Showing 509 open source projects for "pipeline"

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  • 1
    DeepCamera

    DeepCamera

    Open-Source AI Camera. Empower any camera/CCTV

    DeepCamera empowers your traditional surveillance cameras and CCTV/NVR with machine learning technologies. It provides open-source facial recognition-based intrusion detection, fall detection, and parking lot monitoring with the inference engine on your local device. SharpAI-hub is the cloud hosting for AI applications that helps you deploy AI applications with your CCTV camera on your edge device in minutes. SharpAI yolov7_reid is an open-source Python application that leverages AI...
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  • 2
    Siddhi Core Libraries

    Siddhi Core Libraries

    Stream Processing and Complex Event Processing Engine

    Fully open source, cloud-native, scalable, micro streaming, and complex event processing system capable of building event-driven applications for use cases such as real-time analytics, data integration, notification management, and adaptive decision-making. Event processing logic can be written using Streaming SQL queries via graphical and source editors, to capture events from diverse data sources, process and analyze them, integrate with multiple services and data stores, and publish...
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  • 3
    Loggie

    Loggie

    A lightweight, cloud-native data transfer agent and aggregator

    Loggie is a lightweight, high-performance, cloud-native agent and aggregator based on Golang. Loggie includes LogConfig/ClusterLogConfig/Interceptor/Sink CRDs, allowing for the creation of data collection, transfer, processing, and sending pipelines through simple YAML file creation. Supports deployment as an independent intermediate machine, which can receive aggregated data sent by Loggie Agent and can also be used to consume and process various data sources. Configure Filebeat and Loggie...
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  • 4
    Logging operator

    Logging operator

    Logging operator for Kubernetes

    The Logging operator solves your logging-related problems in Kubernetes environments by automating the deployment and configuration of a Kubernetes logging pipeline. The Logging operator manages the log collectors and log forwarders of your logging infrastructure, and the routing rules that specify where you want to send your different log messages. You can filter and process the incoming log messages using the flow custom resource of the log forwarder to route them to the appropriate output...
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  • Vivantio IT Service Management Icon
    Vivantio IT Service Management

    Your service operation isn’t one-size-fits all, so your IT service management solution shouldn’t be either

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  • 5
    Kubero

    Kubero

    A free and self-hosted Heroku PaaS alternative for Kubernetes

    Kubero [pronounced: Kube Hero] is a fully self-hosted Internal Developer Platform (IDP) that brings the workflows of Heroku to your Kubernetes cluster. It enables you to deploy your applications with a few clicks. It has a built-in CI/CD pipeline and supports multiple staging environments. Kubero initiates two containers: one for cloning your repository and another for building your app. Once the build is complete, Kubero will launch the final container and make it accessible via the configured...
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  • 6
    SecretScanner

    SecretScanner

    Find secrets and passwords in container images and file systems

    ... private. However, sometimes attackers can easily access secrets due to flawed security policies or inadvertent mistakes by developers. Sometimes developers use default secrets or leave hard-coded secrets such as passwords, API keys, encryption keys, SSH keys, tokens, etc. in container images, especially during rapid development and deployment cycles in CI/CD pipeline. Also, sometimes users store passwords in plain text.
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  • 7
    Otter-Grader

    Otter-Grader

    A Python and R autograding solution

    Otter Grader is a light-weight, modular open-source autograder developed by the Data Science Education Program at UC Berkeley. It is designed to work with classes at any scale by abstracting away the autograding internals in a way that is compatible with any instructor's assignment distribution and collection pipeline. Otter supports local grading through parallel Docker containers, grading using the autograder platforms of 3rd party learning management systems (LMSs), the deployment...
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  • 8
    gruntwork.io website

    gruntwork.io website

    The gruntwork.io website

    The Gruntwork team deploys a Reference Architecture for you, setting you up with all the foundational pieces you need to get started with AWS and Terraform, including your Landing Zone, network topology, CI / CD pipeline, guard rails, monitoring, out-of-the-box compliance, and more. Use the Gruntwork Infrastructure as Code Library (IaC Library), a collection of reusable Terraform code that has been proven in production at hundreds of companies, to deploy the rest of your infrastructure...
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  • 9
    aqueduct LLM

    aqueduct LLM

    Aqueduct allows you to run LLM and ML workloads on any infrastructure

    Aqueduct is an MLOps framework that allows you to define and deploy machine learning and LLM workloads on any cloud infrastructure. Aqueduct is an open-source MLOps framework that allows you to write code in vanilla Python, run that code on any cloud infrastructure you'd like to use, and gain visibility into the execution and performance of your models and predictions. Aqueduct's Python native API allows you to define ML tasks in regular Python code. You can connect Aqueduct to your existing...
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  • 10
    MedicalGPT

    MedicalGPT

    MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training

    MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training.
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  • 11
    Spring Cloud Config Server

    Spring Cloud Config Server

    External configuration (server and client) for Spring Cloud

    ... the deployment pipeline from dev to test and into production, you can manage the configuration between those environments and be certain that applications have everything they need to run when they migrate. The default implementation of the server storage backend uses git, so it easily supports labelled versions of configuration environments as well as being accessible to a wide range of tooling for managing the content. It is easy to add alternative implementations.
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  • 12
    Mara Pipelines

    Mara Pipelines

    A lightweight opinionated ETL framework, halfway between plain scripts

    ... processing: command line tools as the main tool for interacting with databases and data. Single machine pipeline execution based on Python's multiprocessing. No need for distributed task queues. Easy debugging and output logging. Cost based priority queues: nodes with higher cost (based on recorded run times) are run first.
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  • 13
    Jitsu

    Jitsu

    Jitsu is an open-source Segment alternative

    Jitsu is a fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days. Installing Jitsu is a matter of selecting your framework and adding few lines of code to your app. Jitsu is built to be framework agnostic, so regardless of your stack, we have a solution that'll work for your team. Connect data warehouse (Snowflake, Clickhouse, BigQuery, S3, Redshift ot Postgres) and query your data instantly. Jitsu can either stream data in real-time...
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  • 14
    pointblank

    pointblank

    Data quality assessment and metadata reporting for data frames

    With the pointblank package it’s really easy to methodically validate your data whether in the form of data frames or as database tables. On top of the validation toolset, the package gives you the means to provide and keep up-to-date with the information that defines your tables. For table validation, the agent object works with a large collection of simple (yet powerful!) validation functions. We can enable much more sophisticated validation checks by using custom expressions, segmenting...
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  • 15
    NVIDIA Merlin

    NVIDIA Merlin

    Library providing end-to-end GPU-accelerated recommender systems

    NVIDIA Merlin is an open-source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA Merlin...
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  • 16
    Compose

    Compose

    A machine learning tool for automated prediction engineering

    Compose is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. An end user defines an outcome of interest by writing a labeling function, then runs a search to automatically extract training examples from historical data. Its result is then provided to Featuretools for automated feature engineering and subsequently to EvalML for automated machine learning. Prediction problems are structured...
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  • 17
    Cherrybomb

    Cherrybomb

    Cherrybomb is a CLI tool that helps you avoid undefined user behaviour

    Cherrybomb is an CLI tool written in Rust that helps prevent incorrect code implementation early in development. It works by validating and testing your API using an OpenAPI file. Its main goal is to reduce security errors and ensure your API functions as intended. Cherrybomb makes sure your API is working correctly. It checks your API's spec file (OpenAPI Specification) for good practices and makes sure it follows the OAS rules. Then, it tests your API for common issues and vulnerabilities....
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  • 18
    Armeria

    Armeria

    Your go-to microservice framework for any situation

    Build a reactive microservice at your pace, not theirs. Armeria is your go-to microservice framework for any situation. You can build any type of microservice leveraging your favorite technologies, including gRPC, Thrift, Kotlin, Retrofit, Reactive Streams, Spring Boot and Dropwizard. Let's embrace the reality, we almost always have to deal with more than one protocol. It was once Thrift, today it's gRPC, and REST never gets old. At the same time, you sometimes have to handle health check...
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  • 19
    Amazonka

    Amazonka

    A comprehensive Amazon Web Services SDK for Haskell

    ... an additional licensing clause in their header. GHC versions 8.8.4 and 8.10.7 are officially supported and tested on NixOS, Ubuntu, and macOS. GHC 8.6.5 may also work, but is not tested by our continuous integration pipeline. The Nix package manager is used to obtain and build the other dependencies in a hermetic environment.
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  • 20
    PaddleSpeech

    PaddleSpeech

    Easy-to-use Speech Toolkit including Self-Supervised Learning model

    PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks in speech and audio, with state-of-art and influential models. Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing modules, and deployment process. Low barriers to install, CLI, Server, and Streaming Server is available to quick-start your journey. We provide...
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  • 21
    TextBox

    TextBox

    A text generation library with pre-trained language models github.com

    TextBox 2.0 is an up-to-date text generation library based on Python and PyTorch focusing on building a unified and standardized pipeline for applying pre-trained language models to text generation. From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets. From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general...
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  • 22
    Stable Diffusion in Docker

    Stable Diffusion in Docker

    Run the Stable Diffusion releases in a Docker container

    Run the Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint. Run the Stable Diffusion releases on Huggingface in a GPU-accelerated Docker container. By default, the pipeline uses the full model and weights which requires a CUDA capable GPU with 8GB+ of VRAM. It should take a few seconds to create one image. On less powerful GPUs you may need to modify some of the options; see the Examples section for more details. If you lack...
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  • 23
    Haystack

    Haystack

    Haystack is an open source NLP framework to interact with your data

    Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning...
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  • 24
    Tekton Pipelines

    Tekton Pipelines

    A cloud-native Pipeline resource

    Tekton is a powerful and flexible open-source framework for creating CI/CD systems, allowing developers to build, test, and deploy across cloud providers and on-premise systems. Get started with Tekton. Tekton standardizes CI/CD tooling and processes across vendors, languages, and deployment environments. It works well with Jenkins, Jenkins X, Skaffold, Knative, and many other popular CI/CD tools. Tekton lets you create CI/CD systems quickly, giving you scalable, serverless, cloud native...
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  • 25
    Kedro

    Kedro

    A Python framework for creating reproducible, maintainable code

    Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems...
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