Open Source Python Clustering Software - Page 2

This project allow to test network availability on multiple nodes. It is built as a plugin architecture in order to use it on multiples networks. Test is based on three steps : module status, local visibility of network, and interconnection between nodes

This is a software tool that automates the process of benchmarking high performance computer clusters by auto analyzing the hardware of the cluster and configuring and compiling specialized benchmarks (HPC Challenge, IOzone, Netperf)

Condor GUI is a tool for managing and working with one or more HTCondor pools. It was developed at the Otto von Guericke University Magdeburg, Germany, to support the daily work using HTCondor. Version History: Version 0.9.3: - QDarkStyleSheet is an optional package now Version 0.9.2: - New layout of job file editor - Keyword arguments in job file editor - Linux compatibility

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.

Allows creation and manipulation of diskless Linux (and Unix) clients, using templated system configuration files to increase the flexability and compatability of this application.

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.

GXP is a parallel/distributed shell, plus a parallel task execution engine that runs your Makefile in parallel on distributed machines. Very easy to install (no need to compile. install it on YOUR machine and use it on ALL machines).

KestrelHPC is a software developed to make life easier to the people that want to have a Diskless Cluster. This Cluster is Multi-User and it has got many tools to configure your cluster. This software can be installed in a Debian or Ubuntu.

Can be deployed on AWS, GCE, Azure, OpenStack, vSphere, Equinix Metal (bare metal), Oracle Cloud Infrastructure (Experimental), or Baremetal. Highly available cluster. Composable (Choice of the network plugin for instance). Supports most popular Linux distributions. Continuous integration tests. The list of available docker versions is 18.09, 19.03, and 20.10. The recommended docker version is 20.10. The kubelet might break on docker's non-standard version numbering (it no longer uses semantic versioning). To ensure auto-updates don't break your cluster look into e.g. yum version lock plugin or apt pin). The target servers must have access to the Internet in order to pull docker images. Otherwise, additional configuration is required. The target servers are configured to allow IPv4 forwarding. If using IPv6 for pods and services, the target servers are configured to allow IPv6 forwarding.

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.

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.

MAF (Mobile Agent Framework) is a research prototype which aims to facilitate the development and evaluation of mobile agent by providing a set of handy primitatives and simulation tools.

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.

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.

Pluster is a cluster management software developed with python. It is easy to install and maintain. Pluster Provides cluster performance monitoring and remote OS installing. It's future will be an agent-based management system.

PuSSH is Pythonic, Ubiquitous SSH, a Python wrapper/script that runs commands in parallel on clusters/ranges of linux/unix machines via SSH, ideally where SSH is configured to use Kerberos, RSA/DSA keys, or ssh-agent as to avoid password authentication.

PyGFS is a reliable and secure distributed file system that runs completely in userspace. It supports x509 certificate authentication, authorization and encryption mechanisms to provide secure access over insecure networks.

This package provides Python bindings to the OpenPBS C API. All functions of the OpenPBS C library can be accessed in Python through the use of this package. We provide extensive documentation on the usage and lots of examples.

RainforestCluster is an Amazon EC2 python program that manages and load-balances dynamic clusters to allow for maximum workflow flexibility and speed at minimal cost. It enables one to quickly and cheaply create dynamic compute clusters in the cloud, which can then run computational pipelines generically. It is also able to optimize the use of spot instances - idle computers in Amazon's cloud that are available at drastically reduced cost (5x-10x cheaper) - but can be terminated at any moment if capacity drops or the bid price rises. It also provides pre-installed features such as GlusterFS distributed filesystems, ThunderstormDistributor queuing system, RAID 0 /scratch, password-less ssh, and automatic cluster management, for ease of use and maximum processing speed for computational tools. Originally it was developed as a different version for the Wall Lab at Harvard CBMI.