Open-source alternatives to Nvidia Docker

Collect logs for docker containers

Docker container for Firefox

Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t

Portainer is a unified infrastructure management platform that provides a centralized control plane for deploying, monitoring, and managing containerized applications. It functions as an orchestration-abstraction layer, translating user actions into platform-specific API calls to maintain consistency across diverse container runtimes and cluster technologies. By organizing users, teams, and resources into a single interface, it enables granular role-based access control and lifecycle management for containerized services and stacks. The platform distinguishes itself through its support for di

Traefik is a cloud-native load balancer and dynamic reverse proxy designed for microservices traffic routing. It automatically discovers services and generates network routes by listening to infrastructure changes in orchestrators and service registries. The project distinguishes itself through auto-configuring service routing, which eliminates manual configuration by updating routing rules in real time as infrastructure scales. It also provides automated SSL certificate management, utilizing ACME-based automation to request and renew certificates from remote authorities. Additional capabili

A simple yet powerful tool to turn traditional container/OS images into unprivileged sandboxes.

Triton Inference Server is a high-performance AI model inference server and multi-framework model runtime designed for deploying machine learning models across cloud, data center, and embedded edge infrastructure. It serves as an execution engine that allows for the concurrent running of models from various frameworks to optimize hardware utilization. The project features a dynamic batching inference engine that groups individual requests into larger batches to increase total processing throughput. It also provides a model ensemble pipeline, which enables the chaining of multiple models toget

LocalStack is an infrastructure development environment that provides a local simulation of cloud services. By leveraging container-orchestrated service lifecycles, it allows developers to build, test, and debug cloud-native applications on their local machines without requiring remote connectivity or incurring cloud provider costs. The platform distinguishes itself through sophisticated traffic redirection and request routing, which intercept cloud service calls at the network layer and redirect them to local handlers. This enables seamless integration with existing development workflows, al

Polyaxon is a Kubernetes-native machine learning orchestration platform and MLOps pipeline orchestrator. It serves as a control plane for managing distributed deep learning workloads, automated machine learning pipelines, and experiment tracking. The platform distinguishes itself through specialized services for distributed training management, including MPI-based coordination for PyTorch and TensorFlow. It provides an automated hyperparameter optimization service utilizing Bayesian, random, and grid search algorithms, alongside managed interactive AI workspaces for launching Jupyter notebook

cuDF is a GPU-accelerated dataframe library and data processing engine designed for manipulating and analyzing large tabular datasets. It provides a high-level API for executing filtering, joining, and aggregating operations directly on GPU hardware. The project integrates the Apache Arrow memory format to enable zero-copy data transfers and includes a just-in-time compiler for executing custom user-defined functions on the GPU. The library features specialized acceleration for existing workflows by redirecting standard Pandas dataframe calls and Polars query plans to a GPU backend. It also p

Slurm is a cluster workload manager and job scheduler designed for high-performance computing environments. It functions as a distributed compute orchestrator that queues and executes large-scale computational tasks across multiple compute nodes in a cluster. The system acts as a resource arbitrator, distributing hardware nodes and processors among concurrent users to prevent resource conflicts and maximize efficiency. It coordinates the simultaneous launch of multiple processes across different physical servers to execute parallel jobs and scientific workloads. The platform covers broad cap

JupyterHub is a multi-user platform for hosting and managing isolated Jupyter notebook server instances on a single system or cluster. It serves as a notebook server orchestrator that spawns and monitors individual computing environments on demand. The system uses a plugin-based spawner interface to launch these environments across diverse infrastructure, including local processes, containers, or cloud clusters. It integrates with external identity providers and supports pluggable authentication to verify user identities and secure access. The framework includes centralized administration to

Official MAAS repository. Bugs are tracked on Launchpad: https://bugs.launchpad.net/maas

docker-py is a Python library and SDK that serves as a programmatic client for the Docker Engine API. It provides a programmatic interface for controlling containers, images, and clusters, allowing for the automation of containerized applications and engine resources. The library enables remote Docker administration through various transport methods, including Unix domain sockets, TCP ports, and encrypted SSH tunnels. It handles real-time data links by using WebSockets to stream input and output from active containers. The project covers container lifecycle control, image management for buil

Kubeflow is a Kubernetes machine learning platform and containerized toolkit designed to orchestrate the entire machine learning lifecycle. It functions as an MLOps workflow orchestrator and infrastructure layer for building, training, and deploying models within containerized environments. The project provides specialized infrastructure for scaling compute resources and managing GPU workloads for large-scale distributed training. It automates the transition of models from experimental development to production through workflow orchestration and model deployment services. The platform covers

noVNC is an HTML5 remote desktop viewer and VNC web client that allows users to access and control remote desktops, virtual machines, and containers through a web browser. It functions as a remote desktop management tool that integrates the VNC protocol to enable remote server administration without the need for a dedicated client application. The project includes a VNC WebSocket proxy that translates binary VNC network traffic into WebSockets to bypass browser restrictions on direct TCP connections. This proxy service supports bridging connections to multiple remote servers by listening on v

pyinfra is an agentless infrastructure automation framework that turns declarative Python code into idempotent shell commands to manage servers, containers, and local machines over SSH without requiring any pre-installed software on target hosts. It operates by comparing the desired state of a system against its current state, using a dry-run simulation mode to preview changes and a fact-based conditional execution engine to gather host attributes at runtime and control which operations run. The tool compiles Python operations into optimized shell commands and executes them in parallel across

Graylog2-server is an open-source centralized log management system and aggregator. It functions as a log analysis platform designed to collect, index, and analyze log data from multiple sources within a centralized searchable index. The system provides capabilities for enterprise log aggregation and infrastructure monitoring. It enables the gathering of logs from various servers and applications to facilitate log data analysis and root cause troubleshooting across a network. The platform utilizes a distributed indexing pipeline and message-queue based ingestion to handle log streams. It inc

This project provides a containerized environment for running Steam games and applications without a physical monitor. It consists of a Docker image designed for headless game server hosting, utilizing a virtual display server to enable remote game streaming of video and audio to a web browser. The system integrates NVIDIA GPU virtualization to provide hardware acceleration for high-performance 3D graphics rendering within the container. A remote desktop gateway allows users to access and manage the virtualized desktop environment and game client remotely. The software includes capabilities

Serve is a multimodal AI orchestrator and inference server designed for deploying and scaling machine learning models as cloud-native services. It functions as a containerized workflow engine and distributed service mesh that routes multimodal data through connected execution units. The framework provides specialized capabilities for large language models, including a token streaming gateway that delivers generated text incrementally to reduce perceived latency. It distinguishes itself by enabling the chaining of executors into complex data processing pipelines and the orchestration of these

Paddle is a deep learning framework designed for building, training, and deploying large-scale machine learning models. It incorporates a distributed training engine for optimizing performance across multiple chips and a model inference engine for transforming trained models into production-ready formats for cross-platform execution. The platform features a heterogeneous hardware abstraction and a standardized software stack that allows models to run across diverse hardware architectures through a common interface. It also includes a scientific computing library capable of solving complex dif

This repository is a collection of Jupyter notebooks providing reference implementations and templates for building, training, and deploying machine learning models using Amazon SageMaker. It serves as an example library for implementing model architectures and automating the machine learning lifecycle. The library provides practical patterns for machine learning training, data engineering, and model deployment. It includes implementation guides for MLOps, including workflows for model monitoring, lineage tracking, and hyperparameter tuning. The examples cover a broad range of capabilities i

This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

This project is a collection of PyTorch deep learning courseware consisting of practical projects and programming exercises. It focuses on implementing neural network architectures and model training to solve complex data problems. The repository includes a computer vision project suite for building image classifiers, autoencoders, and style transfer applications. It features a generative adversarial network lab for creating synthetic images and specific implementations for transfer learning to adapt pre-trained weights to new tasks. The codebase covers sequential data analysis for natural l