Search Results for "stack" - Page 6
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Showing 195 open source projects for "stack"
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DeepSeek VL
Towards Real-World Vision-Language Understanding
DeepSeek-VL is DeepSeek’s initial vision-language model that anchors their multimodal stack. It enables understanding and generation across visual and textual modalities—meaning it can process an image + a prompt, answer questions about images, caption, classify, or reason about visuals in context. The model is likely used internally as the visual encoder backbone for agent use cases, to ground perception in downstream tasks (e.g. answering questions about a screenshot). -
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Bufferstack.IO IIoT-Gateway
IIoT Gateway for interfacing industrial,home automation applications
This is Bufferstack. IO IIoT Gateway based on LTS stable software base which allows one to develop, deploy and host IOT, IIoT based applications using NodeJS, Python, influxdb, mosquitto mqtt broker. This is live+install CD, and for better experience in using it, please install it on your HDD/VM/Cloud -
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Amphion
Toolkit for audio, music, and speech generation
...Built on the broader OpenMMLab ecosystem, Amphion follows modular design patterns and configuration systems similar to other OpenMMLab projects, easing adoption for users who are already familiar with that stack. -
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python-proxy
HTTP/HTTP2/HTTP3/Socks4/Socks5/Shadowsocks/ShadowsocksR/SSH
python-proxy, also known as pproxy, is a lightweight proxy tool written in Python for flexible local and remote traffic forwarding. It supports multiple proxy protocols, making it useful for developers, testers, and network administrators who need a compact proxy layer without a heavy service stack. The project can operate as a client, server, forward proxy, reverse proxy, or protocol bridge depending on how it is configured. It supports HTTP, SOCKS4, SOCKS5, Shadowsocks, and newer transport options such as HTTP/2, HTTP/3, and QUIC when the required dependencies are installed. python-proxy also includes SSL-related options, chaining behavior, and multiple connection modes for advanced routing setups. ... -
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autollm
Ship RAG based LLM web apps in seconds
autollm is an open-source Python framework designed to make it much faster to build retrieval-augmented generation applications and expose them as usable services with minimal setup. The project focuses on simplifying the usual stack of model selection, document ingestion, vector storage, querying, and API deployment into a more unified developer experience. Its core idea is that a developer can create a query engine from a document set in just a few lines and then turn that same engine into a FastAPI application almost instantly. AutoLLM supports a broad range of language models and vector databases, which makes it useful for teams that want flexibility without rewriting their application architecture every time they switch providers. ... -
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dbt-re-data
re_data - fix data issues before your users & CEO would discover them
re_data is an open-source data reliability framework for the modern data stack. Currently, re_data focuses on observing the dbt project (together with underlaying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Data transformations in re_data are implemented and exposed as models & macros in this dbt package. Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
...A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed. Containerizing your model and code enables fast and reliable deployment of your model. The SageMaker Inference Toolkit implements a model serving stack and can be easily added to any Docker container, making it deployable to SageMaker. This library's serving stack is built on Multi Model Server, and it can serve your own models or those you trained on SageMaker using machine learning frameworks with native SageMaker support. -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. ... -
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Vimspector
A multi-language debugging system for Vim
...It supports multiple languages (C++, Python, TCL among others) via the Debug Adapter Protocol (DAP) model and provides an in-editor UI for viewing scopes, variables, stack frames, output windows and more. You configure it per-project via a .vimspector.json file (or per file type) specifying the adapter, launch configuration or attach process. Once configured you can run, pause, step into/out/over, set and clear breakpoints and inspect variables directly in Vim splits or floating windows. The UI is heavy compared to typical Vim plugins (multiple panes, layouts) but it fills the gap for users who want an integrated debug workflow without leaving the editor. -
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UnionML
Build and deploy machine learning microservices
...Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. This helps you maintain consistent code across your ML stack, from training to prediction logic. -
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minGPT
A minimal PyTorch re-implementation of the OpenAI GPT
minGPT is a minimalist, educational re-implementation of the GPT (Generative Pretrained Transformer) architecture built in PyTorch, designed by Andrej Karpathy to expose the core structure of a transformer-based language model in as few lines of code as possible. It strips away extraneous bells and whistles, aiming to show how a sequence of token indices is fed into a stack of transformer blocks and then decoded into the next token probabilities, with both training and inference supported. Because the whole model is around 300 lines of code, users can follow each step—from embedding lookup, positional encodings, multi-head attention, feed-forward layers, to output heads—and thus demystify how GPT-style models work beneath the surface. ... -
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Opta
The next generation of Infrastructure-as-Code
...Opta has a vast library of modules (like EKS, RDS, DynamoDB, GKE, Cloud SQL, and even third-party services like Datadog) that engineers can compose together to build their ideal infrastructure stack. It's built on top of Terraform, and designed so engineers aren’t locked in – anyone can write custom Terraform or even take the Opta-generated Terraform and work independently. -
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Grow.dev
A declarative website generator designed for high-quality websites
Grow.dev is a static site generator optimized for building highly interactive, localized microsites. Grow.dev focuses on providing optimal workflows and developer ergonomics for creating projects that are highly maintainable in the long term. Grow.dev encourages a strong but simple separation of content and presentation and makes maintaining content in different locales and environments a snap. -
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WaveRNN
WaveRNN Vocoder + TTS
WaveRNN is a PyTorch implementation of DeepMind’s WaveRNN vocoder, bundled with a Tacotron-style TTS front end to form a complete text-to-speech stack. As a vocoder, WaveRNN models raw audio with a compact recurrent neural network that can generate high-quality waveforms more efficiently than many traditional autoregressive models. The repository includes scripts and code for preprocessing datasets such as LJSpeech, training Tacotron to produce mel spectrograms, training WaveRNN on those spectrograms (with optional GTA data), and finally generating audio. ... -
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RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
Implementation of RQ Transformer, which proposes a more efficient way of training multi-dimensional sequences autoregressively. This repository will only contain the transformer for now. You can use this vector quantization library for the residual VQ. This type of axial autoregressive transformer should be compatible with memcodes, proposed in NWT. It would likely also work well with multi-headed VQ. I also think there is something deeper going on, and have generalized this to any number of... -
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RedELK
Red Team's SIEM - tool for Red Teams used for tracking and alarming
RedELK is a tracking and monitoring tool for Red Teams, providing insights into engagements and supporting real-time situational awareness for offensive operations. -
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yowsup
The WhatsApp lib
...Some of those features are not necessarily exclusive to WhatsApp and therefore it only made sense to maintain some parts as standalone projects. A running Yowsup app is a stack of several layers. A layer is a bidirectional channel that transforms the data passing through it before passing it to underlying or the above layer. -
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Mycroft
Mycroft Core, the Mycroft Artificial Intelligence platform
...And especially not at the cost of their (or your) privacy! Your customers will be able to upgrade their experience with premium content and services. The Mycroft open source voice stack can be freely remixed, extended, and deployed anywhere. Mycroft may be used in anything from a science project to a global enterprise environment. -
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suc
Stack usage calculator
Calculates stack consumed by every application call tree using, as a primary source, different files generated during build. Checks completeness of collected data and supports manual input of those that are missing. -
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itamm
Tool to design and share enterprise solutions, services and processes
...It also has its own extensible notation for visualizing enterprise environment objects. IT(A)-MM is easy to use and allows you to use it wherever you are. Using IT(A)-MM can be the first step towards deploy of the AIAS "A-STACK" to the enterprise environment. AIAS "A-STACK" is the complex tool for enterprise architecture management, IT infrastructure monitoring, meta-data management. See more on https://www.itursoft.ru/solutions -
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CVE-2021-31166
Remote HTTP.sys use-after-free triggered remotely
This is a proof of concept for CVE-2021-31166 ("HTTP Protocol Stack Remote Code Execution Vulnerability"), a use-after-free dereference in http.sys patched by Microsoft in May 2021. The bug itself happens in http!UlpParseContentCoding where the function has a local LIST_ENTRY and appends an item to it. When it's done, it moves it into the Request structure; but it doesn't NULL out the local list. -
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PyCls
Codebase for Image Classification Research, written in PyTorch
...Model definitions are concise and modular, making it easy to prototype new blocks or swap backbones while keeping the rest of the pipeline unchanged. Pretrained weights and evaluation scripts cover common datasets, and the logging/metric stack is designed for quick comparison across runs. Practitioners use pycls both as a baseline factory and as a scaffold for new classification backbones. -
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FastAPI + React
Cookiecutter Template for FastAPI
FastAPI-React is a cookiecutter template that combines FastAPI and React to create full-stack applications. It integrates PostgreSQL, SQLAlchemy, and Docker to provide a comprehensive development environment. -
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Keras TCN
Keras Temporal Convolutional Network
...The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the maximum number of steps back in time from current sample at time T, that a filter from (block, layer, stack, TCN) can hit (effective history) + 1. The receptive field of the TCN can be calculated. Once keras-tcn is installed as a package, you can take a glimpse of what is possible to do with TCNs. Some tasks examples are available in the repository for this purpose. -
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Makani
Makani was developed a commercial-scale airborne wind turbine
Makani was an ambitious Google X project that sought to harness wind energy using airborne wind turbines — autonomous kites capable of generating power while flying in crosswind patterns. This open-source repository contains the complete software stack that powered Makani’s research and flight systems, including the flight simulator, autopilot controller, avionics firmware, visualization tools, and ground control software. The software enables simulation, control, and analysis of the Makani M600 turbine system, designed to operate offshore and autonomously manage complex aerodynamic behaviors. ...
