Search Results for "deploy" - Page 2
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Showing 90 open source projects for "deploy"
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Terraform Examples and Modules for GC
End-to-end modular samples and landing zones toolkit for Terraform
Terraform Examples and Modules for GC is a comprehensive infrastructure-as-code toolkit built on Terraform that enables organizations to design, deploy, and manage enterprise-grade Google Cloud environments using modular and reusable components. It provides a collection of end-to-end blueprints and composable modules that allow teams to implement standardized cloud architectures such as landing zones, networking configurations, and security frameworks. The project is designed to accelerate cloud adoption by offering opinionated yet flexible patterns aligned with Google Cloud best practices, helping organizations bootstrap their environments quickly while maintaining governance and scalability. ... -
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Kubespray
Deploy a Production Ready Kubernetes Cluster
Can be deployed on AWS, GCE, Azure, OpenStack, vSphere, Equinix Metal (bare metal), Oracle Cloud Infrastructure (Experimental), or Baremetal. Highly available cluster. Composable (Choice of the network plugin for instance). Supports most popular Linux distributions. Continuous integration tests. The list of available docker versions is 18.09, 19.03, and 20.10. The recommended docker version is 20.10. The kubelet might break on docker's non-standard version numbering (it no longer uses... -
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codex-keysmith
Version-independent Codex instruction deployment
...Status checks and dry runs show the target directories, prompt source, and planned modifications before deployment. Layered uninstall removes only the newest deployment while preserving unrelated configuration changes. Users can restore hooks separately, deploy to multiple discovered directories, and supply their own instruction file. -
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Ray
A unified framework for scalable computing
...Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
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5
Claude for Legal
A suite of plugins for legal workflows
...It is designed for in-house teams, law firms, clinics, and legal students who need structured assistance across commercial, corporate, employment, privacy, product, litigation, regulatory, AI governance, and IP work. The project can run as a Claude plugin or through Claude’s Managed Agents API, giving teams flexibility in how they deploy the same prompts and skills. Its workflows include contract review, NDA triage, diligence review, DSAR response, employment policy drafting, trademark screening, regulatory monitoring, and litigation support. The repository emphasizes attorney oversight, source attribution, jurisdiction awareness, conservative legal assumptions, and approval gates before anything is filed, sent, or relied on. -
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bitnet.cpp
Official inference framework for 1-bit LLMs
...The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources. -
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Jenkins-Zero-To-Hero
Install Jenkins and configure Docker
...The folder structure includes practical examples such as java-maven-sonar-argocd-helm-k8s and python-jenkins-argocd-k8s, showing real CI/CD flows that build, test, analyze, containerize, and deploy apps to Kubernetes via Argo CD in a GitOps style. The README walks through detailed step-by-step commands and screenshots, making it accessible to beginners who are unfamiliar with Jenkins, AWS, or pipelines. -
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AWS Chalice
Python Serverless Microframework for AWS
Chalice is a Python microframework for writing and deploying serverless applications on AWS with minimal ceremony. You define routes, event handlers, and background tasks in plain Python, and Chalice turns them into AWS Lambda functions wired to API Gateway, Amazon EventBridge schedulers, S3/SNS/SQS triggers, and more. A single command builds your app, bundles dependencies, generates infrastructure templates, and deploys to your account with sensible defaults. The framework includes local... -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
...Its architecture enforces a clear separation of concerns between frontend and backend, which improves maintainability and scalability as applications grow in complexity. The system is designed to support rapid prototyping, enabling developers to iterate on UI and backend logic independently and deploy changes quickly. -
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Recursive Language Models
General plug-and-play inference library for Recursive Language Models
RLM (short for Reinforcement Learning Models) is a modular framework that makes it easier to build, train, evaluate, and deploy reinforcement learning (RL) agents across a wide range of environments and tasks. It provides a consistent API that abstracts away many of the repetitive engineering patterns in RL research and application work, letting developers focus on modeling, experimentation, and fine-tuning rather than infrastructure plumbing. Within the framework, you can define custom agents, environments, policy networks, and reward structures while leveraging built-in dataset utilities, logging, and checkpointing for reproducible experiments. ... -
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Superduper
Superduper: Integrate AI models and machine learning workflows
Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This... -
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One-Lin3r
Gives you one-liners that aids in penetration testing operations
One-Lin3r is a modular and lightweight penetration testing framework designed to provide security professionals with a centralized collection of one-liner commands for a wide range of offensive security tasks. It focuses on simplifying the execution of complex commands by organizing them into categorized modules, allowing users to quickly access and deploy payloads without manually searching or crafting them. The framework includes hundreds of prebuilt one-liners covering areas such as reverse shells, privilege escalation, bind shells, and payload delivery techniques. It is designed to be cross-platform, supporting environments like Windows, Linux, macOS, and BSD systems, making it highly versatile for different penetration testing scenarios. ... -
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AWS ParallelCluster Node
Python package installed on the Amazon EC2 instances
aws-parallelcluster-node is the python package installed on the Amazon EC2 instances launched as part of AWS ParallelCluster. AWS ParallelCluster is an AWS-supported Open Source cluster management tool that makes it easy for you to deploy and manage High-Performance Computing (HPC) clusters in the AWS cloud. Built on the Open Source CfnCluster project, AWS ParallelCluster enables you to quickly build an HPC compute environment in AWS. It automatically sets up the required compute resources and a shared filesystem and offers a variety of batch schedulers such as AWS Batch and Slurm. ... -
14
Webifier
A GitHub Action to deploy Notebooks, Markdowns
Webifier is a stand-alone build tool for converting any repository into a deployable jekyll website. You can define your pages via yaml files and provide notebooks, markdown and pdf and other files for Webifier to render. It uses python markdown providing additional control over attributes and other extensive functionalities. It lets you define and direct how your web pages feel and automatically manages your assets, making it a perfect solution for fast static website development and a... -
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can... -
16
maXbox5
maXbox is a script tool engine, compiler and source lib all in one exe
The Art of Coding: maXbox is a script tool engine, compiler and source lib all in one exe to design and code your scripts in a shellbook! It supports Pascal, Delphi (VCL), Python (P4D), PowerShell and Java Script (Edge WebView). -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual... -
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nimbletools-core
Open Source MCP Platform - Build, scale, and deploy MCP servers on k8s
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CodinIT.dev
Free, local, open-source AI app builder
CodinIT.dev is a free, local, open source AI app builder that lets you go from idea to full-stack application entirely on your machine, no coding required, just chat with AI. You can build unlimited apps with real-time previews, instant undo, and responsive, frictionless workflows. Deep Supabase integration means you can create UI and backend logic in one cohesive environment, while the model-agnostic architecture lets you connect to any AI, whether cloud-based (Gemini 3 Pro, GPT-5,... -
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REST APIs with Flask and Python
Projects and e-book for our course, REST APIs with Flask and Python
A full course to teach you how to use Flask and Python to make REST APIs using multiple Flask extensions and PostgreSQL. Learn Flask, Docker, PostgreSQL, and more. Build professional-grade REST APIs with Python. No more outdated tutorials. Use Python 3.10+ and the latest versions of every Flask extension and library. Run your apps in Docker, host your code with Git, write documentation with Swagger, and test your APIs while developing. Learn how to perform user authentication using JWTs and... -
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Piku
The tiniest PaaS you've ever seen
Piku is a minimalistic Platform as a Service (PaaS) that enables users to deploy applications to their own servers using simple Git push commands. -
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uwsgi-nginx-flask
Docker image with uWSGI and Nginx for Flask applications in Python
...This Docker image allows you to create Flask web applications in Python that run with uWSGI and Nginx in a single container. The combination of uWSGI with Nginx is a common way to deploy Python Flask web applications. It is widely used in the industry and would give you decent performance. If you are starting a new project, you might benefit from a newer and faster framework based on ASGI instead of WSGI (Flask and Django are WSGI-based). Also, if you want to use new technologies like WebSockets it would be easier (and possible) with a newer framework based on ASGI, like FastAPI or Starlette. ... -
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pipeless
A computer vision framework to create and deploy apps in minutes
Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. -
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Example Streamlit
Example Streamlit app that you can fork to test out share.streamlit.io
streamlit-example is an open source sample app created by the Streamlit team to demonstrate how to quickly build and deploy applications with Streamlit. The repository contains a minimal Python app (streamlit_app.py) that can be customized by editing the source file. It is designed for use with share.streamlit.io, allowing developers to fork the repo and instantly deploy their own interactive app. The project includes basic dependencies defined in requirements.txt and supports containerized development via .devcontainer. ... -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own infrastructure to ensure data privacy. With long chain-serve, you can craft REST/WebSocket APIs, spin up LLM-powered conversational Slack bots, or wrap your LangChain apps into FastAPI packages on the cloud or on-premises.
