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2
docs/deploying_clearml/clearml_server_es7_migration.md
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@ -129,7 +129,7 @@ and ClearML Server needs to be installed.
1. Add the `clearml-server` repository to Helm client.
```
helm repo add allegroai https://allegroai.github.io/clearml-server-helm/
helm repo add clearml https://clearml.github.io/clearml-server-helm/
```
Confirm the `clearml-server` repository is now in the Helm client.

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docs/deploying_clearml/clearml_server_linux_mac.md
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@ -136,7 +136,7 @@ Deploying the server requires a minimum of 8 GB of memory, 16 GB is recommended.
2. Download the ClearML Server docker-compose YAML file.
```
sudo curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
sudo curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. For Linux only, configure the **ClearML Agent Services**:

2
docs/deploying_clearml/clearml_server_win.md
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@ -57,7 +57,7 @@ Deploying the server requires a minimum of 8 GB of memory, 16 GB is recommended.
1. Save the ClearML Server docker-compose YAML file.
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
```
1. Run `docker-compose`. In PowerShell, execute the following commands:

6
docs/deploying_clearml/enterprise_deploy/appgw_install_k8s.md
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@ -12,8 +12,8 @@ This guide details the installation of the ClearML AI Application Gateway, speci
* Kubernetes cluster: `>= 1.21.0-0 < 1.32.0-0`
* Helm installed and configured
* Helm token to access allegroai helm-chart repo
* Credentials for allegroai docker repo
* Helm token to access `allegroai` helm-chart repo
* Credentials for `allegroai` docker repo
* A valid ClearML Server installation
## Optional for HTTPS
@ -27,7 +27,7 @@ This guide details the installation of the ClearML AI Application Gateway, speci
```
helm repo add allegroai-enterprise \
https://raw.githubusercontent.com/allegroai/clearml-enterprise-helm-charts/gh-pages \
https://raw.githubusercontent.com/clearml/clearml-enterprise-helm-charts/gh-pages \
--username <GITHUB_TOKEN> \
--password <GITHUB_TOKEN>
```

2
docs/deploying_clearml/upgrade_server_aws_ec2_ami.md
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@ -49,7 +49,7 @@ If upgrading from Trains Server version 0.15 or older, a data migration is requi
1. Download the latest `docker-compose.yml` file. Execute the following command:
```
sudo curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
sudo curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build.

2
docs/deploying_clearml/upgrade_server_gcp.md
View File

@ -38,7 +38,7 @@ you can proceed to upgrade to v2.x.
1. Download the latest `docker-compose.yml` file:
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build.

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docs/deploying_clearml/upgrade_server_kubernetes_helm.md
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@ -7,13 +7,13 @@ title: Kubernetes
```bash
helm repo update
helm upgrade clearml allegroai/clearml
helm upgrade clearml clearml/clearml
```
**To change the values in an existing installation,** execute the following:
```bash
helm upgrade clearml allegroai/clearml --version <CURRENT CHART VERSION> -f custom_values.yaml
helm upgrade clearml clearml/clearml --version <CURRENT CHART VERSION> -f custom_values.yaml
```
See the [clearml-helm-charts repository](https://github.com/clearml/clearml-helm-charts/tree/main/charts/clearml#local-environment)

2
docs/deploying_clearml/upgrade_server_linux_mac.md
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@ -59,7 +59,7 @@ For backwards compatibility, the environment variables ``TRAINS_HOST_IP``, ``TRA
1. Download the latest `docker-compose.yml` file:
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build:

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docs/deploying_clearml/upgrade_server_win.md
View File

@ -49,7 +49,7 @@ you can proceed to upgrade to v2.x.
1. Download the latest `docker-compose.yml` file:
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build.

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docs/getting_started/main.md
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@ -32,19 +32,19 @@ training, and deploying models at every scale on any AI infrastructure.
<tbody>
<tr>
<td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb"><b>Step 1</b></a> - Experiment Management</td>
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb">
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
</tr>
<tr>
<td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb"><b>Step 2</b></a> - Remote Execution Agent Setup</td>
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb">
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
</tr>
<tr>
<td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb"><b>Step 3</b></a> - Remotely Execute Tasks</td>
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb">
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
</tr>

2
docs/guides/clearml-task/clearml_task_tutorial.md
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@ -49,7 +49,7 @@ Execution log at: https://app.clear.ml/projects/552d5399112d47029c146d5248570295
### Executing a Local Script
For this example, use a local version of [this script](https://github.com/clearml/events/blob/master/webinar-0620/keras_mnist.py).
1. Clone the [allegroai/events](https://github.com/clearml/events) repository
1. Clone the [clearml/events](https://github.com/clearml/events) repository
1. Go to the root folder of the cloned repository
1. Run the following command:

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docs/guides/ide/google_colab.md
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@ -16,7 +16,7 @@ and running, users can send Tasks to be executed on Google Colab's hardware.
## Steps
1. Open up [this Google Colab notebook](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/clearml_agent/clearml_colab_agent.ipynb).
1. Open up [this Google Colab notebook](https://colab.research.google.com/github/clearml/clearml/blob/master/examples/clearml_agent/clearml_colab_agent.ipynb).
1. Run the first cell, which installs all the necessary packages:
```

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docs/guides/pipeline/pipeline_decorator.md
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@ -3,7 +3,7 @@ title: Pipeline from Decorators
---
The [pipeline_from_decorator.py](https://github.com/clearml/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py)
example demonstrates the creation of a pipeline in ClearML using the [`PipelineDecorator`](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)
example demonstrates the creation of a pipeline in ClearML using the [`PipelineDecorator`](../../references/sdk/automation_controller_pipelinedecorator.md#class-automationcontrollerpipelinedecorator)
class.
This example creates a pipeline incorporating four tasks, each of which is created from a Python function using a custom decorator:
@ -14,11 +14,11 @@ This example creates a pipeline incorporating four tasks, each of which is creat
* `step_four` - Uses data from `step_two` and the model from `step_three` to make a prediction.
The pipeline steps, defined in the `step_one`, `step_two`, `step_three`, and `step_four` functions, are each wrapped with the
[`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent)
[`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent)
decorator, which creates a ClearML pipeline step for each one when the pipeline is executed.
The logic that executes these steps and controls the interaction between them is implemented in the `executing_pipeline`
function. This function is wrapped with the [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
function. This function is wrapped with the [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
decorator which creates the ClearML pipeline task when it is executed.
The sections below describe in more detail what happens in the pipeline controller and steps.
@ -28,7 +28,7 @@ The sections below describe in more detail what happens in the pipeline controll
In this example, the pipeline controller is implemented by the `executing_pipeline` function.
Using the `@PipelineDecorator.pipeline` decorator creates a ClearML Controller Task from the function when it is executed.
For detailed information, see [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline).
For detailed information, see [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline).
In the example script, the controller defines the interactions between the pipeline steps in the following way:
1. The controller function passes its argument, `pickle_url`, to the pipeline's first step (`step_one`)
@ -39,13 +39,13 @@ In the example script, the controller defines the interactions between the pipel
:::info Local Execution
In this example, the pipeline is set to run in local mode by using
[`PipelineDecorator.run_locally()`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorrun_locally)
[`PipelineDecorator.run_locally()`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorrun_locally)
before calling the pipeline function. See pipeline execution options [here](../../pipelines/pipelines_sdk_function_decorators.md#running-the-pipeline).
:::
## Pipeline Steps
Using the `@PipelineDecorator.component` decorator will make the function a pipeline component that can be called from the
pipeline controller, which implements the pipeline's execution logic. For detailed information, see [`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent).
pipeline controller, which implements the pipeline's execution logic. For detailed information, see [`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent).
When the pipeline controller calls a pipeline step, a corresponding ClearML task will be created. Notice that all package
imports inside the function will be automatically logged as required packages for the pipeline execution step.
@ -63,7 +63,7 @@ executing_pipeline(
```
By default, the pipeline controller and the pipeline steps are launched through ClearML [queues](../../fundamentals/agents_and_queues.md#what-is-a-queue).
Use the [`PipelineDecorator.set_default_execution_queue`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
Use the [`PipelineDecorator.set_default_execution_queue`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorset_default_execution_queue)
method to specify the execution queue of all pipeline steps. The `execution_queue` parameter of the `@PipelineDecorator.component`
decorator overrides the default queue value for the specific step for which it was specified.

2
docs/guides/services/slack_alerts.md
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@ -22,7 +22,7 @@ The Slack API token and channel you create are required to configure the Slack a
1. In **Development Slack Workspace**, select a workspace.
1. Click **Create App**.
1. In **Basic Information**, under **Display Information**, complete the following:
- In **Short description**, enter "Allegro Train Bot".
- In **Short description**, enter "ClearML Train Bot".
- In **Background color**, enter "#202432".
1. Click **Save Changes**.
1. In **OAuth & Permissions**, under **Scopes**, click **Add an OAuth Scope**, and then select the following permissions

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docs/pipelines/pipelines_sdk_function_decorators.md
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@ -4,14 +4,14 @@ title: PipelineDecorator
## Creating Pipelines Using Function Decorators
Use the [`PipelineDecorator`](../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)
class to create pipelines from your existing functions. Use [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent)
to denote functions that comprise the steps of your pipeline, and [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
Use the [`PipelineDecorator`](../references/sdk/automation_controller_pipelinedecorator.md#class-automationcontrollerpipelinedecorator)
class to create pipelines from your existing functions. Use [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent)
to denote functions that comprise the steps of your pipeline, and [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
for your main pipeline execution logic function.
## @PipelineDecorator.pipeline
Using the [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
Using the [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
decorator transforms the function which implements your pipeline's execution logic to a ClearML pipeline controller,
an independently executed task.
@ -70,13 +70,13 @@ parameters. When launching a new pipeline run from the [UI](../webapp/pipelines/
![Pipeline new run](../img/pipelines_new_run.png)
## @PipelineDecorator.component
Using the [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent)
Using the [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent)
decorator transforms a function into a ClearML pipeline step when called from a pipeline controller.
When the pipeline controller calls a pipeline step, a corresponding ClearML task is created.
:::tip Package Imports
In the case that the `skip_global_imports` parameter of [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
In the case that the `skip_global_imports` parameter of [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
is set to `False`, all global imports will be automatically imported at the beginning of each step's execution.
Otherwise, if set to `True`, make sure that each function which makes up a pipeline step contains package imports, which
are automatically logged as required packages for the pipeline execution step.
@ -110,7 +110,7 @@ def step_one(pickle_data_url: str, extra: int = 43):
* `packages` - A list of required packages or a local requirements.txt file. Example: `["tqdm>=2.1", "scikit-learn"]` or
`"./requirements.txt"`. If not provided, packages are automatically added based on the imports used inside the function.
* `execution_queue` (optional) - Queue in which to enqueue the specific step. This overrides the queue set with the
[`PipelineDecorator.set_default_execution_queue method`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
[`PipelineDecorator.set_default_execution_queue method`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorset_default_execution_queue)
method.
* `continue_on_fail` - If `True`, a failed step does not cause the pipeline to stop (or marked as failed). Notice, that
steps that are connected (or indirectly connected) to the failed step are skipped (default `False`)
@ -186,14 +186,14 @@ specify which frameworks to log. See `Task.init`'s [`auto_connect_framework` par
* `auto_connect_arg_parser` - Control automatic logging of argparse objects. See `Task.init`'s [`auto_connect_arg_parser` parameter](../references/sdk/task.md#taskinit)
You can also directly upload a model or an artifact from the step to the pipeline controller, using the
[`PipelineDecorator.upload_model`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_model)
and [`PipelineDecorator.upload_artifact`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_artifact)
[`PipelineDecorator.upload_model`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorupload_model)
and [`PipelineDecorator.upload_artifact`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorupload_artifact)
methods respectively.
## Controlling Pipeline Execution
### Default Execution Queue
The [`PipelineDecorator.set_default_execution_queue`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
The [`PipelineDecorator.set_default_execution_queue`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorset_default_execution_queue)
method lets you set a default queue through which all pipeline steps
will be executed. Once set, step-specific overrides can be specified through the `@PipelineDecorator.component` decorator.
@ -226,7 +226,7 @@ You can run the pipeline logic locally, while keeping the pipeline components ex
#### Debugging Mode
In debugging mode, the pipeline controller and all components are treated as regular Python functions, with components
called synchronously. This mode is great to debug the components and design the pipeline as the entire pipeline is
executed on the developer machine with full ability to debug each function call. Call [`PipelineDecorator.debug_pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratordebug_pipeline)
executed on the developer machine with full ability to debug each function call. Call [`PipelineDecorator.debug_pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratordebug_pipeline)
before the main pipeline logic function call.
Example:
@ -242,7 +242,7 @@ In local mode, the pipeline controller creates Tasks for each component, and com
into sub-processes running on the same machine. Notice that the data is passed between the components and the logic with
the exact same mechanism as in the remote mode (i.e. hyperparameters / artifacts), with the exception that the execution
itself is local. Notice that each subprocess is using the exact same Python environment as the main pipeline logic. Call
[`PipelineDecorator.run_locally`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorrun_locally)
[`PipelineDecorator.run_locally`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorrun_locally)
before the main pipeline logic function.
Example:

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docs/references/sdk/automation_controller_pipelinedecorator.md Normal file
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@ -0,0 +1,5 @@
---
title: PipelineDecorator
---
**AutoGenerated PlaceHolder**

2
docs/webapp/pipelines/webapp_pipeline_table.md
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@ -36,7 +36,7 @@ The pipeline run table contains the following columns:
| Column | Description | Type |
|---|---|---|
| **RUN** | Pipeline run identifier | String |
| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller)'s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)'s `version` parameter | Version string |
| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller)'s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinedecorator.md#class-automationcontrollerpipelinedecorator)'s `version` parameter | Version string |
| **TAGS** | Descriptive, user-defined, color-coded tags assigned to run. | Tag |
| **STATUS** | Pipeline run's status. See a list of the [task states and state transitions](../../fundamentals/task.md#task-states). For Running, Failed, and Aborted runs, you will also see a progress indicator next to the status. See [here](../../pipelines/pipelines.md#tracking-pipeline-progress). | String |
| **USER** | User who created the run. | String |

2
docs/webapp/pipelines/webapp_pipeline_viewing.md
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@ -108,7 +108,7 @@ The details panel includes three tabs:
![console](../../img/webapp_pipeline_step_console_dark.png#dark-mode-only)
* **Code** - For pipeline steps generated from functions using either [`PipelineController.add_function_step`](../../references/sdk/automation_controller_pipelinecontroller.md#add_function_step)
or [`PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent),
or [`PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent),
you can view the selected step's code.
![code](../../img/webapp_pipeline_step_code.png#light-mode-only)

12
docs/webapp/webapp_exp_track_visual.md
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@ -230,13 +230,13 @@ The **INFO** tab shows extended task information:
* [Task description](#description)
* [Task details](#task-details)
### Latest Events Log
### Latest Events Log
:::important Enterprise Feature
The latest events log is available under the ClearML Enterprise plan.
:::info Hosted Service and Enterprise Feature
The latest events log is available only on the ClearML Hosted Service and under the ClearML Enterprise plan.
:::
The Enterprise Server also displays a detailed history of task activity:
The **INFO** tab includes a detailed history of task activity:
* Task action (e.g. status changes, project move, etc.)
* Action time
* Acting user
@ -252,7 +252,7 @@ To download the task history as a CSV file, hover over the log and click <img sr
ClearML maintains a system-wide, large but strict limit for task history items. Once the limit is reached, the oldest entries are purged to make room for fresh entries.
:::
### Description
### Description
Add descriptive text to the task in the **Description** section. To modify the description, hover over the
description box and click **Edit**.
@ -304,7 +304,7 @@ All scalars that ClearML automatically logs, as well as those explicitly reporte
Scalar series can be displayed in [graph view](#graph-view) (default) or in [metric values view](#metric-values-view):
#### Graph View
#### Graph View
Scalar graph view (<img src="/docs/latest/icons/ico-charts-view.svg" alt="Graph view" className="icon size-md space-sm" />)
shows scalar series plotted as a time series line chart. By default, a single plot is shown for each scalar metric,
with all variants overlaid within.

8
docs/webapp/webapp_reports.md
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@ -424,22 +424,22 @@ To add an image, add an exclamation point, followed by the alt text enclosed by
image enclosed in parentheses:
```
![Logo](https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg)
![Logo](https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg)
```
The rendered output should look like this:
![Logo](https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg)
![Logo](https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg)
To add a title to the image, which you can see in a tooltip when hovering over the image, add the title after the image's
link:
```
![With title](https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg "ClearML logo")
![With title](https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg "ClearML logo")
```
The rendered output should look like this:
<img src="https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg" alt="Logo with Title" title="ClearML logo"/>
<img src="https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg" alt="Logo with Title" title="ClearML logo"/>
Hover over the image to see its title.

4
sidebars.js
View File

@ -399,8 +399,10 @@ module.exports = {
'references/sdk/dataset',
{'Pipeline': [
'references/sdk/automation_controller_pipelinecontroller',
'references/sdk/automation_controller_pipelinedecorator',
'references/sdk/automation_job_clearmljob'
]},
]
},
'references/sdk/scheduler',
'references/sdk/trigger',
{'HyperParameter Optimization': [