Search Results for "machine learning workloads"
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Showing 59 open source projects for "machine learning workloads"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,... -
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RustScan
The Modern Port Scanner
...Scans all 65k ports in 3 seconds. Full scripting engine support. Automatically pipe results into Nmap, or use our scripts (or write your own) to do whatever you want. Adaptive learning. RustScan improves the more you use it. No bloated machine learning here, just basic maths. The usuals you would expect. IPv6, CIDR, file input and more. Automatically pipes ports into Nmap. RustScan is a modern take on the port scanner. Sleek & fast. All while providing extensive extendability to you. Not to mention RustScan uses Adaptive Learning to improve itself over time, making it the best port scanner for you. ... -
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Porcupine
On-device wake word detection powered by deep learning
Build always-listening yet private voice applications. Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is using deep neural networks trained in real-world environments. Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux... -
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WiFi DensePose
Turn WiFi signals into real-time human pose estimation and detection
...The project demonstrates how commodity mesh routers and signal processing techniques can be leveraged to infer dense human pose information, even through obstacles such as walls. It is designed to showcase the emerging field of RF-based sensing, where machine learning models interpret wireless channel data to reconstruct human movement and posture. The repository includes components for data processing, model inference, and real-time visualization, making it suitable for research and experimental deployments. Its architecture emphasizes performance and reproducibility, allowing developers to explore non-visual motion capture systems using accessible hardware. ... -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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RuView
Turn WiFi signals into real-time human sensing and spatial awareness.
...Unlike traditional vision systems, RuView operates without cameras, wearables, or cloud connectivity, making it a privacy-first sensing solution. The system runs on low-cost hardware such as ESP32 sensor meshes and performs signal processing and machine learning directly at the edge. By learning the RF signature of each environment over time, RuView adapts automatically to different spaces and improves its sensing accuracy. Designed for applications ranging from healthcare monitoring to disaster response, it enables spaces to gain spatial awareness using the radio signals already present in the environment. -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,... -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative)... -
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently. -
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turbovec
A vector index built on TurboQuant, written in Rust with Python
turbovec is a Rust-based vector index with Python bindings for fast similarity search. It is built around TurboQuant, a quantization approach designed to reduce vector storage while preserving useful distance information. The project targets workloads where embedding search needs to be compact, efficient, and practical to integrate into Python applications. It avoids a separate training phase for the quantizer, which can simplify setup compared with systems that require codebook learning. TurboVec is useful for developers building retrieval, ranking, semantic search, recommendation, or AI memory systems. ... -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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PyBoy
Game Boy emulator written in Python
PyBoy is an open-source Game Boy emulator written in Python, designed for both gameplay and AI experimentation. It allows users to run classic Game Boy games while providing a powerful API for automation, scripting, and reinforcement learning. Developers can interact directly with game memory, inputs, and screen data, making it ideal for training bots and analyzing game mechanics. PyBoy emphasizes performance, enabling accelerated emulation speeds and frame skipping for large-scale simulations. It integrates with tools like OpenAI Gym, allowing seamless use in machine learning workflows. ... -
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Cloud Storage FUSE
A user-space file system for interacting with Google Cloud Storage
Cloud Storage FUSE is an open-source user-space file system adapter that allows Google Cloud Storage buckets to be mounted and accessed as if they were local file systems on a machine. This approach enables applications to interact with cloud storage using standard file system semantics, eliminating the need to rewrite code to use object storage APIs directly. The tool is particularly valuable in data-intensive workflows such as machine learning, where large datasets can be accessed on demand without requiring full local downloads. ... -
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Zarf
DevSecOps for Air Gap & Limited-Connection Systems
Zarf eliminates the complexity of air gap software delivery for Kubernetes clusters and cloud-native workloads using a declarative packaging strategy to support DevSecOps in offline and semi-connected environments. -
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VVV
An open source Vagrant configuration for developing with WordPress
...Approachable development environment with a modern server configuration. Stable state of software and configuration in default provisioning. Excellent and clear documentation to aid in learning and scaffolding. VVV requires recent versions of both Vagrant and VirtualBox to be installed. Vagrant is a “tool for building and distributing development environments”. It works with virtualization software such as VirtualBox to provide a virtual machine sandboxed from your local environment. In addition to VirtualBox, provider support is also included for Parallels, Hyper-V, VMWare Fusion, and VMWare Workstation. -
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ArrayFire
ArrayFire, a general purpose GPU library
ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if... -
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Elasticsearch
A Distributed RESTful Search Engine
Elasticsearch is a distributed, RESTful search and analytics engine that lets you store, search and analyze with ease at scale. It lets you perform and combine many types of searches; it scales seamlessly, and offers answers incredibly fast with search results you can rank based on a variety of factors. Elasticsearch can be used for a wide variety of use cases, from maps and metrics to site search and workplace search, and with all data types. -
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ESPectre
Motion detection system based on Wi-Fi spectre analysis (CSI)
...At its core, it analyzes Wi-Fi Channel State Information (CSI) — detailed measurements of how Wi-Fi waves change as they propagate — to mathematically detect disturbances caused by human movement between a Wi-Fi router and an ESP32 microcontroller, eliminating the need for machine learning or training data to operate effectively. Designed to integrate natively with Home Assistant through ESPHome, ESPectre exposes motion and movement intensity sensors directly to home automation dashboards, allowing users to trigger automations like lighting, heating, or security alerts when motion is detected. -
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SDGym
Benchmarking synthetic data generation methods
...Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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QuestDB
An open source SQL database designed to process time series data
QuestDB is a high-performance, open-source SQL database for applications in financial services, IoT, machine learning, DevOps and observability. It includes endpoints for PostgreSQL wire protocol, high-throughput schema-agnostic ingestion using InfluxDB Line Protocol, and a REST API for queries, bulk imports, and exports. QuestDB implements ANSI SQL with native extensions for time-oriented language features. These extensions make it simple to correlate data from multiple sources using relational and time series joins. ... -
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AWS IoT FleetWise Edge
AWS IoT FleetWise Edge Agent
...AWS IoT FleetWise makes it easy and cost-effective for automakers to collect, transform, and transfer vehicle data to the cloud in near-real-time and use it to build applications with analytics and machine learning that improve vehicle quality, safety, and autonomy. Train autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) with camera data collected from a fleet of production vehicles. Improve electric vehicle (EV) battery range estimates with crowdsourced environmental data, such as weather and driving conditions, from nearby vehicles. ... -
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Grafana
Leading open-source visualization and observability platform
Grafana OSS is the leading open-source platform for visualization and observability. It enables teams to query, visualize, alert on, and explore telemetry data from multiple sources in a single interface. With support for 100+ data source plugins—including Prometheus, Loki, Elasticsearch, InfluxDB, SQL/NoSQL databases, and OpenTelemetry—Grafana helps teams correlate metrics, logs, and traces across applications and infrastructure. Users can build interactive dashboards with rich... -
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Razer Cortex
Automatically streamline your PC’s performance
Automatically streamline your PC’s performance and visuals for supported games, thanks to an advanced AI with a machine-learning algorithm that calculates the best settings based on your preferences. Take your PC optimization one step further with a one-stop solution that automatically detect and delete junk files to free up space. Get more bang for your buck with a price comparison engine that scans top digital PC game stores to compile the best prices, as you join frequent giveaways for a chance to score new games and Razer gear. ... -
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Auto_Img_Renamer
AutoimgRenamer is a Python tool to rename image files in a folder.
AutoimgRenamer is a lightweight Python tool that automatically renames all image files in a selected folder using a specified base name and an incremental index. It’s especially useful for organizing datasets, preparing files for machine learning, or batch-renaming personal image collections. Features 1. Rename .jpg, .jpeg, .png, .gif, .bmp, and .webp images 2. Simple, fast, and efficient 3. Customizable base name for files How It Works The tool scans the selected folder, finds all image files, and renames them to a format like: base_name_0001.PNG, base_name_0002.PNG, etc. -
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Burn To The Brim
Utility for efficiently grouping files and folders together
...By recursively scanning your designated folders, BTTB matches item sizes to your media capacity, finding a near-perfect selection in milliseconds and an absolute perfect packing configuration in just a few seconds. Burn to the Brim 4.0.0 introduces state-of-the-art machine learning semantic coherence logic to BTTB's classical packing algorithms. -
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OnnxStream
Lightweight inference library for ONNX files, written in C++
The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. OnnxStream is based on the idea of decoupling the inference engine from the component responsible for providing the model weights, which is a class derived from WeightsProvider. ...
