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