diff --git a/docs/apps/clearml_param_search.md b/docs/apps/clearml_param_search.md index dca18f2f..38ed9ae0 100644 --- a/docs/apps/clearml_param_search.md +++ b/docs/apps/clearml_param_search.md @@ -26,7 +26,7 @@ of the optimization results in table and graph forms. |---|----|---| |`--project-name`|Name of the project in which the optimization task will be created. If the project does not exist, it is created. If unspecified, the repository name is used.|Yes| |`--task-name`|Name of the optimization task. If unspecified, the base Python script's file name is used.|Yes| -|`--task-id`|ID of an existing ClearML task whose hyperparameters will be optimized. Required unless `--script` is specified.|Yes| +|`--task-id`|ID of a ClearML task whose hyperparameters will be optimized. Required unless `--script` is specified.|Yes| |`--script`|Script to run the parameter search on. Required unless `--task-id` is specified.|Yes| |`--queue`|Queue to enqueue the experiments on.|Yes| |`--params-search`|Parameters space for optimization. See more information [here](#specifying-the-parameter-space). |No| diff --git a/docs/clearml_sdk/clearml_sdk.md b/docs/clearml_sdk/clearml_sdk.md index 597a3840..3638833e 100644 --- a/docs/clearml_sdk/clearml_sdk.md +++ b/docs/clearml_sdk/clearml_sdk.md @@ -67,7 +67,7 @@ See the [HyperParameterOptimizer SDK reference page](../references/sdk/hpo_optim ClearML's `automation` module includes classes that support creating pipelines: * [PipelineController](../pipelines/pipelines_sdk_tasks.md) - A pythonic interface for defining and configuring a pipeline controller and its steps. The controller and steps can be functions in your - python code, or existing ClearML [tasks](../fundamentals/task.md). + python code, or ClearML [tasks](../fundamentals/task.md). * [PipelineDecorator](../pipelines/pipelines_sdk_function_decorators.md) - A set of Python decorators which transform your functions into the pipeline controller and steps. diff --git a/docs/clearml_sdk/model_sdk.md b/docs/clearml_sdk/model_sdk.md index e8b026ae..ceb2861b 100644 --- a/docs/clearml_sdk/model_sdk.md +++ b/docs/clearml_sdk/model_sdk.md @@ -6,7 +6,7 @@ The following page provides an overview of the basic Pythonic interface to Clear ClearML provides the following classes to work with models: * `Model` - Represents a ClearML model, regardless of any task connection. Use this class to programmatically access and manage the ClearML model store. -* `InputModel` - Represents an existing ClearML model to be used in an experiment. Use this class to load a model from ClearML's model store or to import a pre-trained +* `InputModel` - Represents a ClearML model to be used in an experiment. Use this class to load a model from ClearML's model store or to import a pre-trained model from an external resource to use as an experiment's initial starting point. * `OutputModel` - Represents an experiment's output model (training results). An OutputModel is always connected to a [task](../fundamentals/task.md), so the models are traceable to experiments. diff --git a/docs/getting_started/main.md b/docs/getting_started/main.md index 1e3161f4..323d5e70 100644 --- a/docs/getting_started/main.md +++ b/docs/getting_started/main.md @@ -72,7 +72,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
  1. - + Data scientist logo

    Data Scientists

    Learn how to use ClearML's experiment tracking and management tools, and more!

    @@ -82,7 +82,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
  2. - + MLOps engineer logo

    MLOps Engineers

    Learn how to use ClearML's automation, orchestration, and tracking tools

    @@ -92,7 +92,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
  3. - + DevOps Engineer logo

    DevOps Engineers

    Learn learn how to deploy and configure a ClearML Server

    diff --git a/docs/webapp/applications/apps_hpo.md b/docs/webapp/applications/apps_hpo.md index 17b9793c..01ad2a42 100644 --- a/docs/webapp/applications/apps_hpo.md +++ b/docs/webapp/applications/apps_hpo.md @@ -9,7 +9,7 @@ The ClearML HPO App is available under the ClearML Pro plan The Hyperparameter Optimization Application finds the set of parameter values that optimize a specific metric for your model. -It takes in an existing ClearML experiment and its parameters to optimize. The parameter search space can be specified +It takes in a ClearML experiment and its parameters to optimize. The parameter search space can be specified by specific (discrete) values and/or value ranges (uniform parameters). The optimization app launches multiple copies of the original experiment, each time sampling different parameter sets, @@ -21,7 +21,7 @@ limits. ## HPO Instance Configuration * **Import Configuration** - Import an app instance configuration file. This will fill the configuration wizard with the values from the file, which can be modified before launching the app instance -* **Initial Task to Optimize** - ID of an existing ClearML task to optimize. This task will be cloned, and each clone will +* **Initial Task to Optimize** - ID of a ClearML task to optimize. This task will be cloned, and each clone will sample a different set of hyperparameters values * **Optimization Configuration** * Optimization Method - The optimization strategy to employ (e.g. random, grid, hyperband) diff --git a/docs/webapp/applications/apps_task_scheduler.md b/docs/webapp/applications/apps_task_scheduler.md index eebbd942..e5dd60d4 100644 --- a/docs/webapp/applications/apps_task_scheduler.md +++ b/docs/webapp/applications/apps_task_scheduler.md @@ -10,15 +10,15 @@ ClearML's Task Scheduler Application lets you schedule tasks for one-shot and/or The Scheduler is useful for scheduling routine operations, such as backups, generating reports, as well as periodically running pipelines for updating data and models. -Each scheduling job is configured with existing ClearML tasks and a scheduling specification for each task: the time +Each scheduling job is configured with ClearML tasks and a scheduling specification for each task: the time for execution and recurrence type. The Scheduler app will then launch copies of the specified tasks at their specified times. ## Scheduler Instance Configuration * **Scheduled Tasks** - * **Base Task ID** - ID of an existing ClearML task to schedule. This task will be cloned and enqueued for execution at the specified time. - * **Destination Project** - The project where scheduled tasks will be saved. + * **Base Task ID** - ID of a ClearML task to clone and enqueue for execution at the specified time. + * **Destination Project** - The project where the task will be cloned to. * **Queue** - The [ClearML Queue](../../fundamentals/agents_and_queues.md#what-is-a-queue) to which scheduled tasks are enqueued (make sure an agent is assigned to that queue) * **Recurrence** - Recurrence type, select one of the following options: * **None** - The task will run once at the specified time.