Search Results for "workload"
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Showing 17 open source projects for "workload"
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1
MCP K8s Eye
MCP Server for kubernetes management and analyze workload status
A tool designed to manage Kubernetes clusters and analyze workload statuses, providing insights and operational capabilities to enhance cluster performance and reliability. -
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NVIDIA Personal AI Router (PAIR)
Router that virtually distributes inference across connected devices
NVIDIA Personal AI Router (PAIR) is a local inference router that distributes independent AI requests across compatible computers on the same network. It automatically discovers participating machines and routes jobs according to model availability, engine availability, and current workload. PAIR works with Ollama and LM Studio while exposing Ollama-compatible and OpenAI-compatible endpoints to applications and agents. Windows, Linux, and macOS systems can participate in the same cluster across x64 and Arm64 architectures. A desktop application manages nodes, engines, models, jobs, endpoints, and cluster pairing, while a terminal interface supports headless machines. ... -
3
dsh-web
DeepSeek Harness (DSH) Web
dsh-web is a plugin ecosystem that expands the DeepSeek Harness Web GUI into a broader AI development workstation. Its capabilities are packaged as independent plugins that can be installed, replaced, or extended without modifying the DSH source code. The suite adds performance monitoring, agent presets, a task board, mobile remote control, SSH operations, image understanding, and an enhanced workspace panel. Tasks can be organized on a multi-column board and scheduled for real execution... -
4
Compute Library
The Compute Library is a set of computer vision and machine learning
The Compute Library is a set of computer vision and machine learning functions optimized for both Arm CPUs and GPUs using SIMD technologies. The library provides superior performance to other open-source alternatives and immediate support for new Arm® technologies e.g. SVE2. -
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5
Loki Mode
Multi-agent autonomous startup system for Claude Code
Loki Mode is a multi-agent autonomous execution system designed to take structured product requirements or specifications and autonomously drive the creation, testing, deployment, and scaling of complex software projects using a large team of specialized AI agents. It orchestrates dozens of agent types across swarms that handle designated roles — such as architecture, coding, QA, deployment, and business workflows — running in parallel to cover both engineering and operational tasks without... -
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mistral.rs
Fast, flexible LLM inference
...The project includes hardware-aware tooling that can benchmark a system and choose sensible quantization and device-mapping strategies, helping users get strong performance without manual tuning. It also supports serving multiple models from the same server process, enabling routing or quick switching between models depending on workload needs. For user-facing testing, mistral.rs can provide a built-in web UI, and it also offers a dedicated lightweight web chat interface that supports richer interaction patterns. -
7
Beta9
Run serverless GPU workloads with fast cold starts on bare-metal
beta9 is a platform that enables running serverless GPU workloads with fast cold starts on bare-metal servers globally. It allows developers to deploy and scale GPU-accelerated applications without managing underlying infrastructure, offering flexibility and efficiency for AI and high-performance computing tasks. beta9 supports various frameworks and provides tools for monitoring and managing deployments effectively. -
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Flower
Flower: A Friendly Federated Learning Framework
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. -
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Sail
A drop-in Apache Spark replacement written in Rust
...The framework is designed to operate across a variety of environments, including local machines, Kubernetes clusters, and cloud deployments, allowing flexible scaling based on workload requirements. It also emphasizes cost efficiency, with benchmarks showing significant performance improvements and reduced infrastructure usage compared to traditional systems. -
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10
uzu
A high-performance inference engine for AI models
...The engine implements a hybrid architecture in which model layers can be executed either as custom GPU kernels or through Apple’s MPSGraph API, allowing it to balance performance and compatibility depending on the workload. By utilizing Apple’s unified memory architecture, uzu reduces memory copying overhead and improves inference throughput for local AI workloads. The system includes a simple high-level API that enables developers to run models, create inference sessions, and generate outputs with minimal configuration. -
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Ray
A unified framework for scalable computing
...Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. ... -
12
ClawSweeper
ClawSweeper scans all issues and PRs
...The project runs across issues and PRs on a scheduled basis, reviewing each item and producing a reasoned recommendation instead of simply closing content automatically. Its goal is to reduce maintainer workload while keeping the decision process visible and reviewable. ClawSweeper is especially useful for repositories where old issue queues make it difficult to identify what still needs attention. It functions as a focused repository hygiene tool rather than a general-purpose project management platform. -
13
MetaScreener
AI-powered tool for efficient abstract and PDF screening
...The platform can analyze both abstracts and full PDF documents, enabling automated filtering based on research criteria defined by the user. By incorporating natural language processing techniques, the system can identify potentially relevant studies and reduce the workload associated with manual screening. -
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Anthropic's Original Performance
Anthropic's original performance take-home, now open for you to try
Anthropic's Original Performance repository contains the publicly released version of a performance challenge originally used by Anthropic as part of their technical interview process, offering developers the opportunity to optimize and benchmark low-level code against simulated models. The project sets up a baseline performance problem where participants work to reduce simulated “clock cycles” required to run a given workload, effectively challenging them to engineer faster code under constraints. This take-home includes starter code, tests, and tools to debug performance, aiming to measure how effectively one can apply algorithmic improvements and optimizations. Because it’s framed around beating baseline scores — and even outperforming previous automated systems — it encourages both deep knowledge of Python and creative problem-solving. -
15
AskUI Vision Agent
Enable AI to control your desktop, mobile and HMI devices
AskUI’s Vision Agent is an automation framework that allows you—and AI agents—to control real desktops, mobile devices, and HMI systems by perceiving the UI and performing actions like clicking, typing, scrolling, and drag-and-drop. It is designed for multi-platform compatibility and supports multiple AI models so you can tailor perception and decision-making to your workload. The repository presents a feature overview, sample media, and frequent release notes, which show ongoing improvements such as CORS checks and other operational tweaks. The broader AskUI documentation covers the Python Vision Agent along with suite services and inference APIs, indicating a productized ecosystem rather than a single library. ... -
16
KServe
Standardized Serverless ML Inference Platform on Kubernetes
KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and... -
17
nGraph
nGraph has moved to OpenVINO
Frameworks using nGraph Compiler stack to execute workloads have shown up to 45X performance boost when compared to native framework implementations. We've also seen performance boosts running workloads that are not included on the list of Validated workloads, thanks to nGraph's powerful subgraph pattern matching. Additionally, we have integrated nGraph with PlaidML to provide deep learning performance acceleration on Intel, nVidia, & AMD GPUs. nGraph Compiler aims to accelerate developing...
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