Search Results for "network performance" - Page 4
Sort By:
Showing 115 open source projects for "network performance"
View related business solutions-
$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.
-
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.
-
1
LiVES
LiVES is a Video Editing System. It is designed to be simple to use, y
LiVES mixes realtime video performance and non-linear editing in one professional quality application. It is designed to be simple to use, yet powerful. It is small in size, yet it has many advanced features. Using LiVES, you can start editing and making video right away, without having to worry about formats, frame sizes, or framerates. It is a very flexible tool which is used by both professional VJ's and video editors - mix and switch clips from the keyboard, use dozens of realtime... -
2
NLP-Models-Tensorflow
Gathers machine learning and Tensorflow deep learning models for NLP
...The project includes scripts for preparing datasets, training models, and evaluating performance on various text analysis tasks. Many implementations are designed for experimentation, allowing developers to adjust parameters, swap architectures, and test different preprocessing techniques. -
3
BytePS
A high performance and generic framework for distributed DNN training
...We use Tesla V100 32GB GPUs and set batch size equal to 64 per GPU. Each machine has 8 V100 GPUs (32GB memory) with NVLink-enabled. Machines are inter-connected with 100 Gbps RDMA network. This is the same hardware setup you can get on AWS. -
4
PyTracking
Visual tracking library based on PyTorch
A general python framework for visual object tracking and video object segmentation, based on PyTorch. Official implementation of the RTS (ECCV 2022), ToMP (CVPR 2022), KeepTrack (ICCV 2021), LWL (ECCV 2020), KYS (ECCV 2020), PrDiMP (CVPR 2020), DiMP (ICCV 2019), and ATOM (CVPR 2019) trackers, including complete training code and trained models. -
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
5
I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
6
pytorch-examples
Simple examples to introduce PyTorch
...It also serves as a quick reference for common patterns and techniques used in deep learning workflows. The project aligns with PyTorch’s philosophy of combining usability with performance and flexibility. Overall, pytorch-examples is an essential learning resource for anyone working with PyTorch. -
7
NeuroNER
Named-entity recognition using neural networks
..."deep learning") Is cross-platform, open source, freely available, and straightforward to use. Enables the users to create or modify annotations for a new or existing corpus. Train the neural network that performs the NER. During the training, NeuroNER allows monitoring of the network. Evaluate the quality of the predictions made by NeuroNER. The performance metrics can be calculated and plotted by comparing the predicted labels with the gold labels. -
8
Easy-TensorFlow
Simple and comprehensive tutorials in TensorFlow
...Furthermore, since most of the developers are using TensorFlow for code development, having hands-on on TensorFlow is a necessity these days. Tensorboard is a powerful visualization suite that is developed to track both the network topology and performance, making debugging even simpler. -
9
Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
...Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. There are too many symbolic function wrappers already. Tensorpack includes only a few common layers. -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
10
Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
11
coNCePTuaL
DSL for writing communication benchmarks
coNCePTuaL is a toolset for rapidly generating portable, readable, and reproducible network-performance tests. coNCePTuaL can perform the equivalent of many pages of C code with just a few mouse clicks or lines of code in a domain-specific language. -
12
SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with... -
13
Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
14
DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
15
Pyben-nio
Simple python network benchmark that you can ride on!
-
16
TEACUP
TCP Experiment Automation Controlled Using Python
TEACUP automates many aspects of running TCP performance experiments in a specially-constructed physical testbed. TEACUP enables repeatable testing of different TCP algorithms over a range of emulated network path conditions, bottleneck rate limits and bottleneck queuing disciplines. TEACUP utilises a text-based configuration file to define experiments as combinations of parameters specifying desired network path and end host conditions. -
17
Pulsar framework
Event driven concurrent framework for Python
Event-driven concurrent framework for Python. Pulsar's goal is to provide an easy way to build scalable network programs. In the Hello world! webserver example above, many client connections can be handled concurrently. Pulsar tells the operating system (through epoll or select) that it should be notified when a new connection is made, and then it goes to sleep. Pulsar uses the asyncio module from the standard python library and it can be configured to run in multi-processing mode. The http... -
18
cnn-benchmarks
Benchmarks for popular CNN models
The cnn-benchmarks project is a collection of benchmarking scripts designed to evaluate the performance of convolutional neural networks across different hardware and configurations. It provides standardized implementations of popular CNN architectures, enabling developers to measure training speed, memory usage, and computational efficiency. The project focuses on reproducibility, allowing consistent comparisons between models and environments. It is particularly useful for testing GPUs and... -
19
EDCC-CNG
Exploration and categorization of CREs and CRMs
...We developed two programs that serve as exploratory tools in the analysis of CRM-mediated control of gene expression: “Exploration of Distinctive CREs and CRMs” (EDCC) and “CRM Network Generator” (CNG). EDCC correlates the presence and positions of CREs/CRMs with gene expression data and identifies candidate regulatory elements for further functional analysis. CNG provides an unbiased neural network approach to assess the importance of positional features that were determined by EDCC. To sustain a high computational performance even for large datasets, the mostly in Python 3 written programs use k-mer based indexing, parallelization and a neural network approach for categorization. ... -
20
asyncoro
Python framework for asynchronous, concurrent, distributed programming
asyncoro is a Python framework for developing concurrent, distributed, network programs with asynchronous completions and coroutines. Asynchronous completions implemented in asyncoro are sockets (non-blocking sockets), database cursors, sleep timers and locking primitives. Programs developed with asyncoro have same logic and structure as Python programs with threads, except for a few syntactic changes. asyncoro supports socket I/O notification mechanisms epoll, kqueue, /dev/poll (and poll and select, where necessary), and Windows I/O Completion Ports (IOCP) for high performance and scalability, and SSL for security -
21
mod_openopc
just plain WORKS
mod_openopc is a full featured implementation of the OpenOPC library for Python. It is cross platform compatible (full Python 2.5 and up). We used to recommend running it on a POSIX platform (Unix / Linux), but thanks to modest efforts since 2014, performance in Windows OS environments is excellent. -
22
Ganglia
Scalable, distributed monitoring system for high-performance computing
Ganglia is a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids. It is based on a hierarchical design targeted at federations of clusters. Supports clusters up to 2000 nodes in size. -
23
netmonsql
Network performance monitoring and logging script for Cisco devices.
NetMon_Cisco_to_SQL.py is a basic network performance monitoring and logging script for Cisco devices. It gathers parameters (CPU Utilization, Used Processor Memory, Used IO Memory, UP Ethernet Interfaces density, in percent velues) from Cisco routers by parsing command outputs and exports them to a MySQL database. NetMon_Query.py is a query script for the MySQL database in the form of an interactive menu for generating statistics. -
24
DataCopy For SQLServer
Data Copy tool for SQL Server and Oracle
Migrate your data from SQLServer to Oracle without creating single dump file. Input is a SQLServer query file defining dataset you want to copy to Oracle. Target table has to exist for copy to go through. Turbo mode offers 5x copy performance improvement. -
25
Data Spooler for SQLServer #SaveUkraine
Extracts table or query data from SQL Server 2005, 2008, 2012
#SaveUkraine #StopRussia #FreeUkraine #StopPutin #CrimeaIsUkraine #UnitedForUkraine #RussiaInvadedUkraine Spools/extracts/dump table or query data from SQL Server 2015, 2008,2012. Serial spool creates single dump file. Turbo mode offers 5x spool performance improvement. Sharded turbo more creates multiple files.
