vec-inf 0.5.0

Efficient LLM inference on Slurm clusters using vLLM.

Vector Inference: Easy inference on Slurm clusters

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This repository provides an easy-to-use solution to run inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository runs natively on the Vector Institute cluster environment. To adapt to other environments, update the environment variables in cli/_helper.py, cli/_config.py, vllm.slurm, multinode_vllm.slurm and models.yaml accordingly.

Installation

If you are using the Vector cluster environment, and you don't need any customization to the inference server environment, run the following to install package:

pip install vec-inf

Otherwise, we recommend using the provided Dockerfile to set up your own environment with the package

Usage

launch command

The launch command allows users to deploy a model as a slurm job. If the job successfully launches, a URL endpoint is exposed for the user to send requests for inference.

We will use the Llama 3.1 model as example, to launch an OpenAI compatible inference server for Meta-Llama-3.1-8B-Instruct, run:

vec-inf launch Meta-Llama-3.1-8B-Instruct

You should see an output like the following:

Overrides

Models that are already supported by vec-inf would be launched using the cached configuration or default configuration. You can override these values by providing additional parameters. Use vec-inf launch --help to see the full list of parameters that can be overriden. For example, if qos is to be overriden:

vec-inf launch Meta-Llama-3.1-8B-Instruct --qos <new_qos>

Custom models

You can also launch your own custom model as long as the model architecture is supported by vLLM, and make sure to follow the instructions below:

• Your model weights directory naming convention should follow $MODEL_FAMILY-$MODEL_VARIANT ($MODEL_VARIANT is OPTIONAL).
• Your model weights directory should contain HuggingFace format weights.
• You should specify your model configuration by:
  • Creating a custom configuration file for your model and specify its path via setting the environment variable VEC_INF_CONFIG. Check the default parameters file for the format of the config file. All the parameters for the model should be specified in that config file.
  • Using launch command options to specify your model setup.
• For other model launch parameters you can reference the default values for similar models using the list command .

Here is an example to deploy a custom Qwen2.5-7B-Instruct-1M model which is not supported in the default list of models using a user custom config. In this case, the model weights are assumed to be downloaded to a model-weights directory inside the user's home directory. The weights directory of the model follows the naming convention so it would be named Qwen2.5-7B-Instruct-1M. The following yaml file would need to be created, lets say it is named /h/<username>/my-model-config.yaml.

models:
  Qwen2.5-7B-Instruct-1M:
    model_family: Qwen2.5
    model_variant: 7B-Instruct-1M
    model_type: LLM
    gpus_per_node: 1
    num_nodes: 1
    vocab_size: 152064
    max_model_len: 1010000
    max_num_seqs: 256
    pipeline_parallelism: true
    enforce_eager: false
    qos: m2
    time: 08:00:00
    partition: a40
    model_weights_parent_dir: /h/<username>/model-weights

You would then set the VEC_INF_CONFIG path using:

export VEC_INF_CONFIG=/h/<username>/my-model-config.yaml

Note that there are other parameters that can also be added to the config but not shown in this example, such as data_type and log_dir.

status command

You can check the inference server status by providing the Slurm job ID to the status command:

vec-inf status 15373800

If the server is pending for resources, you should see an output like this:

When the server is ready, you should see an output like this:

There are 5 possible states:

• PENDING: Job submitted to Slurm, but not executed yet. Job pending reason will be shown.
• LAUNCHING: Job is running but the server is not ready yet.
• READY: Inference server running and ready to take requests.
• FAILED: Inference server in an unhealthy state. Job failed reason will be shown.
• SHUTDOWN: Inference server is shutdown/cancelled.

Note that the base URL is only available when model is in READY state, and if you've changed the Slurm log directory path, you also need to specify it when using the status command.

metrics command

Once your server is ready, you can check performance metrics by providing the Slurm job ID to the metrics command:

vec-inf metrics 15373800

And you will see the performance metrics streamed to your console, note that the metrics are updated with a 2-second interval.

shutdown command

Finally, when you're finished using a model, you can shut it down by providing the Slurm job ID:

vec-inf shutdown 15373800

> Shutting down model with Slurm Job ID: 15373800

list command

You call view the full list of available models by running the list command:

vec-inf list

NOTE: The above screenshot does not represent the full list of models supported.

You can also view the default setup for a specific supported model by providing the model name, for example Meta-Llama-3.1-70B-Instruct:

vec-inf list Meta-Llama-3.1-70B-Instruct

launch, list, and status command supports --json-mode, where the command output would be structured as a JSON string.

Send inference requests

Once the inference server is ready, you can start sending in inference requests. We provide example scripts for sending inference requests in examples folder. Make sure to update the model server URL and the model weights location in the scripts. For example, you can run python examples/inference/llm/chat_completions.py, and you should expect to see an output like the following:

{
    "id":"chatcmpl-387c2579231948ffaf66cdda5439d3dc",
    "choices": [
        {
            "finish_reason":"stop",
            "index":0,
            "logprobs":null,
            "message": {
                "content":"Arrr, I be Captain Chatbeard, the scurviest chatbot on the seven seas! Ye be wantin' to know me identity, eh? Well, matey, I be a swashbucklin' AI, here to provide ye with answers and swappin' tales, savvy?",
                "role":"assistant",
                "function_call":null,
                "tool_calls":[],
                "reasoning_content":null
            },
            "stop_reason":null
        }
    ],
    "created":1742496683,
    "model":"Meta-Llama-3.1-8B-Instruct",
    "object":"chat.completion",
    "system_fingerprint":null,
    "usage": {
        "completion_tokens":66,
        "prompt_tokens":32,
        "total_tokens":98,
        "prompt_tokens_details":null
    },
    "prompt_logprobs":null
}

NOTE: For multimodal models, currently only ChatCompletion is available, and only one image can be provided for each prompt.

SSH tunnel from your local device

If you want to run inference from your local device, you can open a SSH tunnel to your cluster environment like the following:

ssh -L 8081:172.17.8.29:8081 [e-mail] -N

Where the last number in the URL is the GPU number (gpu029 in this case). The example provided above is for the vector cluster, change the variables accordingly for your environment