User Guide¶
CLI 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:
You should see an output like the following:┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Job Config ┃ Value ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Slurm Job ID │ 16060964 │
│ Job Name │ Meta-Llama-3.1-8B-Instruct │
│ Model Type │ LLM │
│ Vocabulary Size │ 128256 │
│ Partition │ a40 │
│ QoS │ m2 │
│ Time Limit │ 08:00:00 │
│ Num Nodes │ 1 │
│ GPUs/Node │ 1 │
│ CPUs/Task │ 16 │
│ Memory/Node │ 64G │
│ Model Weights Directory │ /model-weights/Meta-Llama-3.1-8B-Instruct │
│ Log Directory │ /h/vi_user/.vec-inf-logs/Meta-Llama-3.1 │
│ vLLM Arguments: │ │
│ --max-model-len: │ 131072 │
│ --max-num-seqs: │ 256 │
└─────────────────────────┴───────────────────────────────────────────┘
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:
To overwrite default vLLM engine arguments, you can specify the engine arguments in a comma separated string:
vec-inf launch Meta-Llama-3.1-8B-Instruct --vllm-args '--max-model-len=65536,--compilation-config=3'
For the full list of vLLM engine arguments, you can find them here, make sure you select the correct vLLM version.
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
qos: m2
time: 08:00:00
partition: a40
model_weights_parent_dir: /h/<username>/model-weights
vllm_args:
--max-model-len: 1010000
--max-num-seqs: 256
You would then set the VEC_INF_CONFIG path using:
NOTE
* There are other parameters that can also be added to the config but not shown in this example, check the ModelConfig for details.
* Check vLLM Engine Arguments for the full list of available vLLM engine arguments. The default parallel size for any parallelization defaults to 1, so none of the sizes were set specifically in this example.
* For GPU partitions with non-Ampere architectures, e.g. rtx6000, t4v2, BF16 isn't supported. For models that have BF16 as the default type, when using a non-Ampere GPU, use FP16 instead, i.e. --dtype: float16.
* Setting --compilation-config to 3 currently breaks multi-node model launches, so we don't set them for models that require multiple nodes of GPUs.
status command¶
You can check the inference server status by providing the Slurm job ID to the status command:
If the server is pending for resources, you should see an output like this:
┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Job Status ┃ Value ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Model Name │ Meta-Llama-3.1-8B-Instruct │
│ Model Status │ PENDING │
│ Pending Reason │ Resources │
│ Base URL │ UNAVAILABLE │
└────────────────┴────────────────────────────┘
When the server is ready, you should see an output like this:
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Job Status ┃ Value ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Model Name │ Meta-Llama-3.1-8B-Instruct │
│ Model Status │ READY │
│ Base URL │ http://gpu042:8080/v1 │
└──────────────┴────────────────────────────┘
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:
And you will see the performance metrics streamed to your console, note that the metrics are updated with a 2-second interval.
┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Metric ┃ Value ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ Prompt Throughput │ 10.9 tokens/s │
│ Generation Throughput │ 34.2 tokens/s │
│ Requests Running │ 1 reqs │
│ Requests Waiting │ 0 reqs │
│ Requests Swapped │ 0 reqs │
│ GPU Cache Usage │ 0.1% │
│ CPU Cache Usage │ 0.0% │
│ Avg Request Latency │ 2.6 s │
│ Total Prompt Tokens │ 441 tokens │
│ Total Generation Tokens │ 1748 tokens │
│ Successful Requests │ 14 reqs │
└─────────────────────────┴─────────────────┘
shutdown command¶
Finally, when you're finished using a model, you can shut it down by providing the Slurm job ID:
list command¶
You call view the full list of available models by running the list command:
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:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Model Config ┃ Value ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ model_name │ Meta-Llama-3.1-8B-Instruct │
│ model_family │ Meta-Llama-3.1 │
│ model_variant │ 8B-Instruct │
│ model_type │ LLM │
│ gpus_per_node │ 1 │
│ num_nodes │ 1 │
│ cpus_per_task │ 16 │
│ mem_per_node │ 64G │
│ vocab_size │ 128256 │
│ qos │ m2 │
│ time │ 08:00:00 │
│ partition │ a40 │
│ model_weights_parent_dir │ /model-weights │
│ vLLM Arguments: │ │
│ --max-model-len: │ 131072 │
│ --max-num-seqs: │ 256 │
└──────────────────────────┴────────────────────────────┘
launch, list, and status command supports --json-mode, where the command output would be structured as a JSON string.
Check Job Configuration¶
With every model launch, a Slurm script will be generated dynamically based on the job and model configuration. Once the Slurm job is queued, the generated Slurm script will be moved to the log directory for reproducibility, located at $log_dir/$model_family/$model_name.$slurm_job_id/$model_name.$slurm_job_id.slurm. In the same directory you can also find a JSON file with the same name that captures the launch configuration, and will have an entry of server URL once the server is ready.
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: Certain models don't adhere to OpenAI's chat template, e.g. Mistral family. For these models, you can either change your prompt to follow the model's default chat template or provide your own chat template via --chat-template: TEMPLATE_PATH.
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:
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 environmentPython API Usage¶
You can also use the vec_inf Python API to launch and manage inference servers.
Check out the Python API documentation for more details. There
are also Python API usage examples in the examples/api folder.