
vector-inference
Efficient LLM inference on Slurm clusters using vLLM.
Stars: 77

This repository provides an easy-to-use solution for running inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository run natively on the Vector Institute cluster environment. Users can deploy models as Slurm jobs, check server status and performance metrics, and shut down models. The repository also supports launching custom models with specific configurations. Additionally, users can send inference requests and set up an SSH tunnel to run inference from a local device.
README:
This repository provides an easy-to-use solution to run inference servers on Slurm-managed computing clusters using vLLM. This package runs natively on the Vector Institute cluster environments. To adapt to other environments, follow the instructions in Installation.
NOTE: Supported models on Killarney are tracked here
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. The latest image has vLLM
version 0.10.1.1
.
If you'd like to use vec-inf
on your own Slurm cluster, you would need to update the configuration files, there are 3 ways to do it:
- Clone the repository and update the
environment.yaml
and themodels.yaml
file invec_inf/config
, then install from source by runningpip install .
. - The package would try to look for cached configuration files in your environment before using the default configuration. The default cached configuration directory path points to
/model-weights/vec-inf-shared
, you would need to create anenvironment.yaml
and amodels.yaml
following the format of these files invec_inf/config
. - The package would also look for an enviroment variable
VEC_INF_CONFIG_DIR
. You can put yourenvironment.yaml
andmodels.yaml
in a directory of your choice and set the enviroment variableVEC_INF_CONFIG_DIR
to point to that location.
Vector Inference provides 2 user interfaces, a CLI and an API
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:
NOTE: On Vector Killarney Cluster environment, the following fields are required:
-
--account
,-A
: The Slurm account, this argument can be set to default by setting environment variableVEC_INF_ACCOUNT
. -
--work-dir
,-D
: A working directory other than your home directory, this argument can be set to default by seeting environment variableVEC_INF_WORK_DIR
.
Models that are already supported by vec-inf
would be launched using the cached configuration (set in slurm_vars.py) 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. You can also launch your own custom model as long as the model architecture is supported by vLLM. For detailed instructions on how to customize your model launch, check out the launch
command section in User Guide
-
batch-launch
: Launch multiple model inference servers at once, currently ONLY single node models supported, -
status
: Check the model status by providing its Slurm job ID. -
metrics
: Streams performance metrics to the console. -
shutdown
: Shutdown a model by providing its Slurm job ID. -
list
: List all available model names, or view the default/cached configuration of a specific model. -
cleanup
: Remove old log directories, use--help
to see the supported filters. Use--dry-run
to preview what would be deleted.
For more details on the usage of these commands, refer to the User Guide
Example:
>>> from vec_inf.api import VecInfClient
>>> client = VecInfClient()
>>> # Assume VEC_INF_ACCOUNT and VEC_INF_WORK_DIR is set
>>> response = client.launch_model("Meta-Llama-3.1-8B-Instruct")
>>> job_id = response.slurm_job_id
>>> status = client.get_status(job_id)
>>> if status.status == ModelStatus.READY:
... print(f"Model is ready at {status.base_url}")
>>> client.shutdown_model(job_id)
For details on the usage of the API, refer to the API Reference
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.
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
.
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 8081:10.1.1.29:8081 [email protected] -N
The example provided above is for the Vector Killarney cluster, change the variables accordingly for your environment. The IP address for the compute nodes on Killarney follow 10.1.1.XX
pattern, where XX
is the GPU number (kn029
-> 29
in this example).
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for vector-inference
Similar Open Source Tools

vector-inference
This repository provides an easy-to-use solution for running inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository run natively on the Vector Institute cluster environment. Users can deploy models as Slurm jobs, check server status and performance metrics, and shut down models. The repository also supports launching custom models with specific configurations. Additionally, users can send inference requests and set up an SSH tunnel to run inference from a local device.

gpt-cli
gpt-cli is a command-line interface tool for interacting with various chat language models like ChatGPT, Claude, and others. It supports model customization, usage tracking, keyboard shortcuts, multi-line input, markdown support, predefined messages, and multiple assistants. Users can easily switch between different assistants, define custom assistants, and configure model parameters and API keys in a YAML file for easy customization and management.

torchchat
torchchat is a codebase showcasing the ability to run large language models (LLMs) seamlessly. It allows running LLMs using Python in various environments such as desktop, server, iOS, and Android. The tool supports running models via PyTorch, chatting, generating text, running chat in the browser, and running models on desktop/server without Python. It also provides features like AOT Inductor for faster execution, running in C++ using the runner, and deploying and running on iOS and Android. The tool supports popular hardware and OS including Linux, Mac OS, Android, and iOS, with various data types and execution modes available.

llm-ollama
LLM-ollama is a plugin that provides access to models running on an Ollama server. It allows users to query the Ollama server for a list of models, register them with LLM, and use them for prompting, chatting, and embedding. The plugin supports image attachments, embeddings, JSON schemas, async models, model aliases, and model options. Users can interact with Ollama models through the plugin in a seamless and efficient manner.

WindowsAgentArena
Windows Agent Arena (WAA) is a scalable Windows AI agent platform designed for testing and benchmarking multi-modal, desktop AI agents. It provides researchers and developers with a reproducible and realistic Windows OS environment for AI research, enabling testing of agentic AI workflows across various tasks. WAA supports deploying agents at scale using Azure ML cloud infrastructure, allowing parallel running of multiple agents and delivering quick benchmark results for hundreds of tasks in minutes.

mlx-lm
MLX LM is a Python package designed for generating text and fine-tuning large language models on Apple silicon using MLX. It offers integration with the Hugging Face Hub for easy access to thousands of LLMs, support for quantizing and uploading models to the Hub, low-rank and full model fine-tuning capabilities, and distributed inference and fine-tuning with `mx.distributed`. Users can interact with the package through command line options or the Python API, enabling tasks such as text generation, chatting with language models, model conversion, streaming generation, and sampling. MLX LM supports various Hugging Face models and provides tools for efficient scaling to long prompts and generations, including a rotating key-value cache and prompt caching. It requires macOS 15.0 or higher for optimal performance.

safety-tooling
This repository, safety-tooling, is designed to be shared across various AI Safety projects. It provides an LLM API with a common interface for OpenAI, Anthropic, and Google models. The aim is to facilitate collaboration among AI Safety researchers, especially those with limited software engineering backgrounds, by offering a platform for contributing to a larger codebase. The repo can be used as a git submodule for easy collaboration and updates. It also supports pip installation for convenience. The repository includes features for installation, secrets management, linting, formatting, Redis configuration, testing, dependency management, inference, finetuning, API usage tracking, and various utilities for data processing and experimentation.

EuroEval
EuroEval is a robust European language model benchmark tool, formerly known as ScandEval. It provides a platform to benchmark pretrained models on various tasks across different languages. Users can evaluate models, datasets, and metrics both online and offline. The tool supports benchmarking from the command line, script, and Docker. Additionally, users can reproduce datasets used in the project using provided scripts. EuroEval welcomes contributions and offers guidelines for general contributions and adding new datasets.

ScandEval
ScandEval is a framework for evaluating pretrained language models on mono- or multilingual language tasks. It provides a unified interface for benchmarking models on a variety of tasks, including sentiment analysis, question answering, and machine translation. ScandEval is designed to be easy to use and extensible, making it a valuable tool for researchers and practitioners alike.

vulnerability-analysis
The NVIDIA AI Blueprint for Vulnerability Analysis for Container Security showcases accelerated analysis on common vulnerabilities and exposures (CVE) at an enterprise scale, reducing mitigation time from days to seconds. It enables security analysts to determine software package vulnerabilities using large language models (LLMs) and retrieval-augmented generation (RAG). The blueprint is designed for security analysts, IT engineers, and AI practitioners in cybersecurity. It requires NVAIE developer license and API keys for vulnerability databases, search engines, and LLM model services. Hardware requirements include L40 GPU for pipeline operation and optional LLM NIM and Embedding NIM. The workflow involves LLM pipeline for CVE impact analysis, utilizing LLM planner, agent, and summarization nodes. The blueprint uses NVIDIA NIM microservices and Morpheus Cybersecurity AI SDK for vulnerability analysis.

garak
Garak is a vulnerability scanner designed for LLMs (Large Language Models) that checks for various weaknesses such as hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks. It combines static, dynamic, and adaptive probes to explore vulnerabilities in LLMs. Garak is a free tool developed for red-teaming and assessment purposes, focusing on making LLMs or dialog systems fail. It supports various LLM models and can be used to assess their security and robustness.

frontend
Nuclia frontend apps and libraries repository contains various frontend applications and libraries for the Nuclia platform. It includes components such as Dashboard, Widget, SDK, Sistema (design system), NucliaDB admin, CI/CD Deployment, and Maintenance page. The repository provides detailed instructions on installation, dependencies, and usage of these components for both Nuclia employees and external developers. It also covers deployment processes for different components and tools like ArgoCD for monitoring deployments and logs. The repository aims to facilitate the development, testing, and deployment of frontend applications within the Nuclia ecosystem.

telemetry-airflow
This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

cagent
cagent is a powerful and easy-to-use multi-agent runtime that orchestrates AI agents with specialized capabilities and tools, allowing users to quickly build, share, and run a team of virtual experts to solve complex problems. It supports creating agents with YAML configuration, improving agents with MCP servers, and delegating tasks to specialists. Key features include multi-agent architecture, rich tool ecosystem, smart delegation, YAML configuration, advanced reasoning tools, and support for multiple AI providers like OpenAI, Anthropic, Gemini, and Docker Model Runner.

sage
Sage is a tool that allows users to chat with any codebase, providing a chat interface for code understanding and integration. It simplifies the process of learning how a codebase works by offering heavily documented answers sourced directly from the code. Users can set up Sage locally or on the cloud with minimal effort. The tool is designed to be easily customizable, allowing users to swap components of the pipeline and improve the algorithms powering code understanding and generation.

garak
Garak is a free tool that checks if a Large Language Model (LLM) can be made to fail in a way that is undesirable. It probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. Garak's a free tool. We love developing it and are always interested in adding functionality to support applications.
For similar tasks

vector-inference
This repository provides an easy-to-use solution for running inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository run natively on the Vector Institute cluster environment. Users can deploy models as Slurm jobs, check server status and performance metrics, and shut down models. The repository also supports launching custom models with specific configurations. Additionally, users can send inference requests and set up an SSH tunnel to run inference from a local device.

vllm
vLLM is a fast and easy-to-use library for LLM inference and serving. It is designed to be efficient, flexible, and easy to use. vLLM can be used to serve a variety of LLM models, including Hugging Face models. It supports a variety of decoding algorithms, including parallel sampling, beam search, and more. vLLM also supports tensor parallelism for distributed inference and streaming outputs. It is open-source and available on GitHub.

bce-qianfan-sdk
The Qianfan SDK provides best practices for large model toolchains, allowing AI workflows and AI-native applications to access the Qianfan large model platform elegantly and conveniently. The core capabilities of the SDK include three parts: large model reasoning, large model training, and general and extension: * `Large model reasoning`: Implements interface encapsulation for reasoning of Yuyan (ERNIE-Bot) series, open source large models, etc., supporting dialogue, completion, Embedding, etc. * `Large model training`: Based on platform capabilities, it supports end-to-end large model training process, including training data, fine-tuning/pre-training, and model services. * `General and extension`: General capabilities include common AI development tools such as Prompt/Debug/Client. The extension capability is based on the characteristics of Qianfan to adapt to common middleware frameworks.

dstack
Dstack is an open-source orchestration engine for running AI workloads in any cloud. It supports a wide range of cloud providers (such as AWS, GCP, Azure, Lambda, TensorDock, Vast.ai, CUDO, RunPod, etc.) as well as on-premises infrastructure. With Dstack, you can easily set up and manage dev environments, tasks, services, and pools for your AI workloads.

tiny-llm-zh
Tiny LLM zh is a project aimed at building a small-parameter Chinese language large model for quick entry into learning large model-related knowledge. The project implements a two-stage training process for large models and subsequent human alignment, including tokenization, pre-training, instruction fine-tuning, human alignment, evaluation, and deployment. It is deployed on ModeScope Tiny LLM website and features open access to all data and code, including pre-training data and tokenizer. The project trains a tokenizer using 10GB of Chinese encyclopedia text to build a Tiny LLM vocabulary. It supports training with Transformers deepspeed, multiple machine and card support, and Zero optimization techniques. The project has three main branches: llama2_torch, main tiny_llm, and tiny_llm_moe, each with specific modifications and features.

examples
Cerebrium's official examples repository provides practical, ready-to-use examples for building Machine Learning / AI applications on the platform. The repository contains self-contained projects demonstrating specific use cases with detailed instructions on deployment. Examples cover a wide range of categories such as getting started, advanced concepts, endpoints, integrations, large language models, voice, image & video, migrations, application demos, batching, and Python apps.

HuaTuoAI
HuaTuoAI is an artificial intelligence image classification system specifically designed for traditional Chinese medicine. It utilizes deep learning techniques, such as Convolutional Neural Networks (CNN), to accurately classify Chinese herbs and ingredients based on input images. The project aims to unlock the secrets of plants, depict the unknown realm of Chinese medicine using technology and intelligence, and perpetuate ancient cultural heritage.
For similar jobs

LitServe
LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.

Lidar_AI_Solution
Lidar AI Solution is a highly optimized repository for self-driving 3D lidar, providing solutions for sparse convolution, BEVFusion, CenterPoint, OSD, and Conversion. It includes CUDA and TensorRT implementations for various tasks such as 3D sparse convolution, BEVFusion, CenterPoint, PointPillars, V2XFusion, cuOSD, cuPCL, and YUV to RGB conversion. The repository offers easy-to-use solutions, high accuracy, low memory usage, and quantization options for different tasks related to self-driving technology.

generative-ai-sagemaker-cdk-demo
This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

cake
cake is a pure Rust implementation of the llama3 LLM distributed inference based on Candle. The project aims to enable running large models on consumer hardware clusters of iOS, macOS, Linux, and Windows devices by sharding transformer blocks. It allows running inferences on models that wouldn't fit in a single device's GPU memory by batching contiguous transformer blocks on the same worker to minimize latency. The tool provides a way to optimize memory and disk space by splitting the model into smaller bundles for workers, ensuring they only have the necessary data. cake supports various OS, architectures, and accelerations, with different statuses for each configuration.

Awesome-Robotics-3D
Awesome-Robotics-3D is a curated list of 3D Vision papers related to Robotics domain, focusing on large models like LLMs/VLMs. It includes papers on Policy Learning, Pretraining, VLM and LLM, Representations, and Simulations, Datasets, and Benchmarks. The repository is maintained by Zubair Irshad and welcomes contributions and suggestions for adding papers. It serves as a valuable resource for researchers and practitioners in the field of Robotics and Computer Vision.

tensorzero
TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. It enables a data & learning flywheel for LLMs by unifying inference, observability, optimization, and experimentation. The platform includes a high-performance model gateway, structured schema-based inference, observability, experimentation, and data warehouse for analytics. TensorZero Recipes optimize prompts and models, and the platform supports experimentation features and GitOps orchestration for deployment.

vector-inference
This repository provides an easy-to-use solution for running inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository run natively on the Vector Institute cluster environment. Users can deploy models as Slurm jobs, check server status and performance metrics, and shut down models. The repository also supports launching custom models with specific configurations. Additionally, users can send inference requests and set up an SSH tunnel to run inference from a local device.

rhesis
Rhesis is a comprehensive test management platform designed for Gen AI teams, offering tools to create, manage, and execute test cases for generative AI applications. It ensures the robustness, reliability, and compliance of AI systems through features like test set management, automated test generation, edge case discovery, compliance validation, integration capabilities, and performance tracking. The platform is open source, emphasizing community-driven development, transparency, extensible architecture, and democratizing AI safety. It includes components such as backend services, frontend applications, SDK for developers, worker services, chatbot applications, and Polyphemus for uncensored LLM service. Rhesis enables users to address challenges unique to testing generative AI applications, such as non-deterministic outputs, hallucinations, edge cases, ethical concerns, and compliance requirements.