Open Source Mac Clustering Software
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Clustering Software for Mac
View 736 business solutionsBrowse free open source Clustering software and projects for Mac below. Use the toggles on the left to filter open source Clustering software by OS, license, language, programming language, and project status.
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Kubernetes Dashboard
General-purpose web UI for Kubernetes clusters
Kubernetes Dashboard is a general purpose, web-based UI for Kubernetes clusters. It allows users to manage applications running in the cluster and troubleshoot them, as well as manage the cluster itself. To access Dashboard from your local workstation you must create a secure channel to your Kubernetes cluster. Kubeconfig Authentication method does not support external identity providers or certificate-based authentication. Metrics-Server has to be running in the cluster for the metrics and graphs to be available. Make sure that you know what you are doing before proceeding. Granting admin privileges to Dashboard's Service Account might be a security risk. In most cases after provisioning cluster using kops, kubeadm or any other popular tool, the ClusterRole cluster-admin already exists in the cluster. We can use it and create only ClusterRoleBinding for our ServiceAccount. If it does not exist then you need to create this role first and grant required privileges manually. -
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minikube
Sets up a local Kubernetes cluster to run it
minikube quickly sets up a local Kubernetes cluster on macOS, Linux, and Windows. We proudly focus on helping application developers and new Kubernetes users. It supports the latest Kubernetes release (+6 previous minor versions). It iscross-platform (Linux, macOS, Windows), and allows the deployment of its functions as a VM, a container, or on bare-metal. Provides multiple container runtimes (CRI-O, containerd, docker), Docker API endpoint for blazing fast image pushes, and advanced features such as LoadBalancer, filesystem mounts, and FeatureGates. Contains addons for easily installed Kubernetes applications. -
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Faiss
Library for efficient similarity search and clustering dense vectors
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It also supports cosine similarity, since this is a dot product on normalized vectors. -
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kube-state-metrics
Add-on agent to generate and expose cluster-level metrics
kube-state-metrics (KSM) is a simple service that listens to the Kubernetes API server and generates metrics about the state of the objects. (See examples in the Metrics section below.) It is not focused on the health of the individual Kubernetes components, but rather on the health of the various objects inside, such as deployments, nodes and pods. kube-state-metrics is about generating metrics from Kubernetes API objects without modification. This ensures that features provided by kube-state-metrics have the same grade of stability as the Kubernetes API objects themselves. In turn, this means that kube-state-metrics in certain situations may not show the exact same values as kubectl, as kubectl applies certain heuristics to display comprehensible messages. kube-state-metrics exposes raw data unmodified from the Kubernetes API, this way users have all the data they require and perform heuristics as they see fit. -
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vcluster
Create fully functional virtual Kubernetes clusters
Create fully functional virtual Kubernetes clusters with vcluster. Each vcluster runs inside a namespace of the underlying k8s cluster. It's cheaper than creating separate full-blown clusters and it offers better multi-tenancy and isolation than regular namespaces. vcluster itself is a certified Kubernetes distribution and is 100% Kubernetes API conform. Everything that works in a regular Kubernetes cluster works in cluster. Pods are scheduled in the underlying host cluster, so they get no performance hit at all while running. Split up large multi-tenant clusters into smaller vcluster to reduce complexity and increase scalability. Since most vcluster api requests and objects will not reach the host cluster at all, vcluster can greatly decrease pressure on the underlying Kubernetes cluster. Create via vcluster CLI, helm, kubectl, Argo or any of your favorite tools (it is basically just a StatefulSet). -
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GGI stands for "General Graphics Interface", and it is a project that aims to develop a reliable, stable and fast graphics system that works everywhere. We want to allow any program using GGI to run on any platform requiring at most a recompile.
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AWS App Mesh Controller For K8s
A controller to help manage App Mesh resources for Kubernetes cluster
AWS App Mesh Controller For K8s is a controller to help manage App Mesh resources for a Kubernetes cluster and injecting sidecars to Kubernetes Pods. The controller watches custom resources for changes and reflects those changes into the App Mesh API. The controller maintains the custom resources (CRDs): meshes, virtual nodes, virtual routers, virtual services, virtual gateways, and gateway routes. The custom resources map to App Mesh API objects. AppMesh controller supports monitoring the control plane with Prometheus and Grafana. AppMesh controller supports integration with multiple tracing solutions for data plane. AppMesh limits pod count per virtualNode. By default the limit is 10. When working against the App Mesh Preview environment, the preview SDK models are downloaded and used to generate the AWS SDK for Go dynamically. -
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JPPF
The open source grid computing solution
JPPF makes it easy to parallelize computationally intensive tasks and execute them on a Grid. -
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RPyC, or Remote Python Call, is a transparent and symmetrical python library for remote procedure calls, clustering and distributed-computing.
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Cybermen - SPC
Services platform in cluster for Java.
Cybermen - SPC (Services Platform in Cluster) es una plataforma de servicios y clúster de procesamiento distribuido, orientado principalmente al desarrollo de aplicaciones de supervisión, control y adquisición de datos (SCADA) y control distribuido (DCS). Cybermen - SPC de por sí, no hace a las aplicaciones SCADA o DCS, pero constituye una base para el desarrollo de este tipo de soluciones, basándose para las mismas en una arquitectura de servicios colaborativos (SOA) y operando en un clúster de procesamiento. Por estar vasado en Java SE, Cybermen - SPC no solo es cross-platform (opera en cualquier sistema operativo que soporte java) sino que al mantenerse dentro de Standard Edition y operar con un numero acotado de dependencias externas, funciona con bajos requerimientos de hardware. Tanto, que sus nodos de procesamiento son capases de operar en hardware ARM v7 con soporte para Java SE Embedded -
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HA-JDBC
High-Availability JDBC
A JDBC driver proxy that adds light-weight, transparent, fault tolerant clustering capability to any underlying JDBC driver. -
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Momoko is a framework for developing distributed, multi-user, network-aware applications. It includes facilities for storing objects in a tree structure, a shell for manipulating the object database, orthogonal persistence, scripting support, and a trans
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Tools, information, and practices for creating and operating dependable, complex, distributed, software-intensive systems. * Sense - Measure - Analyze - Respond - Optimize * Keywords: Performance Co-Pilot - PCP - PCPMon - Rjobd
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Python Integrated Parallel Programming EnviRonment (PIPPER), Python pre-parser that is designed to manage a pipeline, written in Python. It enables automated parallelization of loops. Think of it like OpenMP for Python, but it works in a computer cluster
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Atomatrix is a multithreaded execution system of atomic matrices with a dynamic compiler and a network eviron. It is the next generation object-orientated environment. XML, MUD-OO CLI, GnucDNA support, windows and many subprojects are built-in.
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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. AWS ParallelCluster facilitates both quick start proof of concepts (POCs) and production deployments. You can build higher-level workflows, such as a Genomics portal that automates the entire DNA sequencing workflow, on top of AWS ParallelCluster. -
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Anubis Gate is an abstract Content Management and Distribution tool designed for secure and fault-tolerant enterprise server-farm deployments, with multiple test stages. It is content agnostic, meaning it can distribute any type of content.
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Channel is a C++ framework for distributed message passing and event dispatching, configurable with its components (msg ids,routing algorithms...) as template parameters. As a namespace shared by peer threads, channel supports scope control and filtering
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Cluster Orchestrator
orchestrator / cluster deployment tool
The Orchestrator server is responsible for distribution and launch of simple apply/rollback shell scripts in a particular order across multiple servers . If for any reason deployment is failing at a particular stage the process will stop and you can rollback any changes done until then. In case you want to deploy your code or just call your preferred DSC client to apply the configuration locally the Orchestrator can be a handy tool. The operation can be started manually or scheduled, depending on your needs. In terms of audit and trace capabilities, the system is collecting all console output of your shell scripts from all servers and saves the logs centrally for review. -
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Alternative implementation of Spring framework remoting subsystem that supports different schemes of remote communication between client and server (1-1, 1-many, dynamic services discovering).
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DBD2
A multi-threaded, multi-database tcp-based database insertion app.
DBD2 inserts database records from TCP input. It is a companion project to Syslgod2 and an application in its own right. It ships with a backgrounding client for generic use. The client sends whatever information the user puts on the command-line to the back-end daemon via TCP. Upon successful transmission, the client exits. On failure, it backs off and retries until its life-time timer expires. The back-end daemon accepts data from Syslogd2 or its own client and inserts that data (and envelope information) into one or more databases using MySql client library or dbi drivers (also on SourceForge). The client needs no configuration (all input is via command-line). It is intended for hosts to make database entries without needing database client or interface code. Syslogd2 integration allows DBD2 to act as Syslogd2's database back-end for MySql and other databases supported by the dbi drivers. Original purpose was to track Linux cluster job status but other uses exist. -
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JTOE (Efficient Transport of Java Objects - initials reordered) is a library for transporting Java objects from JVM to JVM through the network. It focuses on simplicity and efficiency enabling really fast implementations of transport layers.
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FMACJ is a parallelization framework for the .NET CLR that gives your applications multicore/cluster support by saying what should be parallelized via attributes. You do not have to think about threads and networks at all. FMACJ also runs on Mono.
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java-based complete Agent (Artificial Intelligence) Framework with all the features you might expect in an Agent Framework: Mobility, Autonomy, Distributed communication, Agents can be written in Java, Jython, or Beanshell.
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ForeIndex
Distributed Index with Apache Hadoop, Apache Lucene and Apache Tika
This is a distributed index framework using Apache Hadoop, Apache Lucene and Apache Tika, to index large volume of data.
