Search Results for "docker-ce"
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Showing 122 open source projects for "docker-ce"
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Kaggle Python Docker
Kaggle Python docker image
Kaggle Python Docker is Kaggle’s official Docker image repository for the Python environment used by Kaggle Notebooks. It contains the Dockerfiles and build configuration for both CPU-only and GPU-enabled notebook images. The project helps users understand, reproduce, and test against the same Python environment that powers Kaggle’s cloud notebooks. -
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gemini-web2api
Convert Google Gemini web into OpenAI-compatible API
...It supports model aliases for Flash, Thinking, Pro-style routing, Auto, and Lite variants. The tool also includes optional API keys, function calling, SSE streaming, web search access, Docker deployment, and client examples for OpenAI SDK-style usage. It is useful for developers who want local experimentation with OpenAI-compatible tooling while routing requests through Gemini web behavior. -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train 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. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. ... -
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Cog
Package and deploy machine learning models using Docker containers
Cog is an open source tool designed to package machine learning models into standardized, production-ready containers. It simplifies the process of deploying models by automatically generating Docker images based on a simple configuration file, eliminating the need to manually write complex Dockerfiles. Developers can define the runtime environment, dependencies, and Python versions required for their models, allowing Cog to build a consistent container environment that follows best practices. Cog also resolves compatibility issues between frameworks and GPU libraries by automatically selecting compatible combinations of CUDA, cuDNN, and machine learning frameworks such as PyTorch or TensorFlow. ... -
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EPUB to Audiobook Converter
EPUB to audiobook converter, optimized for Audiobookshelf
...The tool offers advanced options such as controlling chapter ranges, handling paragraph detection via newline modes, removing endnote markers, and using regex-based search-and-replace files to tweak pronunciations. It can be run directly with Python or via Docker. -
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Harbor LLM
Run a full local LLM stack with one command using Docker
...Harbor supports multiple inference engines, including llama.cpp and vLLM, and connects them seamlessly to user interfaces. It also includes tools for web retrieval, image generation, voice interaction, and workflow automation. Built on Docker, Harbor allows services to run in isolated containers while communicating over a local network. It is intended for local development and experimentation rather than production deployment, giving developers a flexible way to explore AI systems, test configurations, and manage complex LLM stacks without manual wiring or setup overhead. -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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AutoClip
AI-powered video clipping and highlight generation
...It uses a modern web application stack with a front end (React + TypeScript) for user interaction and a back end that handles downloading, processing, clipping, and queue management, allowing real-time progress feedback and easy deployment, e.g. via Docker. -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
FastKoko is a self-hosted text-to-speech server built around the Kokoro-82M model and exposed through a FastAPI backend. It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. It supports multiple languages and voicepacks and allows phoneme based generation for more accurate pronunciation and prosody. ... -
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AlphaFold 3
AlphaFold 3 inference pipeline
...The system is designed for scientific research applications in structural biology, biochemistry, and bioinformatics, enabling accurate modeling of proteins, ligands, and covalent modifications. Users can perform local predictions via Docker containers, integrating AlphaFold 3’s inference process with provided JSON input configurations. The software includes flexible options for running both data preprocessing and GPU-accelerated inference, allowing users to adapt to available computational resources. -
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OpenMed
Open source healthcare AI
...The project includes a curated registry of more than a dozen medical NER models focused on areas such as diseases, drugs, anatomy, genes, and protected health information, and it is built to support both research and deployment scenarios. OpenMed can be used in three main ways: as a simple Python API for scripts and notebooks, as a Docker-friendly FastAPI service for backend integration, and as a batch-processing system for multi-document workflows. -
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ChatTTS webUI & API
A simple native web interface that uses ChatTTS to synthesize text
ChatTTS-ui is a local web interface and API wrapper around the ChatTTS speech synthesis system, designed to make advanced TTS models easy to use from a browser. It runs a small backend server (Python + Torch + ffmpeg) and exposes a simple webpage where you can type text, adjust parameters, and generate audio. The project supports Chinese, English, and mixed text with digits and control symbols, making it suitable for bilingual content and numerically heavy text like announcements or prompts.... -
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AutoAgent AI
Autonomous harness engineering
...It operates through a loop of testing, analyzing failures, and refining the agent’s configuration to maximize a scoring metric. The framework uses a single-file agent harness combined with structured tasks and evaluation suites to guide optimization. It runs inside Docker for safe execution and reproducibility. This approach shifts agent development from manual design to automated optimization. The system is particularly useful for building domain-specific agents that need continuous performance improvement. -
15
Local-NotebookLM
Googles NotebookLM but local
...Users can generate podcasts, summaries, interviews, lectures, debates, tutorials, news reports, executive briefs, and other formats. It supports multiple LLM providers, including local and hosted options, and can be used through a command-line workflow, API server, Docker setup, or web UI. Overall, it is useful for students, researchers, and knowledge workers who want private, customizable audio summaries from documents. -
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AutoCVE
Agent-driven automated CVE discovery platform for source code auditing
...Vulnerabilities are deduplicated and stored in a structured management system, while configurable skills extend individual agents. The platform uses a React frontend, FastAPI backend, PostgreSQL database, and Docker-based deployment. -
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GAIA
Proactive personal AI assistant & companion for daily productivity
...GAIA is available through web, desktop, and mobile interfaces and can also communicate through messaging platforms. The full-stack Nx monorepo includes Next.js, Electron, React Native, FastAPI, LangGraph, bots, voice processing, and Docker-based self-hosting. -
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NVIDIA AI Blueprint
Suite of reference architectures for building GPU-accelerated vision
...It is designed for technical users who need deployable reference architectures for smart spaces, warehouse automation, SOP validation, monitoring, and operational video analytics. The repository includes Python agent code, Docker Compose deployment configurations, skills, scripts, and a Next.js-based UI. -
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Clawbolt
The AI Assistant that actually does things for the trades
...The project supports integrations with QuickBooks Online, Google Calendar, Dropbox, and Google Drive, enabling automated business workflows tied directly to conversations. Its architecture is built with FastAPI, PostgreSQL, Docker, and modular LLM provider support, allowing both cloud and self-hosted deployments. -
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OpenSandbox
OpenSandbox is a general-purpose sandbox platform for AI applications
...It supports multiple programming languages through SDKs, allowing developers to integrate sandbox capabilities into their systems without building custom isolation layers. The platform is built to work with container technologies such as Docker and Kubernetes, enabling scalable and production ready deployments. OpenSandbox is particularly useful for AI agents, code execution services, and any scenario where untrusted code must be executed safely. Its architecture emphasizes flexibility, security boundaries, and operational consistency across environments. Overall, the project aims to standardize sandbox execution for modern AI and cloud native workflows. -
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GPT4Free
The official gpt4free repository
gpt4free is an open-source project offering free, unrestricted access to GPT‑4–style language models without requiring an API key. The repository includes scripts and server implementations designed to replicate OpenAI’s GPT‑4 API behavior by leveraging publicly available or self-hosted models. It’s licensed under GPL‑v3. -
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Ollama RAG Chatbot
Chat with multiple PDFs locally
...Model support is flexible, with compatibility for both Hugging Face models and Ollama-based models, and the interface is delivered through Gradio for a lightweight user experience. The main value of the project is its ability to process multiple PDF inputs and turn them into a question-answering workflow centered on document retrieval. With Docker support, script-based setup, optional ngrok exposure, and a clear local run path, it serves as a compact starter project for people who want a hands-on, self-hosted PDF chat system. -
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BentoML
Unified Model Serving Framework
...Orchestrate distributed inference graph with multiple models via Yatai on Kubernetes. Easily configure CUDA dependencies for running inference with GPU. Automatically generate docker images for production deployment. -
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APKiD
Android Application Identifier for Packers, Protectors and Obfuscators
APKiD gives you information about how an APK was made. It identifies many compilers, packers, obfuscators, and other weird stuff. It's PEiD for Android. -
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Windows Copilot API
Reverse engineered Windows Copilot into an OpenAI-compatible API
...Because compatible applications can target the local endpoint, existing OpenAI SDK workflows require few changes. The project runs on Windows, macOS, and Linux and also includes Docker configuration, tests, examples, and command-line tools. It relies on automation of the signed-in Copilot website and is not affiliated with Microsoft.
