clearml-docs/docs/webapp/applications/apps_model_deployment.md

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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 Task Traffic Router The Model Deployment app relies on the ClearML Traffic Router which implements a secure, authenticated network channel to the model :::

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
• 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 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 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.
  • 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
  • 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