Open Source Python Container Management Software
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Python Container Management Software
View 105 business solutionsBrowse free open source Python Container Management Software and projects below. Use the toggles on the left to filter open source Python Container Management Software by OS, license, language, programming language, and project status.
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Enterprise AI Search, Intranet, and Wiki in one platform.Cut through the noise and end information overload with Guru, an all-in-one wiki, intranet, and knowledge base that serves as your company's single source of truth.
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Integrated AI strategy and execution softwareInspire Software is an AI powered strategic execution platform. It empowers organizations to elevate performance by aligning people, teams, and strategic goals in one streamlined platform. Headquartered in Jackson, Wyoming, Inspire brings together tools for goals, performance tracking, 1:1s, feedback, learning, pulse surveys, and more. This all-in-one solution fosters leadership across every level, helping align team efforts with your organization’s growth strategy for impactful results. With Inspire, you’re not just managing performance—you’re driving growth through leadership. Discover more at inspiresoftware.com.
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Docker-OSX
Run macOS VM in a Docker! Run near native OSX-KVM in Docker
Run Mac OS X in Docker with near-native performance! X11 Forwarding. iMessage security research! iPhone USB working! macOS in a Docker container. -
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Emissary-ingress
open source Kubernetes-native API gateway for microservices
Emissary-Ingress is the most popular API Gateway Kubernetes-native - open-source, that delivers scalability, flexibility, and simplicity for some of the world's largest Kubernetes installations. Emissary-Ingress is an open source CNCF incubating project, and it uses the ubiquitous Envoy Proxy at its core. Once your organization hits a certain scale, a reverse proxy or load balancer isn’t enough for traffic management. If you’re using Kubernetes, you’re probably already at that scale and need support for advanced ingress and API management solutions like blue-green deployment. -
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KServe
Standardized Serverless ML Inference Platform on Kubernetes
KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being used across various organizations. -
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Kapitan
Generic templated configuration management for Kubernetes
Generic templated configuration management for Kubernetes, Terraform, and other things. Kapitan aims to be your one-stop configuration management solution to help you manage the ever-growing complexity of your configurations by enabling Platform Engineering and GitOps workflows. It streamlines complex deployments across heterogeneous environments while providing a secure and adaptable framework for managing infrastructure configurations. Kapitan's inventory-driven model, powerful templating capabilities, and native secret management tools offer granular control, fostering consistency, reducing errors, and safeguarding sensitive data. Empower your team to make changes to your infrastructure whilst maintaining full control, with a GitOps approach and full transparency. -
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Kubernetes shell
Kubernetes shell: An integrated shell for working with the Kubernetes
Kube-shell: An integrated shell for working with the Kubernetes CLI. Under the hood kube-shell still calls kubectl. Kube-shell aims to provide ease-of-use of kubectl and increase productivity. You can use up-arrow and down-arrow to walk through the history of commands executed. Also, up-arrow partial string matching is possible. For e.g. enter 'kubectl get' and use up-arrow and down-arrow to browse through all kubectl get commands. You could also use CTRL+r to search from the history of commands. Details of the current context from kubeconfig is always readily displayed on the bottom toolbar. By pressing F4 button you can switch between the clusters and using F5 can switch between namespaces. -
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Nebula docs
Documentation repo of nebula orchestration system
Nebula is a open source distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking affect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe, some example use cases are appliances\virtual appliances located at clients data centers, edge computing, and POS systems. Ever wandered how your going to push an update to that smart fridge your company is working on as it's thousands of devices around the globe? wish you could have the assurance that your service will always use the latest code\envvars\etc in all of it's edge locations? want the ability to stop\start a globally distributed service with a single command? Nebula was designed from the ground up to answer all of this needs. -
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Nebula reporter
The optional reporter container which reads nebula reports from Kafka
Nebula is an open source-distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking effect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe. Ever wandered how your going to push an update to that smart fridge your company is working on as it's thousands of devices around the globe? wish you could have the assurance that your service will always use the latest code\envvars\etc in all of its edge locations? want the ability to stop\start a globally distributed service with a single command? Nebula was designed from the ground up to answer all of these needs and much more, refer to the documentation if your interested in seeing how to use it. -
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Nebula worker
The worker node manager container which manages nebula nodes
Nebula is a open source distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking affect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe, some example use cases are IoT devices, appliances\virtual appliances located at clients data centers, and edge computing. Nebula imposes no limits on the scale of the cluster, each component in it is designed to scale out to allow millions of workers to be managed by it. Designed to connect to devices that are spread around the globe Nebula is tolerant of network connection issues and will resync the device when it reconnects. With a single API call you can deploy a new container version to managed devices around the globe in minutes. -
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Nebula-Python-SDK
A python SDK for managing Nebula container orchestrator
A python SDK for managing Nebula container orchestrator. First, get NebulaPythonSDK onto your machine, now use it in your code. Nebula container orchestrator aims to help devs and ops treat IoT devices just like distributed Dockerized apps. It aim is to act as Docker orchestrator for IoT devices as well as for distributed services such as CDN or edge computing that can span thousands (possibly even millions) of devices worldwide and it does it all while being open-source and completely free. Nebula imposes no limits on the scale of the cluster, each component in it is designed to scale out to allow millions of workers to be managed by it. Designed to connect to devices that are spread around the globe Nebula is tolerant of network connection issues and will resync the device when it reconnects. With a single API call you can deploy a new container version to managed devices around the globe in minutes. -
High-performance Open Source API GatewayKrakenD is a high-performance API Gateway optimized for resource efficiency, capable of managing 70,000 requests per second on a single instance. The stateless architecture allows for straightforward, linear scalability, eliminating the need for complex coordination or database maintenance.
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OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
Run ML/AI or any type of workload with optimal performance and infrastructure cost. OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. The solution enables ML/AI engineers to run automated experiments based on datasets and hyperparameter conditions within the defined infrastructure budget. Certified FinOps solution with the best cloud cost optimization engine, providing rightsizing recommendations, Reserved Instances/Savings Plans, and dozens of other optimization scenarios. With OptScale, users get complete cloud resource usage transparency, anomaly detection, and extensive functionality to avoid budget overruns. -
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Opta
The next generation of Infrastructure-as-Code
Opta is an infrastructure-as-code framework. Rather than working with a low-level cloud configuration, Opta enables you to work with high-level constructs. Opta high-level constructs produce Terraform configuration files. This helps you avoid lock-in to Opta. You can write custom Terraform code or even take the Opta-generated Terraform and go your own way. Opta is a new kind of Infrastructure-as-Code (IaC) framework that lets engineers work with high-level constructs instead of getting lost in low-level cloud configuration. Opta has a vast library of modules (like EKS, RDS, DynamoDB, GKE, Cloud SQL, and even third-party services like Datadog) that engineers can compose together to build their ideal infrastructure stack. It's built on top of Terraform, and designed so engineers aren’t locked in – anyone can write custom Terraform or even take the Opta-generated Terraform and work independently. -
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Ouroboros
Automatically update running docker containers
Ouroboros will monitor (all or specified) running docker containers and update them to the (latest or tagged) available image in the remote registry. The updated container uses the same tag and parameters that were used when the container was first created such as volume/bind mounts, docker network connections, environment variables, restart policies, entrypoints, commands, etc. Push your image to your registry and simply wait your defined interval for ouroboros to find the new image and redeploy your container autonomously. Notify you via many platforms courtesy of Apprise. Serve metrics for trend monitoring (Currently: Prometheus/Influxdb) Per-command and scenario examples can be found in the wiki. -
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PaaSTA
An open, distributed platform as a service
PaaSTA is a highly-available, distributed system for building, deploying, and running services using containers and Kubernetes. PaaSTA has been running production services at Yelp since 2016. It was originally designed to run on top of Apache Mesos but has subsequently been updated to use Kubernetes. Over time the features and functionality that PaaSTA provides have increased but the principal design remains the same. PaaSTA aims to take a declarative description of the services that teams need to run and then ensures that those services are deployed safely, efficiently, and in a manner that is easy for the teams to maintain. Rather than managing Kubernetes YAML files, PaaSTA provides a simplified schema to describe your service and in addition to configuring Kubernetes it can also configure other infrastructure tools to provide monitoring, logging, cost management etc. -
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Robusta
Kubernetes observability and automation
Keep your Kubernetes microservices up and running. Connect your existing Prometheus, gain 360° observability. Robusta is both an automation engine for Kubernetes and a multi-cluster observability platform. Robusta is commonly used alongside Prometheus, but other tools are supported too. By listening to all the events in your cluster, Robusta can tell you why alerts fired, what happened at the same time, and what you can do about it. Robusta can either improve your existing alerts or be used to define new alerts triggered by APIServer changes. -
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Robusta KRR
Prometheus-based Kubernetes Resource Recommendations
Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for optimizing resource allocation in Kubernetes clusters. It gathers pod usage data from Prometheus and recommends requests and limits for CPU and memory. This reduces costs and improves performance. -
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Status
Minimal docker container status panel
A minimal docker container management panel written in Python / Flask. Before installing this project, please, make sure you have installed docker and docker-compose. -
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Testcontainers Python
Testcontainers is a Python library that providing a friendly API
Testcontainers is a Python library that provides a friendly API to run a Docker container. It is designed to create a runtime environment to use during your automatic tests. -
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cntnr
CLI wrapper for basic container (Docker/libpod) management tasks
CLI wrapper for basic container (Docker/libpod) management tasks. This is a Python wrapper script to simplify specific tasks in a container environment. It is designed to work with your container runtime of choice (currently podman and docker). A CLI menu makes these selections possible. This project was created for a lab assignment in NSSA-244 Virtualization at the Rochester Institute of Technology.
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