---
title: Model Deployment
---
:::important Enterprise Feature
The Model Deployment App is available under the ClearML Enterprise plan.
:::
The Model Deployment application enables users to quickly deploy LLM models as networking services over a secure
endpoint. This application supports various model configurations and customizations to optimize performance and resource
usage. The Model Deployment application serves your model on a machine of your choice. Once an app instance is running,
it serves your 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 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.
:::
Once you start a Model Deployment instance, you can view the following information in its dashboard:
* Status indicator
* 
- App instance is running and is actively in use
* 
- App instance is setting up
* 
- App instance is idle
* 
- 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.
## Model Deployment Instance Configuration
When configuring a new 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` 
to open the app's configuration form.
### Configuration Options
* **Import Configuration** - Import an app instance configuration file. This will fill the instance launch form with the
values from the file, which can be modified before launching the app instance
* **Project name** - ClearML Project Name
* **Task name** - Name of ClearML Task for your Model Deployment app instance
* **Queue** - The [ClearML Queue](../../fundamentals/agents_and_queues.md#what-is-a-queue) to which the Model Deployment app
instance task will be enqueued (make sure an agent is assigned to that queue)
* **Model** - A ClearML Model ID or a HuggingFace model name (e.g. `openai-community/gpt2`)
* **Model Configuration**
* Trust Remote Code - Select to set Hugging Face [`trust_remote_code`](https://huggingface.co/docs/text-generation-inference/main/en/reference/launcher#trustremotecode)
to `true`.
* Revision - The specific Hugging Face version of the model (i.e. weights) you want to use. You
can use a specific commit ID or a branch like `refs/pr/2`.
* Code Revision - The specific revision to use for the model code on HuggingFace Hub. It can be a branch name, a tag
name, or a commit ID. If unspecified, will use the default version.
* Max Model Length - Model context length. If unspecified, will be automatically derived from the model
* Tokenizer - A ClearML Model ID or a Hugging Face tokenizer
* Tokenizer Revision - The specific tokenizer Hugging Face version to use. It can be a branch name, a tag name, or a
commit ID. If unspecified, will use the default version.
* Tokenizer Mode - Select the tokenizer mode:
* `auto` - Uses the fast tokenizer if available
* `slow` - Uses the slow tokenizer.
* **LoRA Configuration**
* Enable LoRA - If checked, enable handling of [LoRA adapters](https://huggingface.co/docs/diffusers/en/training/lora#lora).
* LoRA Modules - LoRA module configurations in the format `name=path`. Multiple modules can be specified.
* Max LoRAs - Max number of LoRAs in a single batch.
* Max LoRA Rank
* LoRA Extra Vocabulary Size - Maximum size of extra vocabulary that can be present in a LoRA adapter (added to the base model vocabulary).
* LoRA Dtype - Select the data type for LoRA. Select one of the following:
* `auto` - If selected, will default to base model data type.
* `float16`
* `bfloat16`
* `float32`
* Max CPU LoRAs - Maximum number of LoRAs to store in CPU memory. Must be greater or equal to the
`Max Number of Sequences` field in the General section below. Defaults to `Max Number of Sequences`.
* **General**
* Disable Log Stats - Disable logging statistics
* Enforce Eager - Always use eager-mode PyTorch. If False, a hybrid of eager mode and CUDA graph will be used for
maximal performance and flexibility.
* Disable Custom All Reduce - See [vllm ParallelConfig](https://github.com/vllm-project/vllm/blob/main/vllm/config.py#L701)
* Disable Logging Requests
* Fixed API Access Key - Key to use for authenticating API access. Set a fixed API key if you've set up the server to
be accessible without authentication. Setting an API key ensures that only authorized users can access the endpoint.
* HuggingFace Token - Token for accessing HuggingFace models that require authentication
* Load Format - Select the model weights format to load:
* `auto` - Load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available.
* `pt` - Load the weights in the pytorch bin format.
* `safetensors` - Load the weights in the safetensors format.
* `npcache` - Load the weights in pytorch format and store a numpy cache to speed up the loading.
* `dummy` Initialize the weights with random values. Mainly used for profiling.
* Dtype - Select the data type for model weights and activations:
* `auto` - if selected, will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models.
* `half`
* `float16`
* `bfloat16`
* `float`
* `float32`
* KV Cache Type - Select data type for kv cache storage:
* `auto` - If selected, will use the model data type. Note FP8 is not supported when cuda version is lower than 11.8.
* `fp8_e5m2`
* Pipeline Parallel Size - Number of pipeline stages
* Tensor Parallel Size - Number of tensor parallel replicas
* Max Parallel Loading Workers - Load model sequentially in multiple batches, to avoid RAM OOM when using tensor
parallel and large models
* Token Block Size
* Random Seed
* Swap Space - CPU swap space size (GiB) per GPU
* GPU Memory Utilization - The fraction of GPU memory to be used for the model executor, which can range from 0 to 1
* Max Number of Batched Tokens - Maximum number of batched tokens per iteration
* Max Number of Sequences - Maximum number of sequences per iteration
* Max Number of Paddings - Maximum number of paddings in a batch
* Quantization - Method used to quantize the weights. If None, we first check the `quantization_config` attribute in
the model config file. If that is None, we assume the model weights are not quantized and use `dtype` to determine the
data type of the weights.
* Max Context Length to Capture - Maximum context length covered by CUDA graphs. When a sequence has context length
larger than this, we fall back to eager mode.
* Max Log Length - Max number of prompt characters or prompt ID numbers being printed in log. Default: unlimited
* **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
