Search Results for "benchmark\"
Sort By:
Showing 722 open source projects for "benchmark\"
View related business solutions-
Simplify every aspect of buying for your business in Order.co. From sourcing products to scaling purchasing across locations to automating your AP and approvals workstreams, Order.co is the platform of choice for growing businesses.
-
Calmerry is beneficial for businesses of all sizes, particularly those in high-stress industries, organizations with remote teams, and HR departments seeking to improve employee well-being and productivity
-
1
Benchmark
A microbenchmark support library
A library to benchmark code snippets, similar to unit tests. -
2
LZ4
Extremely fast compression algorithm
LZ4 is lossless compression algorithm, providing compression speed > 500 MB/s per core (>0.15 Bytes/cycle). It features an extremely fast decoder, with speed in multiple GB/s per core (~1 Byte/cycle). A high compression derivative, called LZ4_HC, is available, trading customizable CPU time for compression ratio. LZ4 library is provided as open-source software using a BSD license. This benchmark simulates simple "static content transfer" scenario such as OS Kernel compression or video game's... -
3
AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
...++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
4
K9s
Kubernetes CLI To Manage Your Clusters In Style!
... support to extend K9s to create your very own cluster commands. Powerful filtering mode to allow user to drill down and view workload related resources. Supports for viewing RBAC rules such as cluster/roles and their associated bindings. Reverse lookup to asserts what a user/group or ServiceAccount can do on your clusters. You can benchmark your HTTP services/pods directly from K9s to see how your application fare and adjust your resources request/limit accordingly. -
Track all your service data in one easy-to-use system, enabling your team to move faster and generate more revenue for your bottom line.
-
5
XMRig
RandomX, KawPow, CryptoNight, AstroBWT and GhostRider unified miner
High performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT CPU/GPU miner, RandomX benchmark, and stratum proxy. XMRig is a high-performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT unified CPU/GPU miner and RandomX benchmark. Official binaries are available for Windows, Linux, macOS, and FreeBSD. The preferred way to configure the miner is the JSON config file as it is more flexible and human-friendly. The command-line interface does... -
6
This website is deprecated. From 2023-11, please use go to github (https://github.com/dacapobench/dacapobench) for the latest releases.
-
7
rathole
A lightweight and high-performance reverse proxy for NAT traversal
A secure, stable and high-performance reverse proxy for NAT traversal, written in Rust. rathole, like frp and ngrok, can help to expose the service on the device behind the NAT to the Internet, via a server with a public IP. High Performance Much higher throughput can be achieved than frp, and more stable when handling a large volume of connections. Low Resource Consumption Consumes much fewer memory than similar tools. See Benchmark. The binary can be as small as ~500KiB to fit the constraints... -
8
Terrascan
Detect compliance and security violations across Infrastructure
Detect compliance and security violations across Infrastructure as Code to mitigate risk before provisioning cloud native infrastructure. As you embrace Infrastructure as Code (IaC) such as Terraform, Kubernetes, Argo CD, Atlantis and AWS CloudFormation, it is important to ensure that security best practices and compliance requirements are observed. Terracan provides 500+ out-of-the-box policies so that you can scan IaC against common policy standards such as the CIS Benchmark. It leverages... -
9
MLDatasets.jl
Utility package for accessing common Machine Learning datasets
This package represents a community effort to provide a common interface for accessing common Machine Learning (ML) datasets. In contrast to other data-related Julia packages, the focus of MLDatasets.jl is specifically on downloading, unpacking, and accessing benchmark datasets. Functionality for the purpose of data processing or visualization is only provided to a degree that is special to some datasets. -
Whether natural disaster, cyberattack, or plain-old human error, data can disappear in the blink of an eye. ConnectWise BCDR (formerly Recover) delivers reliable and secure backup and disaster recovery backed by powerful automation and a 24/7 NOC to get your clients back to work in minutes, not days.
-
10
OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets... -
11
pnpm
Fast, disk space efficient package manager
Fast, disk space efficient package manager. pnpm uses a content-addressable filesystem to store all files from all module directories on a disk. When using npm, if you have 100 projects using lodash, you will have 100 copies of lodash on disk. With pnpm, lodash will be stored in a content-addressable storage. Files inside node_modules are cloned or hard-linked from a single content-addressable storage. pnpm has built-in support for multiple packages in a repository. pnpm creates a non-flat... -
12
MLPerf
Reference implementations of MLPerf™ training benchmarks
This is a repository of reference implementations for the MLPerf training benchmarks. These implementations are valid as starting points for benchmark implementations but are not fully optimized and are not intended to be used for "real" performance measurements of software frameworks or hardware. Benchmarking the performance of training ML models on a wide variety of use cases, software, and hardware drives AI performance across the tech industry. The MLPerf Training working group draws... -
13
Apache Sedona
Cluster computing framework for processing large-scale geospatial data
Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive... -
14
PyG
Graph Neural Network Library for PyTorch
... graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
15
Prowler
An open source security tool to perform AWS security assessment
... Foundations Benchmark (49 checks) and has more than 100 additional checks related to GDPR, HIPAA, PCI-DSS, ISO-27001, FFIEC, SOC2, and others. +200 checks covering security best practices across all AWS regions and most AWS services. Get a direct colorful or monochrome report. Get an HTML, CSV, JUNIT, JSON, or JSON ASFF (Security Hub) format report. -
16
Smile
Statistical machine intelligence and learning engine
Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM languages... -
17
MMAction2
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
OpenMMLab's next generation video understanding toolbox and benchmark. MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. Modular design: We decompose a video understanding framework into different components. One can easily construct a customized video understanding framework by combining different modules. Support four major video understanding tasks: MMAction2 implements various algorithms for multiple video understanding... -
18
BenchmarkTools.jl
A benchmarking framework for the Julia language
BenchmarkTools makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results. This package is used to write and run the benchmarks found in BaseBenchmarks.jl. The CI infrastructure for automated performance testing of the Julia language is not in this package but can be found in Nanosoldier.jl. Our story begins with two packages, "Benchmarks" and "BenchmarkTrackers". The Benchmarks package implemented... -
19
hyperfine
A command-line benchmarking tool
A command-line benchmarking tool. Statistical analysis across multiple runs. Support for arbitrary shell commands. Constant feedback about the benchmark progress and current estimates. Warmup runs can be executed before the actual benchmark. Cache-clearing commands can be set up before each timing run. Statistical outlier detection to detect interference from other programs and caching effects. Export results to various formats: CSV, JSON, Markdown, AsciiDoc. Parameterized benchmarks (e.g. vary... -
20
spp
A simple and powerful proxy
Supported protocol: TCP, UDP, RUDP (Reliable UDP), RICMP (Reliable ICMP), RHTTP (Reliable HTTP), KCP, Quic. Support type: forward proxy, reverse agent, SOCKS5 forward agent, SOCKS5 reverse agent. Agreement and type can be freely combined. External agent agreement and internal forwarding protocols can freely combine. Support Shadowsocks plug-in, spp-shadowsocks-plugin, spp-shadowsocks-plugin-android. -
21
Nightingale
A distributed and high-performance monitoring system
Nightingale is a distributed and high-performance monitoring system. Prometheus enterprise edition. Nightingale is a new generation of domestic intelligent monitoring system. It has good support for cloud-native scenarios and traditional physical machine virtual machine scenarios. It takes 10 minutes to complete the construction and is familiar with use in 1 hour. It has been verified by the massive data of the production environment of Didi, and hopes to create a benchmark for domestic... -
22
kube-bench
Checks whether Kubernetes is deployed
kube-bench is a tool that checks whether Kubernetes is deployed securely by running the checks documented in the CIS Kubernetes Benchmark. Trivy, the all-in-one cloud-native security scanner, can be deployed as a Kubernetes Operator inside a cluster. Both, the Trivy CLI, and the Trivy Operator support CIS Kubernetes Benchmark scanning among several other features. There are multiple ways to run kube-bench. You can run kube-bench inside a pod, but it will need access to the host's PID namespace... -
23
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... -
24
Tile38
Ultra Fast Geospatial Database & Geofencing Server
... components: the server, command line interface tool and server benchmark tool. It is completely free and open source and is available for OSX, Linux, FreeBSD, and Windows. -
25
ReinforcementLearning.jl
A reinforcement learning package for Julia
A collection of tools for doing reinforcement learning research in Julia. Provide elaborately designed components and interfaces to help users implement new algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate...