Open Source Python Clustering Software
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Python Clustering Software
View 8511 business solutionsBrowse free open source Python Clustering Software and projects below. Use the toggles on the left to filter open source Python Clustering Software by OS, license, language, programming language, and project status.
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Kubespray
Deploy a Production Ready Kubernetes Cluster
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. -
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KubiScan
A tool to scan Kubernetes cluster for risky permissions
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. -
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Ganglia
Scalable, distributed monitoring system for high-performance computing
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. -
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Manage node sets, node groups and execute commands on cluster nodes in parallel. Provides an event-based Python library to improve administration of large compute clusters or server farms. Command line tools: clush and nodeset included.
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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Kubez-ansible
To provide quick deployment tools for kubernetes cluster
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. -
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Spilo
Highly available elephant herd: HA PostgreSQL cluster using Docker
Spilo is a Docker-based HA PostgreSQL cluster built on Patroni and heavily optimized for Kubernetes environments. It includes components for failover, streaming replication, backups, and connection pooling. Spilo is used in production by Zalando and is designed to provide a resilient, self-healing Postgres cluster with minimal manual intervention. -
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Genv
GPU environment management and cluster orchestration
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. -
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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.
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Open HPI is an open source implementation of the SA Forum's Hardware Platform Interface (HPI). HPI provides an abstracted interface to managing computer hardware, typically for chassis and rack based servers. The new location for the project is https://github.com/open-hpi/openhpi
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Create and run cloud-based virtual machines.Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications.
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dispy
Distributed and Parallel Computing with/for Python.
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. -
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
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. -
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ActiveGrid is an Enterprise Web 2.0 solution that allows the composition of code-free applications that comply with corporate IT standards. Technologies include Python, Java, XForm, Xpath, WSDL, CSS, XML Schema (XSD), XACML, and BPEL.
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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.
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Clubmask is a resource and job manager that ties together the Bproc global process space system, SIS, the System Installation Suite for installing Linux clusters, the Maui HPC Scheduler, and Supermon resource managers. The goal of Clubmask is to provide
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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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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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Infinispan
High performance distributed in-memory key/value store
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 -
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The ZSyncer project allows multiple Zopes to easily be manually synchronized by transferring data between them using xml-rpc. This can save a lot of time over the old export / transfer / import dance. See the home page for more information.
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asyncoro
Python framework for asynchronous, concurrent, distributed programming
asyncoro is a Python framework for developing concurrent, distributed, network programs with asynchronous completions and coroutines. Asynchronous completions implemented in asyncoro are sockets (non-blocking sockets), database cursors, sleep timers and locking primitives. Programs developed with asyncoro have same logic and structure as Python programs with threads, except for a few syntactic changes. asyncoro supports socket I/O notification mechanisms epoll, kqueue, /dev/poll (and poll and select, where necessary), and Windows I/O Completion Ports (IOCP) for high performance and scalability, and SSL for security -
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coNCePTuaL
DSL for writing communication benchmarks
coNCePTuaL is a toolset for rapidly generating portable, readable, and reproducible network-performance tests. coNCePTuaL can perform the equivalent of many pages of C code with just a few mouse clicks or lines of code in a domain-specific language. -
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slurm-roll
Slurm Resource Manager for Rocks Clusters
Slurm is a replacement for other resource management software and schedulers like gridengine or torque. The slurm roll integrates very well into a rocks clusters installation. In the folder addons, there are a lot of useful rolls for rocks clusters 6.1 and 6.2. These rolls do not depend on slurm. Please see Discussion for further information and support -
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Condor GUI
GUI for managing and working with one or more HTCondor pools.
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 -
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The Distribulator: Distributed Computing For The Rest Of Us. This is an SSH-based command execution and file transfer utility that includes support for batch, console, and shell integration modes, multiple server enviornments, and full audit logs.