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2
docs/apps/clearml_param_search.md
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@ -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.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--task-name`|Name of the optimization task. If unspecified, the base Python script's file name is used.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--task-id`|ID of an existing ClearML task whose hyperparameters will be optimized. Required unless `--script` is specified.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--task-id`|ID of a ClearML task whose hyperparameters will be optimized. Required unless `--script` is specified.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--script`|Script to run the parameter search on. Required unless `--task-id` is specified.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--queue`|Queue to enqueue the experiments on.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--params-search`|Parameters space for optimization. See more information [here](#specifying-the-parameter-space). |<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|

2
docs/clearml_sdk/clearml_sdk.md
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@ -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.

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docs/clearml_sdk/model_sdk.md
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@ -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.

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docs/getting_started/main.md
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@ -72,7 +72,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
<ol>
<li>
<i>
<img src="/docs/latest/icons/ico-data-scientist.svg" />
<img src="/docs/latest/icons/ico-data-scientist.svg" alt="Data scientist logo" />
</i>
<h4>Data Scientists</h4>
<p>Learn how to use ClearML's experiment tracking and management tools, and more!</p>
@ -82,7 +82,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
</li>
<li>
<i>
<img src="/docs/latest/icons/ico-mlops-engineer.svg" />
<img src="/docs/latest/icons/ico-mlops-engineer.svg" alt="MLOps engineer logo" />
</i>
<h4>MLOps Engineers</h4>
<p>Learn how to use ClearML's automation, orchestration, and tracking tools</p>
@ -92,7 +92,7 @@ Want a more in depth introduction to ClearML? Choose where you want to get start
</li>
<li>
<i>
<img src="/docs/latest/icons/ico-devops-engineer.svg" />
<img src="/docs/latest/icons/ico-devops-engineer.svg" alt="DevOps Engineer logo" />
</i>
<h4>DevOps Engineers</h4>
<p>Learn learn how to deploy and configure a ClearML Server</p>

4
docs/webapp/applications/apps_hpo.md
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@ -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)

6
docs/webapp/applications/apps_task_scheduler.md
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@ -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.