Open Source Python Clustering Software for Mac

Ganglia is a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids. It is based on a hierarchical design targeted at federations of clusters. Supports clusters up to 2000 nodes in size.

UPDATE: Chromium is no longer updated or maintained. The project is frozen. Chromium is a flexible framework for scalable real-time rendering on clusters of workstations, derived from the Stanford WireGL project code base.

Genv is an open-source environment and cluster management system for GPUs. Genv lets you easily control, configure, monitor and enforce the GPU resources that you are using in a GPU machine or cluster. It is intended to ease up the process of GPU allocation for data scientists without code changes.

Low-ops, minimal production Kubernetes, for devs, cloud, clusters, workstations, Edge and IoT. MicroK8s automatically chooses the best nodes for the Kubernetes datastore. When you lose a cluster database node, another node is promoted. No admin needed for your bulletproof edge. MicroK8s is small, with sensible defaults that ‘just work’. A quick install, easy upgrades and great security make it perfect for micro clouds and edge computing. As the publishers of MicroK8s, we deliver the world’s most efficient multi-cloud, multi-arch Kubernetes. Under the cell tower. On the racecar. On satellites or everyday appliances, MicroK8s delivers the full Kubernetes experience on IoT and micro clouds. Fully containerized deployment with compressed over-the-air updates for ultra-reliable operations. MicroK8s will apply security updates automatically by default, defer them if you want. Upgrade to a newer version of Kubernetes with a single command. It’s really that easy.

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.

dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. dispy supports public / private / hybrid cloud computing, fog / edge computing.

RPyC, or Remote Python Call, is a transparent and symmetrical python library for remote procedure calls, clustering and distributed-computing.

PBS Cluster Viz is a project to display information useful to admins and users about a computing cluster managed by a PBS-compatible resource manager. Information includes load and job distribution. Interactive as well as static output is available.

Infinispan is an open source, Java based data grid platform. ***IMPORTANT*** Starting with Infinispan 5.0.0.FINAL, Infinispan releases are no longer hosted in Sourceforge. They can now be located in www.jboss.org/infinispan/downloads

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.

A Console-based BitTorrent Client with built-in scheduler for handling multiple sessions. It is designed to manage sessions in queue easily without heavy-weight GUI. External module can search for new torrents in trackers and submit it automatically.

Bluesfield: An experimental distributed computing platform for the support of grassroots movements.

CIMHA is aimed to create integrated suite to provision and manage HA clusters in virtual environment. It analyzes kernel/hardware health of cluster nodes by using VESPER(linux kernel prober) and migrates the Resources based on probe signals.

DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. This alternating “cluster & train” scheme helps the model gradually discover meaningful structure without labels. DeepCluster was one of the early successes in unsupervised visual feature learning, demonstrating that clustering-based reformulation can rival supervised baselines for many downstream tasks. The repository includes code for feature extraction, clustering, training loops, and evaluation benchmarks like linear probes. Because of its simplicity and modular design, DeepCluster has inspired many later methods.

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.

Freenomalism is a free, community based fork of the Enomalism web-based virtual infrastructure/cloud computing platform. The dashboard can help with issues including deployment planning, load balancing, migration, configuration management, and capacity.

Job Monarch is an addon to the Ganglia Monitoring System that provides batch job monitoring and archiving plus a graphical overview of clusters and assorted batch systems. Fully supported batch system: Torque, PBS and SLURM. Experimental: LSF, SGE

To provide quick deployment tools for Kubernetes cluster and cloud-native applications. This session has been tested on Rocky 8.5, Debian 11, and Ubuntu 20.04+ which are supported by python3.

A tool for scanning Kubernetes cluster for risky permissions in Kubernetes's Role-based access control (RBAC) authorization model. KubiScan helps cluster administrators identify permissions that attackers could potentially exploit to compromise the clusters. This can be especially helpful on large environments where there are lots of permissions that can be challenging to track. KubiScan gathers information about risky roles\clusterroles, rolebindings\clusterrolebindings, users and pods, automating traditional manual processes and giving administrators the visibility they need to reduce risk.

Python port of Platform LSF API using swig. Access is provided directly to the API call in addition to server Object classes.

MPY is an MPI implementation for Python using MPICH (or any other MPI implementation). MPY also provides helper functions for ease of programming, and a simple plug-in interface.

Maui Scheduler is an advanced reservation HPC parallel batch scheduler for use with Linux and BSD clusters. Maui provides a complete scientific scheduling solution, supporting running custom parallel and MPI jobs over Myrinet and ethernet.

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

NetWorkSpaces, developed by Scientific Computing Associates Inc., provides a framework to coordinate programs written in Python, making it easier to write distributed applications.

The aim of the OpenGrid project is to enable personal high-performance grid computations. More specific, issues of configuration, deployment, controlling and monitoring of embarrassingly parallel computations are addressed.