Search Results for "apache server" - Page 5
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Showing 127 open source projects for "apache server"
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kolosal
Open Source and Lightweight Local LLM Platform
Kolosal AI is the leading open-source local LLM platform. Download, train, and run local LLM models on your device with no cloud dependencies. An opensource and lightweight alternative to LM Studio. -
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Bandicoot
fast C++ library for GPU linear algebra & scientific computing
* Fast GPU linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use * Provides high-level syntax and functionality deliberately similar to Matlab * Provides an API that is aiming to be compatible with Armadillo for easy transition between CPU and GPU linear algebra code * Useful for algorithm development directly in C++, or quick conversion of research code into production environments * Distributed under the permissive... -
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Openfire LLM Chatbot Plugin
LLM Chatbot Assistant for Openfire server
This plugin is a wrapper to hosted AI Inference server for LLM chat models. It uses the Botz API to create a chatbot in Openfire which will engage in XMPP chat and groupchat conversations. -
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LLaVA
Visual Instruction Tuning: Large Language-and-Vision Assistant
Visual instruction tuning towards large language and vision models with GPT-4 level capabilities. -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist... -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
Serve machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the... -
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GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
GLM-130B is an open bilingual (English and Chinese) dense language model with 130 billion parameters, released by the Tsinghua KEG Lab and collaborators as part of the General Language Model (GLM) series. It is designed for large-scale inference and supports both left-to-right generation and blank filling, making it versatile across NLP tasks. Trained on over 400 billion tokens (200B English, 200B Chinese), it achieves performance surpassing GPT-3 175B, OPT-175B, and BLOOM-176B on multiple... -
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Exadel CompreFace
Leading free and open-source face recognition system
Exadel CompreFace is a free and open-source face recognition GitHub project. Essentially, it is a docker-based application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use CompreFace. The system provides REST API for face recognition, face verification, face detection, face mask detection, landmark detection, age, and gender recognition. The solution also features a role management system that allows you to... -
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langchain-prefect
Tools for using Langchain with Prefect
Large Language Models (LLMs) are interesting and useful - building apps that use them responsibly feels like a no-brainer. Tools like Langchain make it easier to build apps using LLMs. We need to know details about how our apps work, even when we want to use tools with convenient abstractions that may obfuscate those details. Prefect is built to help data people build, run, and observe event-driven workflows wherever they want. It provides a framework for creating deployments on a whole... -
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Diffusion WebUI Colab
Choose your diffusion models and spin up a WebUI on Colab in one click
The most simplistic Colab with most models included by default. Custom models can be added easily. Stable Diffusion 2.0 in testing phase. Choose your diffusion models and spin up a WebUI on Colab in one click. Share your generations in our mastodon server - (This is hosted by a third party. I am not associated with the instance in any way.) The instructions are on the Colab. -
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FedLab
A flexible Federated Learning Framework based on PyTorch
A Python-based framework for federated learning simulation, emphasizing modularity, communication efficiency, and algorithmic flexibility. Supports both server- and client-side customization for research and development purposes. -
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Fedhf
A Flexible Federated Learning Simulator
FedHF is a Python-based simulator for flexible, heterogeneous, and asynchronous federated learning research. It provides configurable resource models, supports asynchronous protocols, and accelerates experimentation. -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
SageMaker MXNet Inference Toolkit is an open-source library for serving MXNet models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain MXNet model types and utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep... -
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TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
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Google Research Football
Check out the new game server
Google Research Football is a reinforcement learning environment simulating soccer matches. It focuses on learning complex behaviors such as team collaboration and strategy formation in competitive settings. -
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DeepStack
The World's Leading Cross Platform AI Engine for Edge Devices
DeepStack is an AI API engine that serves pre-built models and custom models on multiple edge devices locally or on your private cloud. DeepStack runs completely offline and independent of the cloud. You can also install and run DeepStack on any cloud VM with docker installed to serve as your private, state-of-the-art and real-time AI server. -
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Hugging Face Transformer
CPU/GPU inference server for Hugging Face transformer models
Optimize and deploy in production Hugging Face Transformer models in a single command line. At Lefebvre Dalloz we run in-production semantic search engines in the legal domain, in the non-marketing language it's a re-ranker, and we based ours on Transformer. In that setup, latency is key to providing a good user experience, and relevancy inference is done online for hundreds of snippets per user query. Most tutorials on Transformer deployment in production are built over Pytorch and FastAPI.... -
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SparrowRecSys
A Deep Learning Recommender System
SparrowRecSys is an open-source deep learning recommendation system framework designed to demonstrate the architecture and implementation of modern industrial-scale recommender systems. The project integrates multiple machine learning models and data processing pipelines to simulate how real-world recommendation platforms operate. It includes components for offline data processing, feature engineering, model training, real-time data updates, and online recommendation services. SparrowRecSys... -
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alvd
alvd = A Lightweight Vald. A lightweight distributed vector search
A lightweight distributed vector search engine based on Vald codebase. Vald is an awesome highly scalable distributed vector search engine works on Kubernetes. It has great features such as file-based backup, and metrics-based ordering of Agents. Also, Vald is highly configurable using YAML files. It works without Kubernetes, single binary (less than 30MB), easy to run (can be configured by command-line options), and consists of Agent and Server. alvd has almost the same features as Vald's... -
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DownloadBot
A distributed cross-platform Telegram Bot
A distributed cross-platform Telegram Bot that can control your Aria2 server, control server files and also upload to OneDrive / Google Drive. This project is mainly to use a small hard disk server for offline downloading, for large BitTorrent files to be downloaded in sections according to the size of the hard disk, each time downloading a part, then uploading the network disk, deleting and then downloading the other parts, until all the files are downloaded. At the same time, communication... -
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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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TerarkDB
A RocksDB compatible KV storage engine with better performance
TerarkDB is an open-source storage engine designed as a replacement for RocksDB — offering improved performance, better compression, and optimized tail-latency for key-value workloads. It aims to be drop-in compatible with existing RocksDB setups: you can migrate most RocksDB instances over to TerarkDB without rewriting your storage logic. Under the hood, TerarkDB employs advanced data structures and compression strategies to reduce I/O, memory usage, and latency variability — delivering... -
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Seldon Server
Machine learning platform and recommendation engine on Kubernetes
Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more... -
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GGPServer is a web server based on Apache Tomcat that allows to play and watch matches between registered general game glaying (http://en.wikipedia.org/wiki/General_Game_Playing) programs.
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Apache PredictionIO
Machine learning server for developers and ML engineers
Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. Quickly build and deploy an engine as a web service on production with customizable templates; respond to dynamic queries in real-time once deployed as a web service; evaluate and tune multiple engine variants systematically; unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics; speed up machine learning modeling with systematic processes and pre-built evaluation measures; support machine learning and data processing libraries such as Spark MLLib and OpenNLP; implement your own machine learning models and seamlessly incorporate them into your engine; simplify data infrastructure management.
