Open Source Python Clustering Software
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Python Clustering Software
View 8593 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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Auth0 for AI Agents now in GAConnect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.
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Lightspeed golf course management softwareFrom tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
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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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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 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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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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Desktop and Mobile Device Management SoftwareDesktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
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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
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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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Python port of Platform LSF API using swig. Access is provided directly to the API call in addition to server Object classes.
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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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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.
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Dun and Bradstreet Risk Analytics - Supplier IntelligenceRisk, procurement, and compliance teams across the globe are under pressure to deal with geopolitical and business risks. Third-party risk exposure is impacted by rapidly scaling complexity in domestic and cross-border businesses, along with complicated and diverse regulations. It is extremely important for companies to proactively manage their third-party relationships. An AI-powered solution to mitigate and monitor counterparty risks on a continuous basis, this cutting-edge platform is powered by D&B’s Data Cloud with 520M+ Global Business Records and 2B+ yearly updates for third-party risk insights. With high-risk procurement alerts and multibillion match points, D&B Risk Analytics leverages best-in-class risk data to help drive informed decisions. Perform quick and comprehensive screening, using intelligent workflows. Receive ongoing alerts of key business indicators and disruptions.
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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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The Portable Highly Available Sensors (PHASE) project aims at creating an example application that demonstrates the usage of Application Interface Specification service interfaces of the Service Availability Forum.
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Zeus Grid is a Grid Computing environment usefull to run systems in heterogenous machines at same time. In this first step, it will only compile, run and collect application results and file storage.
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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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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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