Best AI tools for< Deploy Docker Containers >

GrapixAI is a leading provider of low-cost cloud GPU rental services and AI server solutions. The company's focus on flexibility, scalability, and cutting-edge technology enables a variety of AI applications in both local and cloud environments. GrapixAI offers the lowest prices for on-demand GPUs such as RTX4090, RTX 3090, RTX A6000, RTX A5000, and A40. The platform provides Docker-based container ecosystem for quick software setup, powerful GPU search console, customizable pricing options, various security levels, GUI and CLI interfaces, real-time bidding system, and personalized customer support.

Duckietown is a platform for delivering cutting-edge robotics and AI learning experiences. It offers teaching resources to instructors, hands-on activities to learners, an accessible research platform to researchers, and a state-of-the-art ecosystem for professional training. Duckietown's mission is to make robotics and AI education state-of-the-art, hands-on, and accessible to all.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

Prodvana is an intelligent deployment platform that helps businesses automate and streamline their software deployment process. It provides a variety of features to help businesses improve the speed, reliability, and security of their deployments. Prodvana is a cloud-based platform that can be used with any type of infrastructure, including on-premises, hybrid, and multi-cloud environments. It is also compatible with a wide range of DevOps tools and technologies. Prodvana's key features include: Intent-based deployments: Prodvana uses intent-based deployment technology to automate the deployment process. This means that businesses can simply specify their deployment goals, and Prodvana will automatically generate and execute the necessary steps to achieve those goals. This can save businesses a significant amount of time and effort. Guardrails for deployments: Prodvana provides a variety of guardrails to help businesses ensure the security and reliability of their deployments. These guardrails include approvals, database validations, automatic deployment validation, and simple interfaces to add custom guardrails. This helps businesses to prevent errors and reduce the risk of outages. Frictionless DevEx: Prodvana provides a frictionless developer experience by tracking commits through the infrastructure, ensuring complete visibility beyond just Docker images. This helps developers to quickly identify and resolve issues, and it also makes it easier to collaborate with other team members. Intelligence with Clairvoyance: Prodvana's Clairvoyance feature provides businesses with insights into the impact of their deployments before they are executed. This helps businesses to make more informed decisions about their deployments and to avoid potential problems. Easy integrations: Prodvana integrates seamlessly with a variety of DevOps tools and technologies. This makes it easy for businesses to use Prodvana with their existing workflows and processes.

Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.

Kapa.ai is an AI-powered platform that provides instant answers to technical questions by transforming knowledge bases into reliable chatbots. Trusted by leading teams like OpenAI, Docker, and Reddit, Kapa.ai offers a self-service platform to build and manage custom AI assistants, deploy AI chatbots in various channels, and optimize documentation with analytics. With over 40 technical source connectors and LLM-optimized knowledge sources, Kapa.ai helps organizations improve user experience, reduce support tickets, and enhance product decisions.

Seldon is an MLOps platform that helps enterprises deploy, monitor, and manage machine learning models at scale. It provides a range of features to help organizations accelerate model deployment, optimize infrastructure resource allocation, and manage models and risk. Seldon is trusted by the world's leading MLOps teams and has been used to install and manage over 10 million ML models. With Seldon, organizations can reduce deployment time from months to minutes, increase efficiency, and reduce infrastructure and cloud costs.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

The website is currently displaying a 503 error message indicating that the deployment is paused by the owner. It seems to be related to Vercel, a platform for deploying websites and applications. The error message provides a code and ID for reference, and suggests contacting the website owner if you are a visitor or reading the documentation section if you are the owner.

Azure Static Web Apps is a platform provided by Microsoft Azure for building and deploying modern web applications. It allows developers to easily host static web content and serverless APIs with seamless integration to popular frameworks like React, Angular, and Vue. With Azure Static Web Apps, developers can quickly set up continuous integration and deployment workflows, enabling them to focus on building great user experiences without worrying about infrastructure management.

PoplarML is a platform that enables the deployment of production-ready, scalable ML systems with minimal engineering effort. It offers one-click deploys, real-time inference, and framework agnostic support. With PoplarML, users can seamlessly deploy ML models using a CLI tool to a fleet of GPUs and invoke their models through a REST API endpoint. The platform supports Tensorflow, Pytorch, and JAX models.

Hanabi.rest is an AI-based API building platform that allows users to create REST APIs from natural language and screenshots using AI technology. Users can deploy the APIs on Cloudflare Workers and roll them out globally. The platform offers a live editor for testing database access and API endpoints, generates code compatible with various runtimes, and provides features like sharing APIs via URL, npm package integration, and CLI dump functionality. Hanabi.rest simplifies API design and deployment by leveraging natural language processing, image recognition, and v0.dev components.

Superflows is a tool that allows you to add an AI Copilot to your SaaS product. This AI Copilot can answer questions and perform tasks for users via chat. It is designed to be easy to set up and configure, and it can be integrated into your codebase with just a few lines of code. Superflows is a great way to improve the user experience of your SaaS product and help users get the most out of your software.

Outfit AI is an AI tool that enables users to design and deploy AI models or workflows as user-ready applications in minutes. It allows users to create custom user interfaces for their AI-powered apps by dropping in an API key from Replicate or Hugging Face. With Outfit AI, users can have creative control over the design of their apps, build complex workflows without any code, and optimize prompts for better performance. The tool aims to help users launch their models faster, save time, and enhance their AI applications with a built-in product copilot.

WWWAI.site is an AI-powered platform that revolutionizes web creation by allowing users to create and deploy websites using natural language input and advanced AI agents. The platform leverages specialized AI agents, such as Code Creation, Requirement Analysis, Concept Setting, and Error Validation, along with Claude API for language processing capabilities. Model Context Protocol (MCP) ensures consistency across all components, while users can choose between GitHub or CloudFlare for deployment. The platform is currently in beta testing with limited availability, offering users a seamless and innovative website creation experience.

IBM Watsonx is an enterprise studio for AI builders. It provides a platform to train, validate, tune, and deploy AI models quickly and efficiently. With Watsonx, users can access a library of pre-trained AI models, build their own models, and deploy them to the cloud or on-premises. Watsonx also offers a range of tools and services to help users manage and monitor their AI models.

Lazy AI is a platform that enables users to build full stack web applications 10 times faster by utilizing AI technology. Users can create and modify web apps with prompts and deploy them to the cloud with just one click. The platform offers a variety of features including AI Component Builder, eCommerce store creation, Crypto Arbitrage Scraper, Text to Speech Converter, Lazy Image to Video generation, PDF Chatbot, and more. Lazy AI aims to streamline the app development process and empower users to leverage AI for various tasks.

PixieBrix is an AI engagement platform that allows users to build, deploy, and manage internal AI tools to drive team productivity. It unifies AI landscapes with oversight and governance for enterprise scale. The platform is enterprise-ready and fully customizable to meet unique needs, and can be deployed on any site, making it easy to integrate into existing systems. PixieBrix leverages the power of AI and automation to harness the latest technology to streamline workflows and take productivity to new heights.

Datature is an all-in-one platform for building and deploying computer vision models. It provides tools for data management, annotation, training, and deployment, making it easy to develop and implement computer vision solutions. Datature is used by a variety of industries, including healthcare, retail, manufacturing, and agriculture.

Amazon Bedrock is a cloud-based platform that enables developers to build, deploy, and manage serverless applications. It provides a fully managed environment that takes care of the infrastructure and operations, so developers can focus on writing code. Bedrock also offers a variety of tools and services to help developers build and deploy their applications, including a code editor, a debugger, and a deployment pipeline.

Prompt Optimizer is a powerful AI prompt optimization tool that helps you write better AI prompts, improving AI output quality. It supports both web application and Chrome extension usage. The tool features intelligent optimization for prompt words, real-time testing to compare before and after optimization, integration with multiple mainstream AI models, client-side processing for security, encrypted local storage for data privacy, responsive design for user experience, and more.

Discord AI Bot is a chatbot tool designed to interact with Ollama and AUTOMATIC1111 Stable Diffusion on Discord. The bot allows users to set up and configure a Discord bot to communicate with the mentioned AI models. Users can follow step-by-step instructions to install Node.js, Ollama, and the required dependencies, create a Discord bot, and interact with the bot by mentioning it in messages. Additionally, the tool provides set-up instructions for Docker users to easily deploy the bot using Docker containers. Overall, Discord AI Bot simplifies the process of integrating AI chatbots into Discord servers for interactive communication.

otto-m8 is a flowchart based automation platform designed to run deep learning workloads with minimal to no code. It provides a user-friendly interface to spin up a wide range of AI models, including traditional deep learning models and large language models. The tool deploys Docker containers of workflows as APIs for integration with existing workflows, building AI chatbots, or standalone applications. Otto-m8 operates on an Input, Process, Output paradigm, simplifying the process of running AI models into a flowchart-like UI.

Next generation artificial intelligent ERP system. On the basis of ERP business, we have expanded GPT-3.5. Individually or company can fine-tune your model through our system. You can provide fully automated business form submission operations through your simple description, and you can chat, interact, and consult information with GPT. You can deploy through Docker to quickly start and use. Completely free project. Enginsh / 简体中文.

AI Deadlines is a web app that displays submission deadlines for top AI conferences like NeurIPS and ICLR. It helps researchers know when to submit their papers. The data is fetched from a GitHub repository and updated automatically using a CRON job. The project is based on an existing repository and features a new UI. Users can contribute by updating conference deadlines in the provided YAML file. The app can be run locally with Node.js and npm or deployed using Docker. It is built with Vite, TypeScript, React, shadcn-ui, and Tailwind CSS. The project is licensed under MIT.

BentoML is an open-source model serving library for building performant and scalable AI applications with Python. It comes with everything you need for serving optimization, model packaging, and production deployment.

MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for LLMs. It simplifies code analysis for researchers and can be seamlessly integrated into LLM training and application processes to enhance performance in a range of code-related tasks. The sandbox environment ensures safe code execution, the code analysis module offers comprehensive analysis reports, and the information integration module combines compilation feedback and analysis results for complex code-related tasks.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

Well-Architected Infrastructure as Code (IaC) Analyzer is a project demonstrating how generative AI can evaluate infrastructure code for alignment with best practices. It features a modern web application allowing users to upload IaC documents, complete IaC projects, or architecture diagrams for assessment. The tool provides insights into infrastructure code alignment with AWS best practices, offers suggestions for improving cloud architecture designs, and can generate IaC templates from architecture diagrams. Users can analyze CloudFormation, Terraform, or AWS CDK templates, architecture diagrams in PNG or JPEG format, and complete IaC projects with supporting documents. Real-time analysis against Well-Architected best practices, integration with AWS Well-Architected Tool, and export of analysis results and recommendations are included.

mjai.app is a platform for mahjong AI competition. It contains an implementation of a mahjong game simulator for evaluating submission files. The simulator runs Docker internally, and there is a base class for developing bots that communicate via the mjai protocol. Submission files are deployed in a Docker container, and the Docker image is pushed to Docker Hub. The Mjai protocol used is customized based on Mortal's Mjai Engine implementation.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

OpenLLM is a platform that helps developers run any open-source Large Language Models (LLMs) as OpenAI-compatible API endpoints, locally and in the cloud. It supports a wide range of LLMs, provides state-of-the-art serving and inference performance, and simplifies cloud deployment via BentoML. Users can fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. The platform also supports various quantization techniques, serving fine-tuning layers, and multiple runtime implementations. OpenLLM seamlessly integrates with other tools like OpenAI Compatible Endpoints, LlamaIndex, LangChain, and Transformers Agents. It offers deployment options through Docker containers, BentoCloud, and provides a community for collaboration and contributions.

ClearML Server is a backend service infrastructure for ClearML, facilitating collaboration and experiment management. It includes a web app, RESTful API, and file server for storing images and models. Users can deploy ClearML Server using Docker, AWS EC2 AMI, or Kubernetes. The system design supports single IP or sub-domain configurations with specific open ports. ClearML-Agent Services container allows launching long-lasting jobs and various use cases like auto-scaler service, controllers, optimizer, and applications. Advanced functionality includes web login authentication and non-responsive experiments watchdog. Upgrading ClearML Server involves stopping containers, backing up data, downloading the latest docker-compose.yml file, configuring ClearML-Agent Services, and spinning up docker containers. Community support is available through ClearML FAQ, Stack Overflow, GitHub issues, and email contact.

Polis is an AI powered sentiment gathering platform that offers a more organic approach than surveys and requires less effort than focus groups. It provides a comprehensive wiki, main deployment at https://pol.is, discussions, issue tracking, and project board for users. Polis can be set up using Docker infrastructure and offers various commands for building and running containers. Users can test their instance, update the system, and deploy Polis for production. The tool also provides developer conveniences for code reloading, type checking, and database connections. Additionally, Polis supports end-to-end browser testing using Cypress and offers troubleshooting tips for common Docker and npm issues.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container. You can deploy your packaged model to your own infrastructure, or to Replicate.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

FinalRip is a distributed video processing tool based on FFmpeg and VapourSynth. It cuts the original video into multiple clips, processes each clip in parallel, and merges them into the final video. Users can deploy the system in a distributed way, configure settings via environment variables or remote config files, and develop/test scripts in the vs-playground environment. It supports Nvidia GPU, AMD GPU with ROCm support, and provides a dashboard for selecting compatible scripts to process videos.

Fast-LLM is an open-source library designed for training large language models with exceptional speed, scalability, and flexibility. Built on PyTorch and Triton, it offers optimized kernel efficiency, reduced overheads, and memory usage, making it suitable for training models of all sizes. The library supports distributed training across multiple GPUs and nodes, offers flexibility in model architectures, and is easy to use with pre-built Docker images and simple configuration. Fast-LLM is licensed under Apache 2.0, developed transparently on GitHub, and encourages contributions and collaboration from the community.

PentAGI is an innovative tool for automated security testing that leverages cutting-edge artificial intelligence technologies. It is designed for information security professionals, researchers, and enthusiasts who need a powerful and flexible solution for conducting penetration tests. The tool provides secure and isolated operations in a sandboxed Docker environment, fully autonomous AI-powered agent for penetration testing steps, a suite of 20+ professional security tools, smart memory system for storing research results, web intelligence for gathering information, integration with external search systems, team delegation system, comprehensive monitoring and reporting, modern interface, API integration, persistent storage, scalable architecture, self-hosted solution, flexible authentication, and quick deployment through Docker Compose.

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