Search Results for "mod-apache-snmp" - Page 11
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Showing 434 open source projects for "mod-apache-snmp"
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Google Cloud Vision API examples
Sample code for Google Cloud Vision
The cloud-vision repository is a sample code collection for the Google Cloud Vision API that shows developers how to implement image analysis tasks across a wide range of languages and platforms. It contains examples organized by language and environment, including Go, Java, Node.js, PHP, Python, Ruby, .NET, Android, iOS, and even a Chrome extension, which makes it especially valuable as a cross-platform learning resource. The repository demonstrates concrete image understanding use cases,... -
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Rank-BM25
A Collection of BM25 Algorithms in Python
A collection of algorithms for querying a set of documents and returning the ones most relevant to the query. The most common use case for these algorithms is, as you might have guessed, to create search engines. -
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SageMaker Scikit-Learn Extension
A library of additional estimators and SageMaker tools based on scikit
A library of additional estimators and SageMaker tools based on scikit-learn. This project contains standalone scikit-learn estimators and additional tools to support SageMaker Autopilot. Many of the additional estimators are based on existing scikit-learn estimators. SageMaker Scikit-Learn Extension is a Python module for machine learning built on top of scikit-learn. In order to use the I/O functionalies in the sagemaker_sklearn_extension.externals module, you will also need to install the... -
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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Model Search
Framework that implements AutoML algorithms
Model Search is an AutoML research system for discovering neural network architectures with minimal human intervention. Instead of hand-crafting models, you define a search space and objectives, then the system explores candidate architectures using controllers and population-based strategies. It supports multiple tasks (such as vision or text) by letting you express reusable building blocks—layers, cells, and topologies—that the search can recombine. Training, evaluation, and promotion of... -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related... -
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Nameko
Python framework for building microservices
Write regular Python methods and classes to implement your service. Nameko will manage connections, transports, and concurrency for you. Spin up multiple service instances to easily scale out. Nameko gives you effortless concurrency by yielding workers when they wait for I/O, leaving you free to handle many requests without the worry of threading. Nameko is compatible with almost any protocol, transport or database. Simply use the built-in extensions, build your own or leverage the... -
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StreamAlert
StreamAlert is a serverless, realtime data analysis framework
StreamAlert is a serverless, real-time data analysis framework that empowers you to ingest, analyze, and alert on data from any environment, using data sources and alerting logic you define. Computer security teams use StreamAlert to scan terabytes of log data every day for incident detection and response. Incoming log data will be classified and processed by the rules engine. Alerts are then sent to one or more outputs. Rules are written in Python; they can utilize any Python libraries or... -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. Common network families (MPS/TT, PEPS, MERA, tree networks) are expressed with concise APIs that encourage... -
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PowerfulSeal
A powerful testing tool for Kubernetes clusters.
PowerfulSeal injects failure into your Kubernetes clusters so that you can detect problems as early as possible. It allows for writing scenarios describing complete chaos experiments. -
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PyTorchVideo
A deep learning library for video understanding research
PyTorchVideo is a deep learning library for video understanding, providing modular components and pretrained models for tasks like action recognition, video classification, detection, and self-supervised learning. It is tightly integrated with PyTorch and PyTorch Lightning, offering flexible APIs for building and training spatiotemporal networks. The library includes efficient implementations of state-of-the-art architectures such as SlowFast, X3D, and MViT, optimized for both research... -
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TRFL
TensorFlow Reinforcement Learning
TRFL, developed by Google DeepMind, is a TensorFlow-based library that provides a collection of essential building blocks for reinforcement learning (RL) algorithms. Pronounced “truffle,” it simplifies the implementation of RL agents by offering reusable components such as loss functions, value estimation tools, and temporal difference (TD) learning operators. The library is designed to integrate seamlessly with TensorFlow, allowing users to define differentiable RL objectives and train... -
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DockStream
A Docking Wrapper to Enhance De Novo Molecular Design
DockStream is a docking wrapper providing access to a collection of ligand embedders and docking backends. Docking execution and post hoc analysis can be automated via the benchmarking and analysis workflow. The flexilibity to specifiy a large variety of docking configurations allows tailored protocols for diverse end applications. DockStream can also parallelize docking across CPU cores, increasing throughput. DockStream is integrated with the de novo design platform, REINVENT, allowing one... -
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Differentiable Neural Computer
A TensorFlow implementation of the Differentiable Neural Computer
The Differentiable Neural Computer (DNC), developed by Google DeepMind, is a neural network architecture augmented with dynamic external memory, enabling it to learn algorithms and solve complex reasoning tasks. Published in Nature in 2016 under the paper “Hybrid computing using a neural network with dynamic external memory,” the DNC combines the pattern recognition power of neural networks with a memory module that can be written to and read from in a differentiable way. This allows the... -
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Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications. -
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ML workspace
All-in-one web-based IDE specialized for machine learning
All-in-one web-based development environment for machine learning. The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard)... -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
SageMaker TensorFlow Serving Container is an a open source project that builds docker images for running TensorFlow Serving on Amazon SageMaker. Some of the build and tests scripts interact with resources in your AWS account. Be sure to set your default AWS credentials and region using aws configure before using these scripts. Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. The Dockerfiles... -
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Nerfies
This is the code for Deformable Neural Radiance Fields
Nerfies demonstrates deformation-aware neural radiance fields that reconstruct and render dynamic, real-world scenes from casual video. Instead of assuming a static world, the method learns a canonical space plus a deformation field that maps changing poses or expressions back to that space during training. This lets the system generate photorealistic novel views of nonrigid subjects—faces, bodies, cloth—while preserving fine detail and consistent lighting. The training pipeline handles... -
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speedtest-cli
Command line interface for testing internet bandwidth using speedtest
Command line interface for testing internet bandwidth using speedtest.net. It is not a goal of this application to be a reliable latency reporting tool. Latency reported by this tool should not be relied on as a value indicative of ICMP style latency. It is a relative value used for determining the lowest latency server for performing the actual speed test against. Speedtest CLI brings the trusted technology and global server network behind Speedtest to the command line. Measure internet... -
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Zebrunner Community Edition
Test Automation Management Tool
Zebrunner CE (Community Edition) is a Test Automation Management Tool for continuous testing and continuous deployment. It allows you to run various kinds of tests and gain successive levels of confidence in the code quality. Zebrunner CE is integrated by default with Carina open-source TestNG framework and uses Jenkins as a CI Tool. It is built on top of popular docker solutions and includes Postgres database, Zebrunner Reporting, Jenkins Master/Slaves Nodes, Selenium Hub, Mobile... -
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Gluon CV Toolkit
Gluon CV Toolkit
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection,... -
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nbdev template
Template for nbdev projects
Write, test, document, and distribute software packages and technical articles, all in one place, your notebook. Traditional programming environments throw away the result of your exploration in REPLs or notebooks. nbdev makes exploration an integral part of your workflow, all while promoting software engineering best practices. -
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BudgetML
Deploy a ML inference service on a budget in 10 lines of code
Deploy a ML inference service on a budget in less than 10 lines of code. BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production fast and cheaply. Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST,... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a...