Search Results for "network optimizer"
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Showing 12 open source projects for "network optimizer"
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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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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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MIVisionX
Set of comprehensive computer vision & machine intelligence libraries
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning... -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms. ... -
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
...We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Mycat2
MySQL Proxy using Java NIO based on Sharding SQL, Calcite
...Support parallel extraction of result sets, support the automatic transfer of back-end result sets, support multiple routing notes, and optimizer notes. Parameterization of the requested SQL, cache physics execution plan, and request of the same parameterization SQL, will avoid some analysis and optimization processes. -
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SimSiam
PyTorch implementation of SimSiam
SimSiam is a PyTorch implementation of “Exploring Simple Siamese Representation Learning” by Xinlei Chen and Kaiming He. The project introduces a minimalist approach to self-supervised learning that avoids negative pairs, momentum encoders, or large memory banks—key complexities of prior contrastive methods. SimSiam learns image representations by maximizing similarity between two augmented views of the same image through a Siamese neural network with a stop-gradient operation, preventing... -
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NASH OS
Nash Operating System for Modern Ecommerce
The all-built-in-one, automatic, ready-to-go out-of-box, easy-to-use state-of-the-art, and really awesome NASH OS! Over 25,000+ flexible features and controls and all scalable!! The most powerful solution ever built to instantly deliver new heights of online ecommerce enterprise to you. -
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SG4 UsePack Optimizer
SG4 UsePack Optimizer is Portable compact solution for better use PC
This pack is for better usage PC in the portable mode for Windows. All full preconfigured and automatized for best simple use!!! * Network Ultimate caching and removing Ads from HTTP web pages * System: AnVir task manager preconfigured for best performance of processes * Others: (for manual running) Killer - Ultimate simple process killer preconfigured for best performance and security of processes Install: No, it is portable - extract file and run. -
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PHP Class Generator will generate PHP 5 source code from your mysql BD model. PCG The internal optimizer self create objects on demand and provide fluid interface. You can plug-it on Zend Framework. PCG takes advantage of technological lead of language.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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Filu
Filu is aimed to support your stock trading
Filu is aimed to support your stock trading. Some of its features are: Market Scanner, Indicator scripting, TA-Lib support, Postgres driven FIs and indicators, Trading scripting, Backtester with optimizer functionality -
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Entirely source-based system. The goal is to provide extremely flexible but easy-to-compile Linux-like OS ready for SSI clusters. Installation procedure is network-independent and automated.
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H2O is a collection of C routines and applications designed for the optimization of multi-reservoir and multi-period hydrothermal power production scheduling. H2O is built upon nonlinear network algorithms and heuristic techniques for higher efficiency.
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