diff --git a/docs/apps/clearml_session.md b/docs/apps/clearml_session.md index 386d32ea..549a0b32 100644 --- a/docs/apps/clearml_session.md +++ b/docs/apps/clearml_session.md @@ -169,7 +169,7 @@ clearml-session --upload-files /mnt/data/stuff You can upload your files in conjunction with the `--store-workspace` option to easily move workloads between local development machines and remote machines with persistent workspace synchronization. See [Storing and Synchronizing Workspace](#storing-and-synchronizing-workspace). -### Starting a Debugging Session +### Starting a Debugging Session You can debug previously executed tasks registered in the ClearML system on a remote interactive session. Input into `clearml-session` the ID of a Task to debug, then `clearml-session` clones the task's git repository and replicates the environment on a remote machine. Then the code can be interactively executed and debugged on JupyterLab / VS Code. diff --git a/docs/clearml_data/clearml_data.md b/docs/clearml_data/clearml_data.md index 4fed1e58..de127ce7 100644 --- a/docs/clearml_data/clearml_data.md +++ b/docs/clearml_data/clearml_data.md @@ -11,10 +11,11 @@ In Machine Learning, you are very likely dealing with a gargantuan amount of dat which you then need to be able to share, reproduce, and track. ClearML Data Management solves two important challenges: -- Accessibility - Making data easily accessible from every machine, +- Accessibility - Making data easily accessible from every machine - Versioning - Linking data and tasks for better **traceability**. -![Dataset lineage and preview](../img/webapp_dataset_lineage_preview.png) +![Dataset preview](../img/webapp_dataset_preview.png#light-mode-only) +![Dataset preview](../img/webapp_dataset_preview_dark.png#dark-mode-only) **We believe Data is not code**. It should not be stored in a git tree, because progress on datasets is not always linear. Moreover, it can be difficult and inefficient to find on a git tree the commit associated with a certain version of a dataset. diff --git a/docs/clearml_sdk/task_sdk.md b/docs/clearml_sdk/task_sdk.md index 25ced7d7..b8eaec25 100644 --- a/docs/clearml_sdk/task_sdk.md +++ b/docs/clearml_sdk/task_sdk.md @@ -253,7 +253,8 @@ task's status. If a task failed or was aborted, you can view how much progress i
-![Task table progress indication](../img/fundamentals_task_progress.png) +![Task table progress indication](../img/fundamentals_task_progress.png#light-mode-only) +![Task table progress indication](../img/fundamentals_task_progress_dark.png#dark-mode-only)
diff --git a/docs/guides/clearml_agent/executable_exp_containers.md b/docs/guides/clearml_agent/executable_exp_containers.md index 910fb819..e0a45063 100644 --- a/docs/guides/clearml_agent/executable_exp_containers.md +++ b/docs/guides/clearml_agent/executable_exp_containers.md @@ -3,7 +3,7 @@ title: Executable Task Containers --- This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)'s [`build`](../../clearml_agent/clearml_agent_ref.md#build) -command to package a task into an executable container. In this example, you will build a Container image that, when +command to package a task into an executable container. 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+ ![Run info](../../img/webapp_pipeline_run_info.png#light-mode-only) ![Run info](../../img/webapp_pipeline_run_info_dark.png#dark-mode-only) +
+ To view a run's complete information, click **Full details**, which will open the pipeline's controller [task page](../webapp_exp_track_visual.md). View each list's complete details in the pipeline task's corresponding tabs: * **PARAMETERS** list > **CONFIGURATION** tab diff --git a/docs/webapp/webapp_model_table.md b/docs/webapp/webapp_model_table.md index 791e6618..0f0c57cb 100644 --- a/docs/webapp/webapp_model_table.md +++ b/docs/webapp/webapp_model_table.md @@ -44,7 +44,7 @@ The model table contains the following columns: | *Metadata* | User defined metadata key column. Available options depend upon the models in the table. | String | -## Customizing the Model table +## Customizing the Model Table The model table is customizable. Changes are persistent (cached in the browser) and represented in the URL, so customized settings can be saved in a browser bookmark and shared with other ClearML users to collaborate.