Search Results for "linux cpu" - Page 6
Sort By:
Showing 1178 open source projects for "linux cpu"
View related business solutions-
Our Free Plans just got better! | Auth0 by OktaYou asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your secuirty. Auth0 now, thank yourself later.
-
Bright Data - All in One Platform for Proxies and Web ScrapingBright Data offers the highest quality proxies with automated session management, IP rotation, and advanced web unlocking technology. Enjoy reliable, fast performance with easy integration, a user-friendly dashboard, and enterprise-grade scaling. Powered by ethically-sourced residential IPs for seamless web scraping.
-
1
AI Upscaler for Blender
AI Upscaler for Blender using Real-ESRGAN
... on the CPU. Blender renders a low-resolution image. The Real-ESRGAN Upscaler upscales the low-resolution image to a higher-resolution image. Real-ESRGAN is a deep learning upscaler that uses neural networks to achieve excellent results by adding in detail when it upscales. -
2
EaseProbe
A simple, standalone, and lightweight tool
... command on a remote host and check the CPU, Memory, and Disk usage. MySQL. Connect to a MySQL server and run the SHOW STATUS SQL. Redis. Connect to a Redis server and run the PING command. Memcache. Connect to a Memcache server and run the version command or validate a given key/value pair. MongoDB. Connect to a MongoDB server and perform a ping. Kafka. Connect to a Kafka server and perform a list of all topics. -
3
ServiceTalk
A networking framework that evolves with your application
... knowledge of EventLoop threading model. Executing CPU-intensive or "blocking" code requires manual thread hops. Subtle out-of-order execution of tasks when code executes both on and off the EventLoop thread. APIs are not tailored towards common application use cases (e.g. request/response, RPC, etc..) The asynchronous programming paradigm presents a barrier to entry in scenarios where vertical scalability is not a primary concern. -
4
SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
Top-Rated Free CRM SoftwareHubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
-
5
DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
... of image tagging, object detection, segmentation, OCR, Audio, Video, Text classification, CSV for tabular data and time series. Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. Full Open Source, with an ecosystem of tools (API clients, video, annotation, ...) Fast Server written in pure C++, a single codebase for Cloud, Desktop & Embedded. -
6
Skytable
Skytable is a fast, secure and reliable realtime NoSQL database
... to exploit all CPU cores which helps lower your TCO. Written in Rust with expert analyzed unsafe code for memory safety and TLS for encrypted connections. Have 1MB memory? That's all Skytable needs. With no platform-specific dependencies, Skytable can virtually run on anything that has an OS. Features like keyspaces, tables, data types, authn+authz, snapshots and more are ready for you to use while we're working on several new data models and features. -
7
AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference... -
8
Netty-socketio
Socket.IO server implemented on Java
...-featured Java Profiler. YourKit, LLC is the creator of innovative and intelligent tools for profiling Java and .NET applications. CentOS, 1 CPU, 4GB RAM runned on VM, CPU 10%, Memory 15%, 6000 xhr-long polling sessions or 15000 websockets sessions, 4000 messages per second. -
9
Halide
A language for fast, portable data-parallel computation
Halide is a programming language for fast, portable data-parallel computation. It was designed to make writing high-performance image and array processing code much easier on modern machines. It works on all major operating systems and with several CPU architectures (X86, ARM, MIPS, Hexagon, PowerPC) and GPU Compute APIs (CUDA, OpenCL, OpenGL, among others). It isn't a standalone programming language however; rather it is embedded in C++ which means that you write C++ code, building... -
Pimberly PIM - the leading enterprise Product Information Management platform.Drive amazing product experiences with quality product data.
-
10
barco
Linux containers from scratch in C
barco is a project I worked on to learn more about Linux containers and the Linux kernel, based on other guides on the internet. Linux containers are made up by a set of Linux kernel features. namespaces: are used to group kernel objects into different sets that can be accessed by specific process trees. There are different types of namespaces, for example,the PID namespace is used to isolate the process tree, while the network namespace is used to isolate the network stack. -
11
CrossDB
Ultra High-performance Lightweight Embedded and Server OLTP RDBMS
CrossDB is a ultra high-performance, lightweight embedded and server OLTP RDBMS. It is designed for high-performance scenarios where the main memory can hold the entire database. -
12
TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing. -
13
KubeAI
Private Open AI on Kubernetes
Get inferencing running on Kubernetes: LLMs, Embeddings, Speech-to-Text. KubeAI serves an OpenAI compatible HTTP API. Admins can configure ML models by using the Model Kubernetes Custom Resources. KubeAI can be thought of as a Model Operator (See Operator Pattern) that manages vLLM and Ollama servers. -
14
td
Telegram client, in Go. (MTProto API)
Telegram MTProto API client in Go for users and bots. -
15
VulkanSceneGraph
Vulkan & C++17 based Scene Graph Project
... and examples are provided in separate repositories, links to these are provided below. The software currently builds under Linux (desktops variants through to Jetson & Raspberry Pi), Windows (VisualStudio, MinGW & Cygwin), Android, and macOS & iOS (using MoltenVk). -
16
Implicit
Fast Python collaborative filtering for implicit feedback datasets
This project provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets. All models have multi-threaded training routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores. In addition, the ALS and BPR models both have custom CUDA kernels - enabling fitting on compatible GPU’s. This library also supports using approximate nearest neighbour libraries such as Annoy, NMSLIB and Faiss for speeding up... -
17
cuML
RAPIDS Machine Learning Library
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU... -
18
Bento
Packer templates for building minimal Vagrant baseboxes
Bento is a project that encapsulates Packer templates for building Vagrant base boxes. A subset of templates are built and published to the bento org on Vagrant Cloud. These published boxes serve as the default boxes for kitchen-vagrant. -
19
Aria2 AriaNg Docker
The Docker image for Aria2 + AriaNg + File Browser + Rclone
One Docker image for file downloading, managing, sharing, as well as video playing and evening cloud storage synchronization. Furthermore, it's pretty small and ARM CPU compatible which means you can also run it on Raspberry Pi. Last but not least, Auto HTTPS can't be more easy. -
20
kube-capacity
A simple CLI that provides an overview of the resource requests
This is a simple CLI that provides an overview of the resource requests, limits, and utilization in a Kubernetes cluster. It attempts to combine the best parts of the output from kubectl top and kubectl describe into an easy-to-use CLI focused on cluster resources. By default, kube-capacity will output a list of nodes with the total CPU and Memory resource requests and limits for all the pods running on them. For clusters with more than one node, the first line will also include cluster-wide... -
21
Robusta KRR
Prometheus-based Kubernetes Resource Recommendations
Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for optimizing resource allocation in Kubernetes clusters. It gathers pod usage data from Prometheus and recommends requests and limits for CPU and memory. This reduces costs and improves performance. -
22
Pixie
Instant Kubernetes-Native Application Observability
... locally in the cluster. Pixie uses less than 5% of cluster CPU and in most cases less than 2%. PxL, Pixie’s flexible Pythonic query language, can be used across Pixie’s UI, CLI, and client APIs. -
23
Apache SkyWalking Java Agent
The Java agent for Apache SkyWalking
SkyWalking-Java: The Java Agent for Apache SkyWalking, which provides native tracing/metrics/logging abilities for Java projects. SkyWalking: an APM(application performance monitor) system, specially designed for microservices, cloud-native and container-based (Docker and Kubernetes) architectures. -
24
EvoTrees.jl
Boosted trees in Julia
A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram-based algorithms with support for multiple loss functions, including various regressions, multi-classification and Gaussian max likelihood. -
25
JobRunr
An extremely easy way to perform background processing in Java
Incredibly easy way to perform fire-and-forget, delayed, scheduled and recurring jobs inside Java applications using only Java 8 lambda's. CPU and I/O intensive, long-running and short-running jobs are supported. Persistent storage is done via either RDBMS (e.g. Postgres, MariaDB/MySQL, Oracle, SQL Server, DB2 and SQLite) or NoSQL (ElasticSearch, MongoDB and Redis). JobRunr provides a unified programming model to handle background tasks in a reliable way and runs them on shared hosting...