---
title: Embedding Model Deployment
---
:::important Enterprise Feature
The Embedding Model Deployment App is available under the ClearML Enterprise plan.
:::
The Embedding Model Deployment app enables users to quickly deploy embedding models as networking services over a secure
endpoint. This application supports various model configurations and customizations, addressing a range of embedding use
cases. The Embedding Model Deployment application serves your model on a machine of your choice. Once an app instance is
running, it serves your embedding model through a secure, publicly accessible network endpoint. The app monitors
endpoint activity and shuts down if the model remains inactive for a specified maximum idle time.
:::info AI Application Gateway
The Embedding Model Deployment app makes use of the ClearML Traffic Router which implements a secure, authenticated
network endpoint for the model.
If the ClearML AI application Gateway is not available, the model endpoint might not be accessible.
:::
After starting an Embedding Model Deployment instance, you can view the following information in its dashboard:
* Status indicator
* <img src="/docs/latest/icons/ico-embedding-model-active.svg" alt="Active instance" className="icon size-md space-sm" /> - App instance is running and is actively in use
* <img src="/docs/latest/icons/ico-embedding-model-loading.svg" alt="Loading instance" className="icon size-md space-sm" /> - App instance is setting up
* <img src="/docs/latest/icons/ico-embedding-model-idle.svg" alt="Idle instance" className="icon size-md space-sm" /> - App instance is idle
* <img src="/docs/latest/icons/ico-embedding-model-stopped.svg" alt="Stopped instance" className="icon size-md space-sm" /> - App instance is stopped
* Idle time - Time elapsed since last activity
* Endpoint - The publicly accessible URL of the model endpoint. Active model endpoints are also available in the
[Model Endpoints](../webapp_model_endpoints.md) table, which allows you to view and compare endpoint details and
monitor status over time
* API base - The base URL for the model endpoint
* API key - The authentication key for the model endpoint
* Test Command - An example command line to test the deployed model
* Requests - Number of requests over time
* Latency - Request response time (ms) over time
* Endpoint resource monitoring metrics over time
* CPU usage
* Network throughput
* Disk performance
* Memory performance
* GPU utilization
* GPU memory usage
* GPU temperature
* Console log - The console log shows the app instance's console output: setup progress, status changes, error messages, etc.
## Embedding Model Deployment Instance Configuration
When configuring a new Embedding Model Deployment instance, you can fill in the required parameters or reuse the
configuration of a previously launched instance.
Launch an app instance with the configuration of a previously launched instance using one of the following options:
* Cloning a previously launched app instance will open the instance launch form with the original instance's
configuration prefilled.
* Importing an app configuration file. You can export the configuration of a previously launched instance as a JSON file
when viewing its configuration.
The prefilled configuration form can be edited before launching the new app instance.
To configure a new app instance, click `Launch New` <img src="/docs/latest/icons/ico-add.svg" alt="Add new" className="icon size-md space-sm" />
to open the app's configuration form.
### Configuration Options
* Import Configuration - Import an app instance configuration file. This will fill the configuration form with the
values from the file, which can be modified before launching the app instance
* Project name - ClearML Project where your Embedding Model Deployment app instance will be stored
* Task name - Name of ClearML Task for your Embedding Model Deployment app instance
* Queue - The [ClearML Queue](../../fundamentals/agents_and_queues.md#what-is-a-queue) to which the Embedding Model
Deployment app instance task will be enqueued (make sure an agent is assigned to it)
* Model Configuration
* Model - A ClearML Model ID or a Hugging Face model name (e.g. `openai-community/gpt2`)
* Revision - The specific Hugging Face version of the model you want to use. You can use a specific commit ID or a
branch like `refs/pr/2`
* Tokenization Workers - Number of tokenizer workers used for payload tokenization, validation, and truncation.
Defaults to the number of CPU cores on the machine
* Dtype - The data type enforced on the model
* Pooling - Model pooling method. If `pooling` is not set, the pooling configuration will be parsed from the model
`1_Pooling/config.json` configuration. If `pooling` is set, it will override the model pooling configuration. Possible
values:
* `cls`: Use CLS token
* `mean`: Apply Mean pooling
* `splade`: Apply SPLADE (Sparse Lexical and Expansion) pooling. This option is only available for `ForMaskedLM`
Transformer models
* \+ Add item - Add another model endpoint. Each model will be accessible through the same base URL, with the model
name appended to the URL.
* Hugging Face Token - Token for accessing Hugging Face models that require authentication
* Idle Time Limit (Hours) - Maximum idle time after which the app instance will shut down
* Export Configuration - Export the app instance configuration as a JSON file, which you can later import to create a
new instance with the same configuration
