs/guides/frameworks/tensorflow/tensorboard_toy.md b/docs/guides/frameworks/tensorflow/tensorboard_toy.md index d112a4b9..bd622df6 100644 --- a/docs/guides/frameworks/tensorflow/tensorboard_toy.md +++ b/docs/guides/frameworks/tensorflow/tensorboard_toy.md @@ -14,25 +14,29 @@ project. The `tf.summary.scalar` output appears in the ClearML web UI, in the task's **SCALARS**. Resource utilization plots, which are titled **:monitor: machine**, also appear in the **SCALARS** tab. - + + ## Plots The `tf.summary.histogram` output appears in **PLOTS**. - + + ## Debug Samples ClearML automatically tracks images and text output to TensorFlow. They appear in **DEBUG SAMPLES**. - + + ## Hyperparameters ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **TF_DEFINE**. - + + diff --git a/docs/guides/frameworks/tensorflow/tensorflow_mnist.md b/docs/guides/frameworks/tensorflow/tensorflow_mnist.md index 263e7da6..a4afd8f9 100644 --- a/docs/guides/frameworks/tensorflow/tensorflow_mnist.md +++ b/docs/guides/frameworks/tensorflow/tensorflow_mnist.md @@ -13,30 +13,35 @@ When the script runs, it creates a task named `Tensorflow v2 mnist with summarie The loss and accuracy metric scalar plots appear in the task's page in the **ClearML web UI** under **SCALARS**. Resource utilization plots, which are titled **:monitor: machine**, also appear in the **SCALARS** tab. - + + ## Hyperparameters ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **TF_DEFINE**. - + + ## Console All console output appears in **CONSOLE**. - + + ## Artifacts Models created by the task appear in the task's **ARTIFACTS** tab. ClearML automatically logs and tracks models and any snapshots created using TensorFlow. - + + Clicking on a model's name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view the model's details and access the model. - \ No newline at end of file + + \ No newline at end of file diff --git a/docs/guides/frameworks/xgboost/xgboost_metrics.md b/docs/guides/frameworks/xgboost/xgboost_metrics.md index 93886191..384398e2 100644 --- a/docs/guides/frameworks/xgboost/xgboost_metrics.md +++ b/docs/guides/frameworks/xgboost/xgboost_metrics.md @@ -13,7 +13,8 @@ the `examples` project. ClearML automatically captures scalars logged with XGBoost, which can be visualized in plots in the ClearML WebApp, in the task's **SCALARS** tab. - + + ## Models @@ -21,14 +22,17 @@ ClearML automatically captures the model logged using the `xgboost.save` method, View saved snapshots in the task's **ARTIFACTS** tab. - + + To view the model details, click the model name in the **ARTIFACTS** page, which will open the model's info tab. Alternatively, download the model. - + + ## Console All console output during the script's execution appears in the task's **CONSOLE** page. - \ No newline at end of file + + \ No newline at end of file diff --git a/docs/guides/frameworks/xgboost/xgboost_sample.md b/docs/guides/frameworks/xgboost/xgboost_sample.md index cc861cca..3d0f349d 100644 --- a/docs/guides/frameworks/xgboost/xgboost_sample.md +++ b/docs/guides/frameworks/xgboost/xgboost_sample.md @@ -18,25 +18,30 @@ classification dataset using XGBoost The feature importance plot and tree plot appear in the task's page in the **ClearML web UI**, under **PLOTS**. - + + - + + ## Console All other console output appear in **CONSOLE**. - + + ## Artifacts Models created by the task appear in the task's **ARTIFACTS** tab. 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b0392287..09087091 100644 Binary files a/docs/img/keras_colab_01.png and b/docs/img/keras_colab_01.png differ diff --git a/docs/img/keras_colab_01_dark.png b/docs/img/keras_colab_01_dark.png new file mode 100644 index 00000000..304712c5 Binary files /dev/null and b/docs/img/keras_colab_01_dark.png differ diff --git a/docs/img/keras_colab_02.png b/docs/img/keras_colab_02.png index f0efa89e..9055afb1 100644 Binary files a/docs/img/keras_colab_02.png and b/docs/img/keras_colab_02.png differ diff --git a/docs/img/keras_colab_02_dark.png b/docs/img/keras_colab_02_dark.png new file mode 100644 index 00000000..8395f2e8 Binary files /dev/null and b/docs/img/keras_colab_02_dark.png differ 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 3d5caa09..fe0cbb18 100644 --- 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). - + + ## 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   diff --git a/docs/integrations/click.md b/docs/integrations/click.md index c1169615..995fdd2c 100644 --- 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. - + + 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. diff --git a/docs/integrations/fastai.md b/docs/integrations/fastai.md index aabc00c9..332bf75e 100644 --- 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). - + + ## 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   diff --git a/docs/integrations/hydra.md b/docs/integrations/hydra.md index faaa41b0..306698fd 100644 --- 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. - + + ## Modifying Hydra Values 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/keras_tuner.md b/docs/integrations/keras_tuner.md index 705526b8..55960684 100644 --- 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. - + + ClearML automatically logs the parameters of each task run in the hyperparameter search. They appear in tabular form in the task's **PLOTS**. - + + ClearML automatically stores the output model. It appears in the task's **ARTIFACTS** **>** **Output Model**. - + + ## Example 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 5a9184d3..24b227f3 100644 --- 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). - + + ## 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   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/tensorboard.md b/docs/integrations/tensorboard.md index 317c983f..d002c582 100644 --- 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. - + + - + + ## Automatic Logging Control By default, when ClearML is integrated into your script, it captures all of your TensorBoard plots, images, and metrics. diff --git a/docs/integrations/tensorboardx.md b/docs/integrations/tensorboardx.md index 673b2c7b..0adc7557 100644 --- 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. - + + ## Automatic Logging Control By default, when ClearML is integrated into your script, it captures all of your TensorboardX plots, images, metrics, videos, and text. 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 831c379c..44dcbde0 100644 --- 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). - + + ## 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   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