Remove GPU Compute (#823)

docs/webapp/applications/apps_gpu_compute.md

@@ -1,83 +0,0 @@
----
-title: GPU Compute
----
-
-:::info Pro Plan Offering
-The ClearML GPU Compute App is available under the ClearML Pro plan
-::: 
-
-Set up to run your workloads on 100% green cloud machines at optimized costs – no setup required! The ClearML GPU Compute 
-Application automatically spins cloud machines up or down based on demand. The app optimizes machine usage according to 
-a user defined resource budget: define your budget by specifying the GPU type and number of GPUs you want to use.
-
-Each application instance monitors a ClearML queue: new cloud machines are spun up if there are pending jobs on the 
-queue. The app instance automatically terminates idle machines based on a specified maximum idle time.
-
-For more information about how autoscalers work, see [Autoscalers Overview](../../cloud_autoscaling/autoscaling_overview.md#autoscaler-applications).
-
-## GPU Compute 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
-* **Machine Specification** 
-    * GPU Type - NVIDIA GPU on the machine
-    * Number of GPUs - Number of GPUs in the cloud machine
-    * The rest of the machine's available resources are dependent on the number and type of GPUs specified above:  
-      * vCPUs - Number of vCPUs in the cloud machine
-      * Memory - RAM available to the cloud machine
-      * Hourly Price - Machine's hourly rate   
-      * Disk - Amount of Disk space available to the cloud machine
-    * Monitored Queue - Queue associated with application instance. The tasks enqueued to this queue will be executed on 
-      machines of this specification
-    * Cloud Machine Limit - Maximum number of concurrent machines to launch
-* **Idle Time Limit** (optional) - Maximum time in minutes that a cloud machine can be idle before it is spun down
-* **Default Docker Image** - Default Docker image in which the ClearML Agent will run. Provide a Docker stored 
-  in a Docker artifactory so instances can automatically fetch it
-* **Git Configuration** - Git credentials with which the ClearML Agents running on your cloud instances will access your repositories to retrieve the code for their jobs
-    * Git User
-    * Git Password / Personal Access Token
-* **Cloud Storage Access** (optional) - Access credentials to cloud storage service. Provides ClearML Tasks running on cloud 
-  machines access to your storage
-* **Additional ClearML Configuration** (optional) - A ClearML configuration file to use by the ClearML Agent when executing your experiments
-
-![GPU Compute wizard](../../img/apps_gpu_compute_wizard.png)
-
-## Dashboard
-
-Once a GPU Compute instance is launched, the dashboard displays a summary of your cloud usage and costs.
-
-![GPU Compute dashboard](../../img/apps_gpu_compute_dashboard.png)
-
-The GPU Compute dashboard shows:
-* Service status indicator
-  * <img src="/docs/latest/icons/ico-server-ok.svg" alt="Working server" className="icon size-md space-sm" /> - Cloud service is available
-  * <img src="/docs/latest/icons/ico-server-alert.svg" alt="Not working server" className="icon size-md space-sm" /> - Cloud service is currently unavailable
-* Cloud instance details 
-  * GPU type
-  * Number of GPUs
-  * Number of vCPUs
-  * RAM 
-  * Storage
-* Cost details 
-  * Instance rate 
-  * Total cost for current billing cycle
-* Number of current running cloud instances 
-* Instance History - Number of running cloud instances over time  
-* Console - The log shows updates of cloud instances being spun up/down. 
-
-:::tip Console Debugging   
-To make the autoscaler console log show additional debug information, change an active app instance's log level to DEBUG:
-1. Go to the app instance task's page > **CONFIGURATION** tab > **USER PROPERTIES** section 
-1. Hover over the section > Click `Edit` > Click `+ADD PARAMETER`
-1. Input `log_level` as the key and `DEBUG` as the value of the new parameter.
-
-![Autoscaler debugging](../../img/webapp_autoscaler_debug_log.png)
-
-The console's log level will update in the autoscaler's next iteration.  
-:::
-
-:::tip EMBEDDING CLEARML VISUALIZATION
-You can embed plots from the app instance dashboard into [ClearML Reports](../webapp_reports.md). These visualizations 
-are updated live as the app instance(s) updates. The Enterprise Plan and Hosted Service support embedding resources in 
-external tools (e.g. Notion). Hover over the plot and click <img src="/docs/latest/icons/ico-plotly-embed-code.svg" alt="Embed code" className="icon size-md space-sm" /> 
-to copy the embed code, and navigate to a report to paste the embed code.
-:::

docs/webapp/applications/apps_overview.md

@@ -24,7 +24,7 @@ ClearML provides the following applications:
 * [**Streamlit Launcher**](apps_streamlit.md) - Create visual web interfaces for your models with Streamlit (available under ClearML Enterprise Plan)
 
 :::info Autoscalers
-Autoscaling ([GPU Compute](apps_gpu_compute.md), [AWS Autoscaler](apps_aws_autoscaler.md), and [GCP Autoscaler](apps_gcp_autoscaler.md))
+Autoscaling ([AWS Autoscaler](apps_aws_autoscaler.md) and [GCP Autoscaler](apps_gcp_autoscaler.md))
 was previously available through the Applications page. The autoscaler functionality has been moved to the [Orchestration page](https://app.clear.ml/workers-and-queues/autoscalers) 
 in the WebApp. 
 :::