Month: August 2024
My email cannot connect to the server
When I open my email, I get an error saying it cannot connect to the server. I have important emails. I need your access. How do I correct this problem?
When I open my email, I get an error saying it cannot connect to the server. I have important emails. I need your access. How do I correct this problem? Read More
Password for EXCEL document lost
I seem to have lost me password for my EXCEL document. Is there anyway to recover it, or override it?
Thanks!
I seem to have lost me password for my EXCEL document. Is there anyway to recover it, or override it? Thanks! Read More
Share point list
Using the List layout, which currently displays two columns in a row, how to set custom to one column in a row and four columns in a row
Using the List layout, which currently displays two columns in a row, how to set custom to one column in a row and four columns in a row Read More
How to impose MFA for external users who may access encrypted messages?
We use Microsoft Purview labels to encrypt emails. We’ve disabled the OTP and would like to replace it with MFA. Is there an option? According to Microsoft documentation, this is not possible, however there may have been a workaround.
We use Microsoft Purview labels to encrypt emails. We’ve disabled the OTP and would like to replace it with MFA. Is there an option? According to Microsoft documentation, this is not possible, however there may have been a workaround. Read More
Issues with Search-UnifiedAuditLog
I have been trying to export user mailbox audit logs using search-unfiedauditlog PS script but it does not export or find any logs related to delete, movetodeleteditems,softdelete or harddelete operations. I tried below and all other options and either it error out or does not pull anything. I am trying with all below operations and only UPDATE & CREATE works.
Search-UnifiedAuditLog -StartDate “08/01/2024” -EndDate “08/15/2024” -UserIds “email address removed for privacy reasons” -RecordType “ExchangeItem” -Operations “SoftDelete”, “HardDelete”, “MoveToDeletedItems” -ResultSize 5000 -SessionCommand ReturnLargeSet -HighCompleteness
OR
Search-UnifiedAuditLog -StartDate “08/01/2024” -EndDate “08/15/2024” -UserIds “email address removed for privacy reasons” -RecordType “ExchangeItem” -Operations “SoftDelete”, “HardDelete”, “MoveToDeletedItems” -ResultSize 5000 -SessionCommand ReturnLargeSet
I also tried the above PS using each individul operations but nothing works.
Does anybody have any clue or fix for this? Appreciate your help.
I have been trying to export user mailbox audit logs using search-unfiedauditlog PS script but it does not export or find any logs related to delete, movetodeleteditems,softdelete or harddelete operations. I tried below and all other options and either it error out or does not pull anything. I am trying with all below operations and only UPDATE & CREATE works. Search-UnifiedAuditLog -StartDate “08/01/2024” -EndDate “08/15/2024” -UserIds “email address removed for privacy reasons” -RecordType “ExchangeItem” -Operations “SoftDelete”, “HardDelete”, “MoveToDeletedItems” -ResultSize 5000 -SessionCommand ReturnLargeSet -HighCompletenessORSearch-UnifiedAuditLog -StartDate “08/01/2024” -EndDate “08/15/2024” -UserIds “email address removed for privacy reasons” -RecordType “ExchangeItem” -Operations “SoftDelete”, “HardDelete”, “MoveToDeletedItems” -ResultSize 5000 -SessionCommand ReturnLargeSet I also tried the above PS using each individul operations but nothing works.Does anybody have any clue or fix for this? Appreciate your help. Read More
New on Azure Marketplace: August 1-11, 2024
We continue to expand the Azure Marketplace ecosystem. For this volume, 229 new offers successfully met the onboarding criteria and went live. See details of the new offers below:
Get it now in our marketplace
Adaptive Analytics Suite: Adaptive Analytics Suite from Spektra Systems is a platform that offers advanced analytics to adapt to changing business needs. It provides real-time insights, predictive capabilities, and robust data analysis to help organizations make informed decisions.
AI ERP: This solution from Pintor Project uses AI to automate and optimize purchasing, warehouse stock management, order fulfillment, and sales processes. It offers multi-channel capabilities and personalized recommendations to enhance operational efficiency and adaptability for businesses of all sizes. The ERP solution eliminates manual tasks, reduces human error, and provides real-time visibility into the supply chain.
Aitomatic aiKO | AI Agents for Industrial Companies: Aitomatic aiKO is an industrial knowledge organizer that creates custom AI agents to solve complex problems in sectors like finance and semiconductor. The agents provide expert-reliable and actionable recommendations, adapting to dynamic environments. The team behind aiKO has a century of combined Industrial-AI experience, making them a trusted partner for companies looking to optimize operations.
AlmaLinux 8.10 Generation 2 with Support by Rinne Labs: AlmaLinux 8.10 minimal is a lightweight and secure image built from the official ISO image, with only essential packages for optimal performance. It reduces storage requirements, optimizes system responsiveness, and is updated with the latest security patches and updates. Ideal for rapid deployment of web applications, efficient development and testing environments, stable and secure server infrastructure, data analytics, and machine learning.
AlmaLinux 9 Generation 2 with Support by Rinne Labs: AlmaLinux 9 minimal is a lightweight and secure image built from the official ISO image, with only essential packages for optimal performance. It reduces storage requirements, optimizes system responsiveness, and is updated with the latest security patches and updates. Ideal for rapid deployment of web applications, efficient development and testing environments, stable and secure server infrastructure, data analytics, and machine learning.
AlmaLinux 9 Generation 2 with Support by Rinne Labs: AlmaLinux 9 minimal is a lightweight and secure image built from the official ISO image, with only essential packages for optimal performance. It reduces storage requirements, optimizes system responsiveness, and is updated with the latest security patches and updates. Ideal for rapid deployment of web applications, efficient development and testing environments, stable and secure server infrastructure, data analytics, and machine learning.
Apache Guacamole v1.5.5 on Ubuntu 20: Apache Guacamole is a clientless remote desktop gateway that allows users to access and manage remote desktops and servers through a web browser. It supports various protocols and offers multi-factor authentication and central management of remote access policies. It can be easily deployed on-premises or in the cloud and is customizable. It provides a reliable, secure, and flexible remote access solution for modern, distributed work environments.
Apache Spark: ATH Infosystems offers Apache Spark, an open-source distributed computing framework for big data processing and analytics, with in-memory processing, parallel computation, and support for various workloads and programming languages. Spark integrates seamlessly with other big data technologies and frameworks such as Hadoop, HDFS, Hive, Kafka, and more, allowing users to leverage existing infrastructure and data sources.
Arqit NetworkSecure – Standalone (Test Mode) Adaptor: Arqit NetworkSecure Standalone Adaptor provides static symmetric keys to devices for integration testing purposes only. For on-demand, scalable quantum-safe symmetric keys, upgrade to the NetworkSecure Adaptor and access SKA-Platform features. Suitable for protecting point-to-point VPN data links against quantum threats.
BidCortex AI Studio: BidCortex is an RFP accelerator that categorizes RFPs into distinct sections, saving time and reducing dependency on pre-sales teams. It seamlessly integrates with your existing data lake and provides a competitive advantage in the bidding process.
Black on Debian 11: Black automatically formats Python code according to predefined rules. This pre-configured image on Azure simplifies integration and provides a consistent environment across different compute resources. Azure experts are available around the clock for support. Key features include efficiency, consistency, integration with CI/CD, ease of use, and high performance.
Black on Oracle Linux 8.8: Black automatically formats Python code according to predefined rules. This pre-configured image on Azure simplifies integration and provides a consistent environment across different compute resources. Azure experts are available around the clock for support. Key features include efficiency, consistency, integration with CI/CD, ease of use, and high performance.
Black on Red Hat Enterprise Linux 8.7: Black automatically formats Python code according to predefined rules. This pre-configured image on Azure simplifies integration and provides a consistent environment across different compute resources. Azure experts are available around the clock for support. Key features include efficiency, consistency, integration with CI/CD, ease of use, and high performance.
Black on Ubuntu 20.04 LTS: Black automatically formats Python code according to predefined rules. This pre-configured image on Azure simplifies integration and provides a consistent environment across different compute resources. Azure experts are available around the clock for support. Key features include efficiency, consistency, integration with CI/CD, ease of use, and high performance.
Black on Ubuntu 22.04 LTS: Black automatically formats Python code according to predefined rules. This pre-configured image on Azure simplifies integration and provides a consistent environment across different compute resources. Azure experts are available around the clock for support. Key features include efficiency, consistency, integration with CI/CD, ease of use, and high performance.
Black on Ubuntu 24.04 LTS: Black automatically formats Python code according to predefined rules. This pre-configured image on Azure simplifies integration and provides a consistent environment across different compute resources. Azure experts are available around the clock for support. Key features include efficiency, consistency, integration with CI/CD, ease of use, and high performance.
Carahsoft Offer: Ivanti Neurons for Discovery: Carahsoft offers Ivanti Neurons for Discovery, which provides quick and precise asset information, identifying users and endpoints connected to the network, their connection time, and installed software.
Carahsoft Offer: Ivanti Neurons for ITSM: Carahsoft offers Ivanti Neurons for ITSM, This product offers automation, efficiency, and security for IT service management operations. It aims to improve productivity, compliance, and satisfaction levels for enterprises.
CB Platform – Virtual Machine: Crowdbotics uses AI to enable code reuse at scale, accelerating application development. Its CodeOps approach gathers specs, matches reusable code modules, and assembles them into new projects, freeing up developers to focus on creating unique code. The platform is built on Microsoft Azure and optimized for building new applications that run on Azure.
CentOS 7 with Extended Lifecycle Support: CentOS 7 is a stable and secure platform for enterprise workloads on Azure. TuxCare’s extended support provides ongoing security updates, bug fixes, and technical assistance. It is cost-effective and optimized for cloud environments, making it ideal for budget-conscious applications, legacy application support, and development and testing environments.
CentOS 8 Stream with Extended Lifecycle Support: CentOS 8 Stream is a stable and secure platform for enterprise workloads on Azure. TuxCare’s extended support provides ongoing security updates, bug fixes, and technical assistance. It is cost-effective and optimized for cloud environments, making it ideal for budget-conscious applications, legacy application support, and development and testing environments.
CentOS 8.5 with Extended Lifecycle Support: CentOS 8.5 is a stable and secure platform for enterprise workloads on Azure. TuxCare’s extended support provides ongoing security updates, bug fixes, and technical assistance. It is cost-effective and optimized for cloud environments, making it ideal for budget-conscious applications, legacy application support, and development and testing environments.
Church 365: Church 365 is a system that maximizes the value of Office 365 and Microsoft Azure for churches. It enables team creation and management, assigns roles to volunteers, and offers capabilities such as email automation, member surveys, live streaming, digital signage, and public website creation. It helps church staff coordinate technology usage and extend it to the broader membership while reducing costs.
Cipher xMDR Platform and Services: Cipher xMDR Service provides early detection and response to threats through its cloud-based xMDR platform, which uses artificial intelligence to efficiently manage alerts and generate enriched investigations. The service offers customizable protection, talent management, and cost optimization. The platform generates a model of the digital adversary and focuses on in-depth investigations, robotized and assisted technology, and continuous threat hunting.
CIS Level 1 Benchmarks for Microsoft Windows 11 Enterprise Multi-Session: This Azure-based virtual machine comes with a pre-configured Windows 11 Enterprise multi-session image that meets CIS level 1 compliance needs. CIS benchmarks provide guidelines for securing and configuring Windows 11 Enterprise multi-session systems to mitigate common security vulnerabilities and threats. Madarson IT images are up-to-date, secure, and follow industry standards.
CIS Level 1 Benchmarks for Microsoft Windows 11 Pro: This Azure-based virtual machine comes with pre-configured Windows 11 Pro image that meets Center for Internet Security (CIS) level 1 compliance needs. CIS benchmarks provide guidelines for securing and configuring Windows 11 Pro systems to mitigate common security vulnerabilities and threats. Madarson IT images are always up to date, secure, and built to work right out of the box.
CIS Level 2 Benchmarks for Microsoft Windows 11 Enterprise Multi-Session: This Azure-based virtual machine from Madarson IT comes pre-configured with the latest Microsoft Windows 11 Enterprise multi-session image that is hardened to address the Center for Internet Security (CIS) level 2 compliance needs. CIS benchmarks provide detailed configuration settings and recommendations for various components of Windows 11 Enterprise multi-session, including operating system settings, services, applications, and network configurations.
CIS Level 2 Benchmarks for Microsoft Windows 11 Pro: This Azure-based virtual machine from Madarson IT comes pre-configured with the latest Microsoft Windows 11 Pro image that is hardened to address the Center for Internet Security (CIS) level 2 compliance needs. CIS benchmarks for Windows 11 Pro provide guidelines and best practices for securing and configuring Windows 11 Pro systems to mitigate common security vulnerabilities and threats.
Cisco DNA Software for SD-WAN and Routing: Cisco DNA Software offers cloud delivery of Catalyst SD-WAN control components with automatic updates for enhanced security and functionality. It also offers centralized management, predictive analytics, and cloud on-ramp for optimized user experience. Cisco provides SD-WAN infrastructure and life cycle management for both hosted and cloud-delivered options.
ClickHouse v24.7.2.13 on Ubuntu 20: ClickHouse is an open-source, columnar database management system designed for high-speed online analytical processing. Its columnar storage format enhances data retrieval performance and compression efficiency, making it ideal for real-time data analysis. ClickHouse’s distributed processing architecture ensures high availability and effective management of extensive datasets.
Cloud Resilience Copilot: The Dual-vault Cloud Time Machine is a recovery-as-code solution that can restore and rebuild Azure IaaS, PaaS, Serverless, and Container resources to any point in time. It reduces application downtime, avoids duplicate cloud infrastructure, and simplifies the recovery process.
Cloudshot: Cloudshot monitors key performance indicators, tracks resource utilization, and identifies opportunities for optimization in your public cloud account. It provides advanced analytics capabilities, including predictive modeling and trend analysis, for proactive issue identification, capacity planning, and resource allocation optimization.
CodeIgniter: CodeIgniter is a powerful PHP framework that’s specially built for developers who need a very simple and elegant toolkit to create full-fledged web applications. It helps developers to minimize the amount of code for a given task or a project that helps in delivering projects faster than the anticipated time.
Cognitive Solutions: Cognitive Solutions by Pyxis offers a platform for building AI assistants that can be customized by business specialists. The assistants are flexible and personalized, adapting to the user’s needs. The platform includes innovative tools for personalization and monitoring, as well as a self-improvement module and evaluation and monitoring modules.
Connected Operational Risk Management Application: Corizance is a connected risk intelligence platform for high-risk industries, offering features like risk assessment, monitoring, and management. It also provides solutions for cyber risk quantification, compliance, and fraud monitoring. The platform boasts benefits like revenue savings, operational efficiencies, and risk management efficiency.
Crayon FortiManager and FortiAnalyzer Solution: FortiManager and FortiAnalyzer are solutions developed by Fortinet to manage and configure their security infrastructure. FortiManager provides tools for network administrators to deploy, configure, and monitor Fortinet devices and policies. FortiAnalyzer offers insights into network activity, compliance requirements, and operational efficiency by collecting and analyzing log data from Fortinet products and third-party devices.
Debian 10 with OpenVPN Server: Debian 10 with OpenVPN Server from Virtual Pulse is a secure solution for creating private networks, encrypting internet traffic and protecting sensitive data. Ideal for IT professionals, network administrators, and privacy-conscious individuals, it offers flexibility to configure and manage VPN connections, ensuring network security against potential threats.
Debian 11 with OpenVPN Server: Transform your network security and remote access capabilities with Debian 11 and OpenVPN Server from Virtual Pulse. Ideal for businesses and privacy-conscious individuals, it offers secure remote access, privacy protection, easy deployment and management, scalability, and monitoring and troubleshooting. Establish a secure VPN solution tailored to your specific requirements.
Docker Engine Enterprise on Windows Server 2022: Tidal media offers Docker Engine Enterprise, a powerful container platform for designing, deploying, and managing applications. It offers advanced networking capabilities, image signing, and multi-node swarm management for large-scale container deployments. With native Microsoft Windows support and robust security, it provides a reliable and secure platform for modern application development.
DocLens – AI Document Summarizer: DocLens is an AI-powered solution that summarizes documents based on specific needs and perspectives. It analyzes text, understands context, and extracts pertinent information to provide a concise, accurate summary. This role-based summarization increases workforce efficiency and saves time by 30%. Shorthills AI provides DocLens as a module to accelerate AI/ML journeys for enterprises.
Effortless Deployments on Kubernetes with Argo CD: Enhance your Kubernetes experience with TechLatest’s virtual machine offer pre-installed with Argo CD, a GitOps continuous delivery tool. Automate deployments, increase operational efficiency, and ensure security with role-based access control. Manage multiple clusters from a single interface and choose from various synchronization strategies.
Elasticsearch v8.14.3 on Ubuntu 20: Anarion Technologies offers Elasticsearch, a scalable, open-source search and analytics engine that supports complex queries at high speeds. It can handle various data types and formats, making it suitable for log and event data analysis, full-text search, and business intelligence. Its RESTful API simplifies integration with other applications and services, while its integration with other tools in the Elastic Stack enhances its capabilities for data analysis and visualization.
Enhanced IQ: Enhanced IQ from Dynamico AI is a secure and compliant platform that simplifies the process of building and deploying AI solutions with user-friendly interfaces and pre-built templates. It integrates with existing company data and systems, ensuring that AI models are grounded in the organization s specific context, enhancing relevance and accuracy.
Enterprise Compliance Management Application for AMCs and Wealth Managers: CORIZANCE is an AI-powered risk and compliance management platform designed for AMCs and wealth management companies to comply with non-fund related regulatory requirements. It offers real-time monitoring, risk mitigation, and comprehensive compliance tools. The platform ensures full compliance with RBI operational risk management requirements and provides a user-friendly interface for easy navigation.
EspoCRM on CentOS: ATH Infosystems offers EspoCRM, an open-source CRM platform with configurable dashboards, reporting tools, and automated workflows. It can be integrated with third-party applications and services. The platform includes powerful tools for generating detailed reports on sales, customer interactions, and performance metrics.
Exotel Intelligent Virtual Agent: Chatbot: Exotel offers AI-powered communication solutions for enterprises to enhance customer engagement and experience. Its Gen AI-powered voice and chatbots understand context, intent, and sentiment, leading to personalized and efficient customer service. The platform integrates with existing systems, providing a robust solution for modern enterprises.
Exotel Intelligent Virtual Agent: Voicebot: Exotel offers AI-powered communication solutions for enterprises to enhance customer engagement and experience. Its Gen AI-powered voice and chatbots understand context, intent, and sentiment, leading to personalized and efficient customer service. The platform integrates with existing systems, providing a robust solution for modern enterprises.
Fuff on Debian 11: FUFF is a user-friendly Python framework for streamlined development. This pre-configured Microsoft Azure image simplifies integration and provides a consistent environment across different compute resources. Azure experts are available for support. Key features include efficiency, modularity, and high performance.
Fuff on Oracle Linux 8.8: FUFF is a user-friendly Python framework for streamlined development. This pre-configured Microsoft Azure image simplifies integration and provides a consistent environment across different compute resources. Azure experts are available for support. Key features include efficiency, modularity, and high performance.
Fuff on Red Hat Enterprise Linux 8.7: FUFF is a user-friendly Python framework for streamlined development. This pre-configured Microsoft Azure image simplifies integration and provides a consistent environment across different compute resources. Azure experts are available for support. Key features include efficiency, modularity, and high performance.
Fuff on Ubuntu 20.04 LTS: FUFF is a user-friendly Python framework for streamlined development. This pre-configured Microsoft Azure image simplifies integration and provides a consistent environment across different compute resources. Azure experts are available for support. Key features include efficiency, modularity, and high performance.
Fuff on Ubuntu 22.04 LTS: FUFF is a user-friendly Python framework for streamlined development. This pre-configured Microsoft Azure image simplifies integration and provides a consistent environment across different compute resources. Azure experts are available for support. Key features include efficiency, modularity, and high performance.
Fuff on Ubuntu 24.04 LTS: FUFF is a user-friendly Python framework for streamlined development. This pre-configured Microsoft Azure image simplifies integration and provides a consistent environment across different compute resources. Azure experts are available for support. Key features include efficiency, modularity, and high performance.
GeneFrame AI Studio: GeneFrame from Shorthills AI is an AI-based tool that creates visual family charts from uploaded files containing patient information in various formats. It’s customizable and can be integrated into your data lake.
GeneSys AI Studio: GeneSys is an AI-based application that creates patient pedigree charts by collecting their family history through a simple chat interface. It presents the data in the visual form of a family tree and a concise summary, helping medical practitioners form the gene tree quickly and enhance medical assessments.
Go Language v1.22.5 on Ubuntu 20: Go is an open-source programming language created by Google in 2009. It features a clean syntax and strong support for concurrent programming, and emphasizes performance and efficiency. Its robust standard library allows developers to build complex applications without relying on external libraries. Go is well-suited for developing high-performance, scalable applications such as web servers, networking tools, and distributed systems.
GoPhish Phishing Simulator on Ubuntu – Hardened by HailBytes: GoPhish is a phishing simulation platform that helps organizations enhance their cybersecurity defenses. It offers customizable campaigns and templates, real-time analytics, and easy integration with existing security tools. GoPhish empowers employees to recognize and respond to threats, making it ideal for organizations of all sizes.
HomeaZZon: HomeaZZon from Cognitive Generation Enterprises is a platform designed for property developers, architects, and real estate professionals to streamline their operations and enhance productivity. It integrates project management, real-time collaboration, and customer engagement into one seamless interface, addressing common industry pain points.
IBM StreamSets: StreamSets is a data integration platform that helps enterprises migrate and modernize data platforms to power ML use cases and build cloud-ready applications. It enables seamless data ingestion from diverse sources into Microsoft Azure Storage, Azure Event Hub, Azure Synapse, and more. The platform offers new levels of enterprise visibility across the full lifecycle of data integration.
inFlow for Retail: inFlow for Retail from iNextLabs uses AI to personalize shopping, streamline e-commerce, expedite payments, optimize logistics, and strengthen customer support. It offers a robust dashboard for managerial control and transparency.
InfluxDB: ATH InfoSystems offers InfluxDB, an open-source time-series database optimized for monitoring, metrics, and analytics. It features high-performance data storage and retrieval, powerful querying capabilities, efficient data compression, and integration with visualization and monitoring tools.
Intenseye: Intenseye’s computer vision AI platform empowers EHS leaders to reduce work-related injuries and illnesses, automate workflows, and improve compliance. It continuously monitors for unsafe acts and conditions, assesses and mitigates workplace hazards, and digitizes EHS workflows. The platform transforms safety into a business advantage by improving operational efficiency and driving a positive culture.
iTop: ATH Infosystems offers iTop, an open-source ITSM solution with modules for incident, change, and service request management. It supports customizable workflows and automation, offers built-in reporting and dashboard features, and has a user-friendly interface for managing IT assets and configurations.
Ivanti Neurons for ITSM: Deliver new levels of satisfaction and effectiveness for your IT service management operations while making your business more productive, compliant, and secure.
Jina Reranker v2 Base – Multilingual: Jina Reranker v2 is a neural text reranking model that enhances search results by prioritizing relevant documents. It has high multilingual performance, can handle queries up to 512 tokens, and has ultra-fast document throughput. Ideal for vector search and retrieval augmented generation.
Joomla! CMS on Windows Server 2022: This virtual machine from Belinda CZ comes pre-configured with essential components to install and run Joomla! CMS. Choose between XAMPP and WampServer web servers. It includes tools for managing databases, analyzing web statistics, and testing email functionalities. Ideal for corporate sites, small business websites, e-commerce platforms, and more.
Joomla! on CentOS 8.5: ATH Infosystems offers Joomla!, an open-source CMS with a user-friendly interface, multilingual support, powerful user management, and built-in SEO features. It empowers users to build dynamic websites and powerful online applications.
Jupyter Notebook v7.2.1 on Ubuntu 20: Anarion offers Jupyter Notebook, an open-source web application that allows users to write and execute code in an interactive environment. It supports multiple programming languages and is ideal for data analysis, education, and collaborative projects. Notebooks can be easily shared and exported to various formats, making them a valuable tool for reproducible research.
JupyterHub: ATH Infosystems offers JupyterHub, an open-source platform for collaborative data analysis and research, supporting multi-user environments with flexible authentication options and scalable deployment options. Credentials are stored securely in the credentials.txt file.
Kanboard: ATH Infosystems offers Kanboard, an open-source project management tool that offers Kanban boards, customizable task management, project analytics, and collaborative features. Its drag-and-drop interface enables easy task updates, and multiple plugins and integrations extend functionality.
LAMP on Ubuntu 20: Anarion offers LAMP, a popular technology stack for developing and hosting dynamic web applications. It consists of Linux as the operating system, Apache as the web server, MySQL as the database management system, and PHP/Perl/Python as the scripting languages. LAMP provides a reliable and effective platform for web development, supporting small projects and large-scale web applications.
Laravel v8.83.27: Anarion offers Laravel, a PHP framework for web development that simplifies tasks like routing, authentication, and database management. It uses Eloquent ORM to simplify database interactions and offers built-in support for user management. Laravel also provides tools like Artisan and Blade to automate tasks and create dynamic views.
ReachFive: ReachFive is a customer identity and access management (CIAM) platform that provides a frictionless and secure customer experience. It offers authentication methods, data control, unified profiles, access control, and cloud-native deployment options. The platform connects customers, brands, partners, and applications securely, accelerating digital transformation.
Live Application Design: Live Application Design from Intact partners lets you create detailed prototypes in real time, reducing risk and time in implementing technology. Intended for technology and management analysts/consultants, it teaches designing live, interactive solutions, creating valuable BRD documentation, and supporting business cases, feasibility studies, and system modeling.
Move the Chain Engage: Move the Chain is an employee engagement platform that fosters connections among teammates and builds a sense of community within parent companies and at local levels. It integrates directly with Microsoft Teams and offers features such as employee onboarding, social intranet, recognition and gamification, community hub, event management, and corporate social responsibility.
NATS: ATH Infosystems offers NATS, a high-performance messaging system for cloud-native apps, with low latency and high throughput. It supports multiple messaging patterns and is designed for scalability and performance in cloud environments.
Neovim on Debian 11: Apps4Rent offers Neovim for Text Editing, a pre-configured image that offers a consistent environment for using the Neovim text editor. It includes all necessary software and is supported by Debian cloud infrastructure. Neovim is open-source, cost-effective, and suitable for various text editing tasks.
Neovim on Ubuntu 20.04 LTS: Apps4Rent offers Neovim for Text Editing, a pre-configured image that offers a consistent environment for using the Neovim text editor. It includes all necessary software and is supported by Ubuntu cloud infrastructure. Neovim is open-source, cost-effective, and suitable for various text editing tasks.
Neovim on Ubuntu 22.04 LTS: Apps4Rent offers Neovim for Text Editing, a pre-configured image that offers a consistent environment for using the Neovim text editor. It includes all necessary software and is supported by Ubuntu cloud infrastructure. Neovim is open-source, cost-effective, and suitable for various text editing tasks.
Neovim on Ubuntu 24.04 LTS: Apps4Rent offers Neovim for Text Editing, a pre-configured image that offers a consistent environment for using the Neovim text editor. It includes all necessary software and is supported by Ubuntu cloud infrastructure. Neovim is open-source, cost-effective, and suitable for various text editing tasks.
NestJS: ATH Infosystems offers NestJS, a TypeScript-based Node.js framework for building scalable server-side applications with a modular architecture and strong typing. It integrates with various tools and libraries, offering built-in support for microservices, WebSockets, and GraphQL. NestJS is an open-source project with a vibrant community and regular updates.
Nextcloud on CentOS 8.5: ATH Infosystems offers Nextcloud, a secure open-source file synchronization and sharing platform with collaboration features and end-to-end encryption options. Collaborate on documents in real-time using integrated applications and editing tools, and extend functionality with a wide range of apps and integrations.
Nextuple Capacity: Nextuple’s Capacity Management Service (NCM) integrates with existing systems to track and communicate capacity effectively. It accommodates new capacity types and allows AI and ML models to recommend capacity levels based on real-time data. NCM addresses use cases such as warehouse operations, transportation, customer receiving, service, manufacturing, and offers customizable templates.
NEXYTE: NEXYTE by Cognyte is a decision intelligence platform that helps organizations make data-driven decisions by fusing and analyzing data sources of all types. It empowers analysts and investigators to assess risks, conduct investigations, and optimize resources across multiple domains. NEXYTE provides advanced analytics for scalable decision-making and an open platform for collaborative data analysis.
Numpy on Oracle Linux 8.8: Numpy for Scientific Computing on Oracle Linux 8.8 is a pre-configured image that offers a consistent environment for using the Numpy library. It includes all necessary software and is supported by Oracle Linux cloud infrastructure. Apps4Rent handles integration complexities and offers around-the-clock support.
Oracle 8 with OpenVPN Server: Oracle 8 with OpenVPN Server from Virtual Pulse provides a reliable solution for secure remote access and data protection. It offers strong encryption, centralized management, scalability, and compliance readiness. The setup ensures confidential and secure communication channels for businesses of all sizes.
OSSN: ATH Infosystems offers OSSN, an open-source social networking platform with customizable user profiles, friend connections, groups, messaging system, and activity feeds. Themes and plugins to extend functionality are supported.
osTicket: ATH Infosystems offers osTicket, an open-source helpdesk management solution that streamlines support operations and improves customer satisfaction. It offers customizable ticketing, automated routing, email integration, canned responses, and comprehensive reporting.
PaddlePaddle on Debian 11: PaddlePaddle, a deep learning platform by Baidu, is available on Microsoft Azure through a pre-configured image by Apps4Rent. The image provides a consistent environment for developing and deploying PaddlePaddle, with support from Azure experts. Key features include efficiency, dynamic graph support, high-level APIs, ease of use, and high performance.
PaddlePaddle on Oracle Linux 8.8: PaddlePaddle, a deep learning platform by Baidu, is available on Microsoft Azure through a pre-configured image by Apps4Rent. The image provides a consistent environment for developing and deploying PaddlePaddle, with support from Azure experts. Key features include efficiency, dynamic graph support, high-level APIs, ease of use, and high performance.
PaddlePaddle on Red Hat Enterprise Linux 8.7: PaddlePaddle, a deep learning platform by Baidu, is available on Microsoft Azure through a pre-configured image by Apps4Rent. The image provides a consistent environment for developing and deploying PaddlePaddle, with support from Azure experts. Key features include efficiency, dynamic graph support, high-level APIs, ease of use, and high performance.
PaddlePaddle on Ubuntu 20.04 LTS: PaddlePaddle, a deep learning platform by Baidu, is available on Microsoft Azure through a pre-configured image by Apps4Rent. The image provides a consistent environment for developing and deploying PaddlePaddle, with support from Azure experts. Key features include efficiency, dynamic graph support, high-level APIs, ease of use, and high performance.
PaddlePaddle on Ubuntu 22. 04 LTS: PaddlePaddle, a deep learning platform by Baidu, is available on Microsoft Azure through a pre-configured image by Apps4Rent. The image provides a consistent environment for developing and deploying PaddlePaddle, with support from Azure experts. Key features include efficiency, dynamic graph support, high-level APIs, ease of use, and high performance.
PaddlePaddle on Ubuntu 24.04 LTS: PaddlePaddle, a deep learning platform by Baidu, is available on Microsoft Azure through a pre-configured image by Apps4Rent. The image provides a consistent environment for developing and deploying PaddlePaddle, with support from Azure experts. Key features include efficiency, dynamic graph support, high-level APIs, ease of use, and high performance.
Paper Polisher for PowerPoint: Paper Polisher is an AI-based writing assistant that refines content by using collegiate vocabulary substitutions, professional grammar conventions, and proper punctuation. It’s a Microsoft 365 Task Pane Add-in available for Word, Outlook, and PowerPoint that saves time and effort by turning brief passages into pages, transforming memos into messages, and making slides out of sentences with a single click.
Passgage – Employee Super App: Passgage is an all-in-one HR management solution that simplifies and streamlines HR processes. It offers time and attendance tracking, employee engagement tools, expense management, performance tracking, and internal communication features. Passgage consolidates all digital tools into one platform, providing a seamless experience that adapts to digital transformation.
Penfield.AI Platform for Human-AI Automation: Penfield.AI offers an automated workflow software platform that captures analyst interaction data across their tools and converts it into rich knowledge, which is then used to standardize and continuously improve processes. The platform integrates with different tools like SIEM, SOAR, and ITSM to improve productivity and continuously upskill analysts by capturing and analyzing data on their interactions with tools.
PrestaShop: ATH Infosystems offers PrestaShop, a customizable e-commerce platform with a powerful back office, themes, modules, payment and shipping options, and built-in tools for efficient management. Optimize your store for search engines and track performance with analytics and reporting features.
Puppet Bolt v3.30.0 on Ubuntu 20: Anarion offers Puppet Bolt, an open-source orchestration tool that simplifies and automates tasks across remote systems without requiring a Puppet agent. It supports various transport protocols, including SSH and WinRM, and can integrate with other Puppet tools. Bolt’s user-friendly command-line interface and YAML-based plan language make it ideal for quick fixes and complex orchestration scenarios.
PyTorch v2.4.0 on Ubuntu 20: Anarion offers PyTorch, an open-source machine learning library that lets researchers and developers experiment with complex neural network architectures. Its dynamic computational graph allows for real-time adjustments and fine-tuning during model training, offering greater flexibility and ease in debugging and experimenting with different model configurations.
QuerySense AI Studio: QuerySense from Shorthills AI is an AI-powered accelerator that enhances search capabilities by accurately classifying queries into keyword or semantic searches. It creates a personal knowledge base and understands the intent and context of queries to provide the most relevant results, improving user experience and efficiency. It can be integrated with existing data lakes for seamless document navigation.
RabbitMQ: ATH Infosystems offers RabbitMQ, an open-source message broker software that allows applications to communicate with each other reliably and asynchronously. It supports multiple messaging protocols, high availability, and flexible routing. The web management interface can be accessed at http://your-server-ip:15672. Use at your own risk.
Rancher and kubectl for Unified Kubernetes Control: Take control of your Kubernetes clusters with a pre-configured virtual machine from TechLatest featuring Rancher and kubectl. Manage multiple clusters from a single dashboard, streamline provisioning, access control, and monitoring, and troubleshoot issues with ease. Deploy and scale clusters effortlessly with Rancher’s robust features, designed for small and large-scale applications.
Red Hat Enterprise Linux 9.4 with Trusted Launch: Red Hat Enterprise Linux offers enterprise-level support for open-source technology with built-in security features and certifications. Ntegral Certified Apps provide up-to-date and secure images, while subscription manager allows for easy registration.
Redis: ATH Infosystems offers Redis, an open-source, in-memory data store used as a database, cache, and message broker. It supports various data structures and provides options for data persistence, replication, and automatic failover.
Relate: Relate by Forum Care Limited is a meeting enablement platform that objectively measures trust using Sandi, a Generative AI coach. It integrates with existing client-facing tools and CRM software and can be configured to comply with specific requirements. Relate offers detailed data to track team performance, as well as coaching and monitoring to support trust-building behaviors.
Rocky Linux 9 (ARM/AArch64) with Support by Rinne Labs: This Rocky Linux 9 minimal image is a lightweight and secure server infrastructure built from the latest official ISO image. It includes only essential packages for optimal performance, reducing storage requirements and optimizing system responsiveness. It’s ideal for rapid deployment of web applications, efficient development and testing environments, and data analytics and machine learning.
Rocky Linux 9.1 (ARM/AArch64) with Support by Rinne Labs: This Rocky Linux 9.1 minimal image is a lightweight and secure server infrastructure ideal for web applications, development and testing environments, and data analytics. It’s built from the official ISO image, updated with the latest security patches, and optimized for faster boot times and reduced resource consumption.
Rocky Linux 9.2 (ARM/AArch64) with Support by Rinne Labs: Rinne Labs offers a lightweight and secure Rocky Linux 9.2 image built from the official ISO image, with only essential packages for optimal performance. The image is updated with the latest security patches, making it ideal for rapid deployment of web applications, efficient development and testing environments, stable and secure server infrastructure, data analytics, and machine learning.
Rocky Linux 9.3 (ARM/AArch64) with Support by Rinne Labs: Rinne Labs offers a lightweight and secure Rocky Linux 9.3 image built from the official ISO image, with only essential packages for optimal performance. It’s fast to boot, reduces storage requirements, and is optimized for system responsiveness. It’s ideal for web applications, development, testing, and stable server infrastructure.
RStudio v2024.4.2_764 on Ubuntu 20: Anarion offers RStudio, a powerful IDE for the R programming language, catering to the needs of data scientists, statisticians, and researchers. Its intuitive interface, tools for data analysis, statistical computing, and data visualization make it an essential tool for anyone working with R. RStudio also supports reproducible research and dynamic reporting, enhancing collaboration and transparency in data analysis projects.
SCAPE API: SCAPE is a cloud-based business communication and customer experience technology that offers AI-powered omnichannel customer experiences across Voice, Email, Chat and emerging channels. It integrates with Microsoft products and CRMs such as Microsoft Dynamics and offers connectivity options through Microsoft Teams. SCAPE API allows integration with external systems.
ShipIntel: ShipIntel is an AI-driven maritime AIS toolbox that combines machine learning algorithms and generative AI technology with a company’s data to provide valuable insights for informed decisions. It offers live search and position lists, sea route calculator, live port and area monitoring, historical AIS tracking, predictive vessel destination, and customizable private maps. ShipIntel Pre-Fix includes maritime email tracking, AI-driven cargo handling, and pre-voyage calculations. It saves hours each day and is suitable for any type and size of companies involved in maritime business.
Synapse Data Fabric: Synapse Data Fabric from Spektra Systems is a comprehensive data management platform that unifies disparate data sources into a single, cohesive framework. It’s designed to streamline data integration, enhance data accessibility, and provide real-time analytics capabilities.
Tangra AI Image Generator: Tangra AI Image Generator offers stunning photorealistic visuals with various preset styles, AI-generated prompts, and templates. It provides creative freedom with landscape, portrait, and square modes, and the ability to generate similar and multiple images. The Pro version allows unlimited image creation and download.
TcpDump on Debian 11: TcpDump is a powerful command-line packet analyzer tool used for network monitoring and data acquisition. This package from Apps4Rent is a robust product image that offers a pre-configured environment for a user-friendly and consistent experience across compute resources.
TcpDump on Oracle Linux 8.8: TcpDump is a powerful command-line packet analyzer tool used for network monitoring and data acquisition. This package from Apps4Rent is a robust product image that offers a pre-configured environment for a user-friendly and consistent experience across compute resources.
TcpDump on Ubuntu 22.04 LTS: TcpDump is a powerful command-line packet analyzer tool used for network monitoring and data acquisition. This package from Apps4Rent is a robust product image that offers a pre-configured environment for a user-friendly and consistent experience across compute resources.
TcpDump on Ubuntu 24.04 LTS: TcpDump is a powerful command-line packet analyzer tool used for network monitoring and data acquisition. This package from Apps4Rent is a robust product image that offers a pre-configured environment for a user-friendly and consistent experience across compute resources.
TimeVision: TimeVision from Agic Technology is a web application for companies working on a contract or project basis. It allows employees to record hours, activities, and expenses for projects, as well as track time spent on internal projects and manage administrative information. The system is easy to use and integrates with Microsoft Dynamics Business Central ERP.
Tmux on Debian 11: Tmux is a terminal multiplexer that lets you switch easily between several programs in one terminal, detach them, and reattach them. Apps4Rent offers a pre-configured Tmux image, providing a consistent environment and handling integration complexities. Around-the-clock support is available.
Tmux on Oracle Linux 8.8: Tmux is a terminal multiplexer that lets you switch easily between several programs in one terminal, detach them, and reattach them. Apps4Rent offers a pre-configured Tmux image, providing a consistent environment and handling integration complexities. Around-the-clock support is available.
TraceRoute on Debian 11: TraceRoute is a network diagnostic tool that traces the route packets take from one computer to another. Apps4Rent offers a pre-configured TraceRoute image, providing a consistent and user-friendly experience for network diagnostics. Around-the-clock support is available.
TraceRoute on Oracle Linux 8.8: TraceRoute is a network diagnostic tool that traces the route packets take from one computer to another. Apps4Rent offers a pre-configured TraceRoute image, providing a consistent and user-friendly experience for network diagnostics. Around-the-clock support is available.
TraceRoute on Ubuntu 20.04 LTS: TraceRoute is a network diagnostic tool that traces the route packets take from one computer to another. Apps4Rent offers a pre-configured TraceRoute image, providing a consistent and user-friendly experience for network diagnostics. Around-the-clock support is available.
TraceRoute on Ubuntu 22.04 LTS: TraceRoute is a network diagnostic tool that traces the route packets take from one computer to another. Apps4Rent offers a pre-configured TraceRoute image, providing a consistent and user-friendly experience for network diagnostics. Around-the-clock support is available.
TraceRoute on Ubuntu 24.04 LTS: TraceRoute is a network diagnostic tool that traces the route packets take from one computer to another. Apps4Rent offers a pre-configured TraceRoute image, providing a consistent and user-friendly experience for network diagnostics. Around-the-clock support is available.
Ubuntu 20.04 with OpenVPN Server: Ubuntu 20.04 with OpenVPN Server from Virtual Pulse is a highly secure and customizable VPN solution that lets organizations create a private tunnel into a public network, protecting sensitive data from threats and unauthorized access. It offers 256-bit encryption, works with multiple operating systems, and is recommended by internet security experts.
Ubuntu 22.04 with OpenVPN Server: Ubuntu 22.04 with OpenVPN Server from Virtual Pulse is a highly secure and customizable VPN solution that lets organizations create a private tunnel into a public network, protecting sensitive data from threats and unauthorized access. It offers 256-bit encryption, works with multiple operating systems, and is recommended by internet security experts.
Ubuntu Pro 22.04 LTS (Confidential VM): Ubuntu Pro on Microsoft Azure Confidential VM from Canonical offers a highly secure cloud experience for sensitive and regulated workloads. It combines Ubuntu Pro’s advanced security features with Azure’s Confidential Computing technology, providing multi-layered security that protects data at rest, in transit, and during processing. The image is optimized for Azure and integrates with Azure tools for streamlined updates and management.
Unifii360 Advance Backorder Management: Unifii360 Advance Backorder Management for Microsoft Dynamics 365 Business Central from Eagle360 Consulting streamlines backorder management, enhances order fulfillment, and reduces delays. Key features include automatic tracking, customizable notifications, and detailed reporting.
Unifii360 EDI Integration: Unifii360 EDI Integration for Microsoft Dynamics 365 Business Central from Eagle360 Consulting streamlines EDI integration for managing back orders on sales orders. It improves communication with trading partners, reduces manual data entry, and ensures efficient order processing. Key features include automatic EDI document generation, real-time data synchronization, and comprehensive reporting.
Unifii360 Tasklet: Unifii360 Tasklet Addon for Tasklet Mobile WMS and Microsoft Dynamics 365 Business Central from Eagle360 Consulting optimizes warehouse management processes with advanced functionalities and automation features. Key features include breakbulk, auto post put away, auto post WS on pick, auto print shipment, auto email invoice, auto print invoice, and combine invoice.
Vtiger: ATH Infosystems offers Vtiger, an open-source CRM that helps businesses manage customer relationships, streamline sales processes, and improve productivity. It offers features for managing leads, opportunities, sales pipelines, marketing automation, and customer support.
Websoft9 Application Hosting Platform: Websoft9 is a self-hosting PaaS that allows easy deployment of over 200 template applications with a one-click approach. It employs a GitOps approach and includes an integrated app store. Users have complete autonomy, ensuring that applications can be easily configured and continuously deployed even after they are live. The web-based interface allows users to perform tasks such as domain binding, HTTPS setup, access control, and status monitoring.
Websoft9 Applications Hosting Platform for Apache Answer: Apache Answer is a cloud-native Q&A platform for online communities, available on the Websoft9 Applications Hosting Platform. Websoft9 is a lightweight, self-hosting PaaS that allows users to deploy multiple applications on their own cloud infrastructure. With a web-based interface, users can easily configure and continuously deploy applications and access a range of related applications with one-click deployment.
Wekan: ATH Infosystems offers Wekan, an open-source and collaborative kanban board application designed for task and project management. It provides a visual and intuitive interface for organizing tasks, workflows, and projects, making it suitable for individuals, teams, and organizations. Wekan integrates with various third-party tools and services through plugins and APIs, providing users with additional functionalities and customization options to enhance their workflow.
WorkspaceHub: WorkspaceHub is a Microsoft 365 control center that integrates with Teams, SharePoint Online, and Azure DevOps. It offers customization, metadata, and self-service to address organizational challenges. Users can create, manage, and search workspaces with an intuitive interface.
Go further with workshops, proofs of concept, and implementations
AI in Security Workshop: Sentinel’s AI in Security Workshop explains how AI is transforming IT security and offers expert guidance on how to protect critical assets in Microsoft Azure against evolving threats. The workshop covers how Microsoft Copilot for Security responds to threats, assesses current defensive tools, and provides actionable steps for protection.
Azure Cloud Assessment and Migration Service: 2-Week Implementation: This cloud assessment service from Alfa Connections helps businesses understand how to migrate their legacy applications to Microsoft Azure. It offers three approaches: lift-and-shift, modernization, and Greenfield. The service provides a detailed Azure readiness and sizing analysis, migration recommendations, and cost optimization steps. At the end of the assessment, businesses will have a clear understanding of how to host their application in Azure.
Azure Databricks Implementation: Microsoft Azure Databricks offers improved data processing performance, scalability, flexibility, collaborative notebooks, unified data lifecycle management, easy integration with other Azure services, Delta Lake for data management, and cost optimization. Professional services from Baufest include consulting, implementation and configuration, integration with other systems, solutions development, support and maintenance, and governance and compliance.
Azure Fundamentals: 12-Hour Proof of Concept: The Microsoft Azure Fundamentals course from Respect Solutions helps IT professionals gain practical experience with the platform and achieve the Microsoft Certified: Azure Fundamentals certification. It covers cloud principles, Azure services, and tools for managing and governing Azure environments. The course is tailored for IT professionals who are new to Azure and does not require prior scripting knowledge.
Azure Platform Management Support: ivision offers around-the-clock enterprise-class systems management and operations for Microsoft Azure, employing AI, root cause analysis, and cloud infrastructure alerting. It provides multiple tiers of support, including core managed service, reporting, monitoring, remediation, problem and incident management for Azure platform workloads, and identity and platform monitoring and remediation.
Azure Virtual Desktop (AVD): Proof of concept (PoC) Package: This package from Provectus Technologies provides a test environment for Azure Virtual Desktop, including scoping calls, workshops, deployment of code, and feedback on the process. It also includes identification of further action alternatives and a final report. This offer is available in Austria, Germany, and Switzerland.
Bluecycle Cribl Health Check: The Cribl Deployment Health Check evaluates the health and efficiency of your Cribl Enterprise deployment in Microsoft Azure. It reviews the system architecture, data flows, and performance metrics to identify opportunities for improved configuration, data source ingest, and event hub optimization. The findings provide actionable recommendations to enhance the overall health and performance of your Cribl deployment.
Chatbot Azure OpenAI and ChatGPT MVP: Microsoft Azure OpenAI and ChatGPT-4 allow for better decision-making by identifying patterns and trends in data, with direct communication through Microsoft Teams. The platform can access structured, unstructured, and semi-structured data, and can be asked questions in multiple languages. The approach consists of three phases: pre-requisites, discovery, and development, with a fully scalable platform.
Cloud Analytics Intro: 1-Day Workshop: Fellowmind’s “Azure AI Envisioning Workshop” offers a comprehensive insight into modernizing data infrastructure with Microsoft Azure. The workshop covers topics such as Azure Data Services, data strategies, modernization of data, integration of Azure Data Services, security in the Azure Cloud, and basic Azure AI services. The workshop also includes an introduction to using AI to recognize hidden patterns in data. This workshop is only available in Germany.
Data Governance Implementation: This implementation from AVASOFT offers data governance and quality management services for Microsoft Azure, including metadata management, compliance monitoring, and training programs. It aims to help organizations maximize their Azure investment, ensure compliance, and extract business value from their data assets. Pricing and timelines vary based on the Azure environment’s size and complexity.
Data Warehouse Implementation – Azure Databricks and ADF: AVASOFT offers a tailored Microsoft Azure Databricks and Azure Data Factory implementation package for organizations facing data challenges. They provide private Databricks infrastructure in Azure with seamless connectivity to on-prem data sources, DevOps CI/CD pipelines for automated infrastructure deployment and code promotion, and ADF integration for ETL operations or as a connector for data sources not supported in Databricks.
Database Health Checks and Audits: This service from AVASOFT offers comprehensive database health checks and audits for Microsoft Azure, including scalability and security assessments, resource utilization analysis, customizable alerting systems, and query performance enhancement. The outcome includes a detailed report, implementation plan, and transition plan for optimal performance, security, and resource utilization.
DXC Azure Staging Layer Accelerator: DXC Technology offers an accelerator to streamline data migration from legacy and non-standard sources, reducing time and cost for implementation. Their expertise in Microsoft Azure data migration projects includes accelerators for enhanced data ingestion, automation and efficiency, scalability and performance, and data quality and consistency.
EnterpriseGPT GDPR-Compliant: 2-Week Proof of Value: Inovex offers GDPR-compliant enterprise GPT technology for various contexts, including customer care, digital assistants, and content generation. The package includes Microsoft Azure OpenAI, corporate account login, chat interface, prompt library, PDF upload, and integration of additional document types and web search.
Entra ID with Bamboo HR: 5-Day Implementation: Microsoft Entra ID with Bamboo HR Implementation simplifies the user authentication and authorization process. It gives employees access to Bamboo HR and other cloud applications with one login and password. The integration helps customers utilize Azure’s security features more effectively, improve user productivity, enhance security and compliance, and reduce IT costs and complexity.
Entra ID with Ramp: 5-Day Implementation: Microsoft Entra ID with Ramp Implementation streamlines access management for cloud applications, including Ramp’s HR software. It simplifies user authentication and authorization, improves security, and reduces the complexity of managing user credentials. This integration gives employees access to Ramp and other cloud applications with one login and password.
Eviden SAP Data Lake Accelerator to Azure: 2- to 3-Month Implementation: SAP Data Lake Accelerator integrates SAP data with other datasets on Microsoft Azure, accelerating analytics capabilities, breaking down data silos, and enhancing organizational efficiency. It has modular and reusable data transformation code, maintainable codebase, scalability, data lineage and impact analysis, data observability, documentation, and uses dbt.
FinOps Services: Ciklum’s FinOps: Foundational Cycle consulting service helps control Microsoft Azure cloud costs and maximize value in just 6 weeks. Its three-phase approach includes a comprehensive review of Azure accounts, optimization of business needs with cloud spending, and implementation of quick wins for immediate cost savings. On average, clients see a 20-30% reduction in cloud costs.
Generative AI: Launch your AI strategy with Azure OpenAI: Henson Group’s solutions use generative AI to drive business outcomes in various industries while ensuring responsible AI practices. Its services continuously learn and adapt from interactions, becoming more efficient over time. Leveraging Microsoft Fabric and OpenAI models, these solutions are scalable and serve industries such as finance, healthcare, and manufacturing.
Managed CloudOps Services: 4-Week Implementation: Get a stable foundation for your business operations with a fully managed cloud operation package from Ciklum. The package includes proactive capacity management, regular patching and upgrades, backup and recovery tasks, security hardening, and more. The CloudOps team provides enhanced team readiness, robust monitoring and observability, streamlined deployment processes, optimal incident response framework, and scalable and efficient operations.
Microsoft Azure OpenAI Chatbot Workshop: teccle group’s Microsoft Azure OpenAI Chatbot Workshop provides an introduction to AI applications and solutions, demonstrating how AI can be applied in businesses. The workshop offers two options: one for those without a specific Azure OpenAI use case, and one for those with an existing use case. Both options include Microsoft Azure OpenAI basics, standard use cases, live demos, and a roadmap for prioritized AI solutions.
Microsoft Entra ID and Bamboo HR User Information Sync: 5-Day Consulting Service: This cloud-based integration solution from IT Partner enables seamless and secure data exchange between Entra ID and Bamboo HR for managing employee information. It reduces manual work and human errors, ensures data quality and compliance, and provides detailed logs of synchronization activities and errors for auditing and troubleshooting purposes.
Microsoft Entra ID and Ramp User Information Sync: 5-Day Consulting Service: Entra ID and Ramp offer a cloud-based integration solution for managing employee information. The service allows users to create, update, and deactivate employee accounts in both systems with a single action, ensuring data consistency and accuracy across the organization. Custom mapping of user attributes and logging of synchronization activities are also supported.
Microsoft Fabric Discovery: Half-Day Workshop: The Oakland Group offers a half-day Fabric Discovery Workshop to help organizations understand the key capabilities of Microsoft Fabric, identify the best use cases, and get a sense of the company’s culture. The workshop includes a detailed report on key use cases and recommendations, along with a follow-up session to explore potential collaboration.
Microsoft Fabric Pilot: 3-Week Proof of Concept: Microsoft Fabric simplifies data ingestion, transformation, and presentation. The pilot program offers a comprehensive overview of its capabilities and how they can benefit your organization. This program from SDK includes planning, setup, ingestion, reporting, and knowledge transfer.
Microsoft Intune Implementation: Avertium’s endpoint management solution for Intune streamlines onboarding of new workstations and safeguards data across all workstations and applications. Their multi-phased approach combines strategic insight with meticulous implementation support to maximize the value of Microsoft Azure. Avertium assesses, designs, and protects with a focus on IT infrastructure, security, and efficiency, providing tailored guidance and support every step of the way.
Microsoft Purview and Manta Lineage Connector: 4-Week Proof of Concept: The Prolifics Custom Connector enhances Purview’s data lineage capabilities for organizations with complex on-premises environments. It addresses gaps in Purview’s native lineage capture for various data sources, including SQL Server, Excel, and IBM DataStage. The Manta scanner extracts and analyzes metadata, and the connector loads lineage into Purview for inspection.
Microsoft Purview Information Protection: 12-Week Implementation: UNIFY Solutions offers consulting services to implement Microsoft Purview information protection. Its solution helps classify, label, and protect data based on its sensitivity, ensuring protection stays with the data as it moves across different locations, following a standard project approach for design, implementation, testing, and support.
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Migrate On-Prem SQL to Azure Databricks and Synapse: AVASOFT offers expert services for migrating on-prem SQL Server data warehouse to Microsoft Azure Synapse Analytics and Azure Databricks. This migration enhances data processing and analytics within a secure, scalable Azure environment, merging big data capabilities and collaborative data science to create a robust platform for data exploration, BI, and AI applications. Key features include seamless integration, enhanced analytics, security and compliance, cost-efficiency, and streamlined collaboration.
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Nous MuleSoft to Azure Migration Assessment: 2-Week Proof of Concept: Nous offers cost optimization and organizational alignment for a unified team. The assessment includes micro-app-based migration and incremental migration of 4 different layers of the MuleSoft system with validation through canary testing. Deliverables include a discovery report, target architecture, and roadmap for a comprehensive implementation plan.
Product Discovery: 8-Week Implementation: Ciklum’s Product Discovery with Microsoft Azure helps teams reduce uncertainty by identifying problems worth solving and building solutions with validated MVP scope. Azure’s analytics and data insights capabilities support decision-making, while scalable cloud services enable cost-effective testing of product ideas. The process accelerates speed to market, mitigates risks early, and prioritizes customer segments for MVP launch.
SDK Fabric Implementation: SDK offers a three-month program to implement Microsoft Fabric, a data management and analytics platform. The program includes data discovery, modeling, transformation, and blending, as well as the implementation of Fabric and Job Manager Accelerator. Benefits include improved analytics adoption, simplified platform management, and self-serve analytics.
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VMware to Azure VMware Solution Migration: Microsoft Azure VMware Solution (AVS) allows seamless migration of VMware-based workloads to dedicated Azure infrastructure, providing a fully managed VMware environment in Azure with all VMware licenses and Azure compute for a predictable monthly cost. Version 1 offers a VMware to AVS Migration Service with three collaborative phases, including discovery and assessment, design and migration, and ongoing Azure Managed Services.
XDR Rapid Protect (Sentinel and Defender Managed Security Operations): Kocho’s XDR Rapid Protect offers a cloud-native, AI-driven XDR service using Microsoft Sentinel and Defender. With around-the-clock monitoring, flexible contracts, and personalized service, Kocho’s Managed Security Service provides hassle-free onboarding and AI-powered defense against cyber threats.
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Microsoft Tech Community – Latest Blogs –Read More
Why is my model not converging with ode45 solver ?
I am trying to simulate an electronical device that can be modeled by a mass-spring-damper system with an additional non-linear force. The equation at the equilibrium for the system is the following :
The goal of my MATLAB code is to solve this equation for $x$ and find the equilibrium. For this purpose, I use the `ode45` function like this (all coefficient are defined in my code but not shown here) :
x0 = 0;
[t, x] = ode45(@(t, x) odefun(t, x, eps0, a, V0, B, d0, K), tspan, x0);
function dxdt = odefun(t, x, eps, a, V, B, d0, K)
dxdt = ((eps * a * V^2) ./ (2 * B * (d0 – x).^2)) + ((K / B) .* x);
end
The equation is good according to my teachers and several papers but the solver never converges and I can’t see why. All coefficient are defined according to the dimensions of a capacitive micromachined ultrasound transducer. The solution of ode45 is the following :
This is obviously wrong because the dimensions of a cmut are bellow millimeter.
Can you see any mistakes in the way I use the ode45 solver ?
Full code :
% DIMENSIONS DE LA MEMBRANE
e = 500e-9; % epaisseur [m]
r = 20e-6; % rayon [m]
d0 = 550e-9; % epaisseur de cavite [m]
a = pi * r^2; % surface de la membrane [m^2]
% PARAMETRES MECANIQUES
E = 200e9; % module d’Young du SiN [Pa]
nu = 0.25; % coefficient de poisson du SiN
eta = 18.5e-6; % viscosité dynamique de l’air [Pa.s]
p0 = 1e5; % pression exterieure [Pa]
rhoSiN = 3170; % densite du SiN [Kg/m^3]
m = rhoSiN * a * e; % masse membrane [Kg]
K = (16 * E * e^3) / (3 * (1 – nu^2) * r^2); % raideur [N/m]
B = (eta * pi * r^2) / e; % amortissement [N.s/m]
% PARAMETRES ELECTRIQUES
eps0 = 8.85e-12; % permittivité diélectrique du vide [F/m]
V0 = 10; % tension de polarisation [V]
% Conditions initiales et plage de temps
x0 = 0;
ti = 0; tf = 1e-3;
dt = 1e-6;
tspan = linspace(ti, tf, 1/dt);
% Résolution par RK4
[t, x] = ode45(@(t, x) odefun(t, x, eps0, a, V0, B, d0, K), tspan, x0);
plot(t,x,’-‘)
function dxdt = odefun(t, x, eps, a, V, B, d0, K)
dxdt = ((eps * a * V^2) ./ (2 * B * (d0 – x).^2)) + ((K / B) .* x);
end
References :
– Y. Wang, L. -M. He, Z. Li, W. Xu and J. Ren, "A computationally efficient nonlinear dynamic model for cMUT based on COMSOL and MATLAB/Simulink"
– T. Merrien, A. Boulmé and D. Certon, "Lumped-Parameter Equivalent Circuit Modeling of CMUT Array Elements"
I tried several solver from MATLAB and implemented my own Runge-Kutta algorithm based on the Wikipedia example. I also verified the coefficients according to my references.I am trying to simulate an electronical device that can be modeled by a mass-spring-damper system with an additional non-linear force. The equation at the equilibrium for the system is the following :
The goal of my MATLAB code is to solve this equation for $x$ and find the equilibrium. For this purpose, I use the `ode45` function like this (all coefficient are defined in my code but not shown here) :
x0 = 0;
[t, x] = ode45(@(t, x) odefun(t, x, eps0, a, V0, B, d0, K), tspan, x0);
function dxdt = odefun(t, x, eps, a, V, B, d0, K)
dxdt = ((eps * a * V^2) ./ (2 * B * (d0 – x).^2)) + ((K / B) .* x);
end
The equation is good according to my teachers and several papers but the solver never converges and I can’t see why. All coefficient are defined according to the dimensions of a capacitive micromachined ultrasound transducer. The solution of ode45 is the following :
This is obviously wrong because the dimensions of a cmut are bellow millimeter.
Can you see any mistakes in the way I use the ode45 solver ?
Full code :
% DIMENSIONS DE LA MEMBRANE
e = 500e-9; % epaisseur [m]
r = 20e-6; % rayon [m]
d0 = 550e-9; % epaisseur de cavite [m]
a = pi * r^2; % surface de la membrane [m^2]
% PARAMETRES MECANIQUES
E = 200e9; % module d’Young du SiN [Pa]
nu = 0.25; % coefficient de poisson du SiN
eta = 18.5e-6; % viscosité dynamique de l’air [Pa.s]
p0 = 1e5; % pression exterieure [Pa]
rhoSiN = 3170; % densite du SiN [Kg/m^3]
m = rhoSiN * a * e; % masse membrane [Kg]
K = (16 * E * e^3) / (3 * (1 – nu^2) * r^2); % raideur [N/m]
B = (eta * pi * r^2) / e; % amortissement [N.s/m]
% PARAMETRES ELECTRIQUES
eps0 = 8.85e-12; % permittivité diélectrique du vide [F/m]
V0 = 10; % tension de polarisation [V]
% Conditions initiales et plage de temps
x0 = 0;
ti = 0; tf = 1e-3;
dt = 1e-6;
tspan = linspace(ti, tf, 1/dt);
% Résolution par RK4
[t, x] = ode45(@(t, x) odefun(t, x, eps0, a, V0, B, d0, K), tspan, x0);
plot(t,x,’-‘)
function dxdt = odefun(t, x, eps, a, V, B, d0, K)
dxdt = ((eps * a * V^2) ./ (2 * B * (d0 – x).^2)) + ((K / B) .* x);
end
References :
– Y. Wang, L. -M. He, Z. Li, W. Xu and J. Ren, "A computationally efficient nonlinear dynamic model for cMUT based on COMSOL and MATLAB/Simulink"
– T. Merrien, A. Boulmé and D. Certon, "Lumped-Parameter Equivalent Circuit Modeling of CMUT Array Elements"
I tried several solver from MATLAB and implemented my own Runge-Kutta algorithm based on the Wikipedia example. I also verified the coefficients according to my references. I am trying to simulate an electronical device that can be modeled by a mass-spring-damper system with an additional non-linear force. The equation at the equilibrium for the system is the following :
The goal of my MATLAB code is to solve this equation for $x$ and find the equilibrium. For this purpose, I use the `ode45` function like this (all coefficient are defined in my code but not shown here) :
x0 = 0;
[t, x] = ode45(@(t, x) odefun(t, x, eps0, a, V0, B, d0, K), tspan, x0);
function dxdt = odefun(t, x, eps, a, V, B, d0, K)
dxdt = ((eps * a * V^2) ./ (2 * B * (d0 – x).^2)) + ((K / B) .* x);
end
The equation is good according to my teachers and several papers but the solver never converges and I can’t see why. All coefficient are defined according to the dimensions of a capacitive micromachined ultrasound transducer. The solution of ode45 is the following :
This is obviously wrong because the dimensions of a cmut are bellow millimeter.
Can you see any mistakes in the way I use the ode45 solver ?
Full code :
% DIMENSIONS DE LA MEMBRANE
e = 500e-9; % epaisseur [m]
r = 20e-6; % rayon [m]
d0 = 550e-9; % epaisseur de cavite [m]
a = pi * r^2; % surface de la membrane [m^2]
% PARAMETRES MECANIQUES
E = 200e9; % module d’Young du SiN [Pa]
nu = 0.25; % coefficient de poisson du SiN
eta = 18.5e-6; % viscosité dynamique de l’air [Pa.s]
p0 = 1e5; % pression exterieure [Pa]
rhoSiN = 3170; % densite du SiN [Kg/m^3]
m = rhoSiN * a * e; % masse membrane [Kg]
K = (16 * E * e^3) / (3 * (1 – nu^2) * r^2); % raideur [N/m]
B = (eta * pi * r^2) / e; % amortissement [N.s/m]
% PARAMETRES ELECTRIQUES
eps0 = 8.85e-12; % permittivité diélectrique du vide [F/m]
V0 = 10; % tension de polarisation [V]
% Conditions initiales et plage de temps
x0 = 0;
ti = 0; tf = 1e-3;
dt = 1e-6;
tspan = linspace(ti, tf, 1/dt);
% Résolution par RK4
[t, x] = ode45(@(t, x) odefun(t, x, eps0, a, V0, B, d0, K), tspan, x0);
plot(t,x,’-‘)
function dxdt = odefun(t, x, eps, a, V, B, d0, K)
dxdt = ((eps * a * V^2) ./ (2 * B * (d0 – x).^2)) + ((K / B) .* x);
end
References :
– Y. Wang, L. -M. He, Z. Li, W. Xu and J. Ren, "A computationally efficient nonlinear dynamic model for cMUT based on COMSOL and MATLAB/Simulink"
– T. Merrien, A. Boulmé and D. Certon, "Lumped-Parameter Equivalent Circuit Modeling of CMUT Array Elements"
I tried several solver from MATLAB and implemented my own Runge-Kutta algorithm based on the Wikipedia example. I also verified the coefficients according to my references. differential equations, physics, matlab, model, ode45, ode MATLAB Answers — New Questions
How to correctly abort a running Matlab function/thread in a .NET Assembly in .NET8?
Dear MathWorks team
In our .NET Framework 4.7.2 application we instanciate a class from a Matlab .NET assembly and call one function to calculate.
The call is done in a own background thread of the application.
If the calculation takes too long or the calculation request has been canceled, we just aborted the .NET thread with thread.Abort().
.NET core (at least v8) does not support the Abort() function anymore. The only general alternative suggestion I could find is to outsource the call into an own process and then kill the process if required. This seems to be quite complicated and Matlab takes a few seconds to initialize on the first call, which is too slow for us.
Is there any best practice / example how to achieve this?
Thanks in advance
Public Sub New(ByVal theTimeout As Integer, theData As Object, theWorkerMethod As TheWorkerDelegate, theCompleteCallback As CompleteCallbackDelegate, theSynchObj As ISynchronizeInvoke)
_theData = theData
_theCompleteCallback = theCompleteCallback
_theWorkerMethod = theWorkerMethod
_theSyncObject = theSynchObj
If theTimeout > 0 Then
_timeoutTimer = New WcsTimer(theTimeout * 1000, WcsTimer.TimerMode.SingleShot)
AddHandler _timeoutTimer.TimeElapsed, AddressOf _timeoutTimerElapsed
End If
_theThread = New Threading.Thread(AddressOf _startWorkerThread)
_theThread.Name = "Matlab Supervision Thread"
_theThread.Start()
End Sub
Public Sub Abort()
_stopTimeoutTimer()
If _theThread IsNot Nothing AndAlso _theThread.IsAlive Then
_theThread.Abort()
End If
_theThread = Nothing
End Sub
Private Sub _startWorkerThread()
Dim theEx As Exception = Nothing
Try
_theWorkerMethod(_theData) -> Matlab call
Catch ex As Exception
theEx = ex
End Try
_stopTimeoutTimer()
Dim p As Object() = {_theData, False, theEx}
_theSyncObject.Invoke(_theCompleteCallback, p)
End Sub
Private Sub _timeoutTimerElapsed(sender As Object, e As EventArgs)
WcsTrace.Log(WcsTrace.Category.Detailed, $"********* BackgroupWorker Timeoute -> Abort **************")
Abort()
_theCompleteCallback(_theData, True, Nothing)
End SubDear MathWorks team
In our .NET Framework 4.7.2 application we instanciate a class from a Matlab .NET assembly and call one function to calculate.
The call is done in a own background thread of the application.
If the calculation takes too long or the calculation request has been canceled, we just aborted the .NET thread with thread.Abort().
.NET core (at least v8) does not support the Abort() function anymore. The only general alternative suggestion I could find is to outsource the call into an own process and then kill the process if required. This seems to be quite complicated and Matlab takes a few seconds to initialize on the first call, which is too slow for us.
Is there any best practice / example how to achieve this?
Thanks in advance
Public Sub New(ByVal theTimeout As Integer, theData As Object, theWorkerMethod As TheWorkerDelegate, theCompleteCallback As CompleteCallbackDelegate, theSynchObj As ISynchronizeInvoke)
_theData = theData
_theCompleteCallback = theCompleteCallback
_theWorkerMethod = theWorkerMethod
_theSyncObject = theSynchObj
If theTimeout > 0 Then
_timeoutTimer = New WcsTimer(theTimeout * 1000, WcsTimer.TimerMode.SingleShot)
AddHandler _timeoutTimer.TimeElapsed, AddressOf _timeoutTimerElapsed
End If
_theThread = New Threading.Thread(AddressOf _startWorkerThread)
_theThread.Name = "Matlab Supervision Thread"
_theThread.Start()
End Sub
Public Sub Abort()
_stopTimeoutTimer()
If _theThread IsNot Nothing AndAlso _theThread.IsAlive Then
_theThread.Abort()
End If
_theThread = Nothing
End Sub
Private Sub _startWorkerThread()
Dim theEx As Exception = Nothing
Try
_theWorkerMethod(_theData) -> Matlab call
Catch ex As Exception
theEx = ex
End Try
_stopTimeoutTimer()
Dim p As Object() = {_theData, False, theEx}
_theSyncObject.Invoke(_theCompleteCallback, p)
End Sub
Private Sub _timeoutTimerElapsed(sender As Object, e As EventArgs)
WcsTrace.Log(WcsTrace.Category.Detailed, $"********* BackgroupWorker Timeoute -> Abort **************")
Abort()
_theCompleteCallback(_theData, True, Nothing)
End Sub Dear MathWorks team
In our .NET Framework 4.7.2 application we instanciate a class from a Matlab .NET assembly and call one function to calculate.
The call is done in a own background thread of the application.
If the calculation takes too long or the calculation request has been canceled, we just aborted the .NET thread with thread.Abort().
.NET core (at least v8) does not support the Abort() function anymore. The only general alternative suggestion I could find is to outsource the call into an own process and then kill the process if required. This seems to be quite complicated and Matlab takes a few seconds to initialize on the first call, which is too slow for us.
Is there any best practice / example how to achieve this?
Thanks in advance
Public Sub New(ByVal theTimeout As Integer, theData As Object, theWorkerMethod As TheWorkerDelegate, theCompleteCallback As CompleteCallbackDelegate, theSynchObj As ISynchronizeInvoke)
_theData = theData
_theCompleteCallback = theCompleteCallback
_theWorkerMethod = theWorkerMethod
_theSyncObject = theSynchObj
If theTimeout > 0 Then
_timeoutTimer = New WcsTimer(theTimeout * 1000, WcsTimer.TimerMode.SingleShot)
AddHandler _timeoutTimer.TimeElapsed, AddressOf _timeoutTimerElapsed
End If
_theThread = New Threading.Thread(AddressOf _startWorkerThread)
_theThread.Name = "Matlab Supervision Thread"
_theThread.Start()
End Sub
Public Sub Abort()
_stopTimeoutTimer()
If _theThread IsNot Nothing AndAlso _theThread.IsAlive Then
_theThread.Abort()
End If
_theThread = Nothing
End Sub
Private Sub _startWorkerThread()
Dim theEx As Exception = Nothing
Try
_theWorkerMethod(_theData) -> Matlab call
Catch ex As Exception
theEx = ex
End Try
_stopTimeoutTimer()
Dim p As Object() = {_theData, False, theEx}
_theSyncObject.Invoke(_theCompleteCallback, p)
End Sub
Private Sub _timeoutTimerElapsed(sender As Object, e As EventArgs)
WcsTrace.Log(WcsTrace.Category.Detailed, $"********* BackgroupWorker Timeoute -> Abort **************")
Abort()
_theCompleteCallback(_theData, True, Nothing)
End Sub .net8, thread, abort MATLAB Answers — New Questions
HDL Coder; Matlab Function Blocks and Clocked Processes
I belive my request is quite straigth forward.
I want the logic of my Matlab Funciton Block to be generated as clocked logic and not combinatorial logic.
Lets use the eml_hdl_incrementer example.
It generates
eml_inc_blk_1_output : PROCESS (ctr_preset, ctr_preset_val_unsigned, current_count)
But I would want it to generate
eml_inc_blk_1_output : PROCESS (clk)
I mean it should not be a miracle to achieve, but I could not find an option, that allows me to enforce this behaviour.
Is there an option to make matlab generate a clocked process or do I have to use specific patterns in my funciton?I belive my request is quite straigth forward.
I want the logic of my Matlab Funciton Block to be generated as clocked logic and not combinatorial logic.
Lets use the eml_hdl_incrementer example.
It generates
eml_inc_blk_1_output : PROCESS (ctr_preset, ctr_preset_val_unsigned, current_count)
But I would want it to generate
eml_inc_blk_1_output : PROCESS (clk)
I mean it should not be a miracle to achieve, but I could not find an option, that allows me to enforce this behaviour.
Is there an option to make matlab generate a clocked process or do I have to use specific patterns in my funciton? I belive my request is quite straigth forward.
I want the logic of my Matlab Funciton Block to be generated as clocked logic and not combinatorial logic.
Lets use the eml_hdl_incrementer example.
It generates
eml_inc_blk_1_output : PROCESS (ctr_preset, ctr_preset_val_unsigned, current_count)
But I would want it to generate
eml_inc_blk_1_output : PROCESS (clk)
I mean it should not be a miracle to achieve, but I could not find an option, that allows me to enforce this behaviour.
Is there an option to make matlab generate a clocked process or do I have to use specific patterns in my funciton? hdl coder, clocked process MATLAB Answers — New Questions
how to plot from .csv file?
i hv to find percentage difference values for columns 3,6,9,12 for 2 pol HH and VV separatelyand find out difference at each frequency
later at x axis frequency and y axis these indivdual percentage values has to be plotted in bar chart
A=csvread(‘baseline.csv’,1,0)
freq=A(1:6,2);
A_1_PE_HH=A(1:6,3);
B_2_PE_HH=A(1:6,9);
A_1_fre_HH=A(1:6,6);
B_2_fre_HH=A(1:6,12);
HH_1st_example=((A_1_PE_HH(2,1)-B_2_PE_HH(2,1))/A_1_PE_HH(2,1))*100 %finding percentage differnce between 3 and 6 columns
HH_2nd_example=((A_1_PE_HH(2,1)-B_2_fre_HH(2,1))A_1_PE_HH(2,1))*100 %finding percentage differnce between 3 and 12 columns
HH_3rd_example=((A_1_fre_HH(2,1)-B_2_PE_HH(2,1))/A_1_fre_HH(2,1))*100 %finding percentage differnce between 6 and 9 columns
HH_4th_example=((A_1_fre_HH(2,1)-B_2_fre_HH(2,1))/A_1_PE_HH(2,1))*100 %finding percentage differnce between 6 and 12 columns
same i have to find for VV also i m getting stuck at this placei hv to find percentage difference values for columns 3,6,9,12 for 2 pol HH and VV separatelyand find out difference at each frequency
later at x axis frequency and y axis these indivdual percentage values has to be plotted in bar chart
A=csvread(‘baseline.csv’,1,0)
freq=A(1:6,2);
A_1_PE_HH=A(1:6,3);
B_2_PE_HH=A(1:6,9);
A_1_fre_HH=A(1:6,6);
B_2_fre_HH=A(1:6,12);
HH_1st_example=((A_1_PE_HH(2,1)-B_2_PE_HH(2,1))/A_1_PE_HH(2,1))*100 %finding percentage differnce between 3 and 6 columns
HH_2nd_example=((A_1_PE_HH(2,1)-B_2_fre_HH(2,1))A_1_PE_HH(2,1))*100 %finding percentage differnce between 3 and 12 columns
HH_3rd_example=((A_1_fre_HH(2,1)-B_2_PE_HH(2,1))/A_1_fre_HH(2,1))*100 %finding percentage differnce between 6 and 9 columns
HH_4th_example=((A_1_fre_HH(2,1)-B_2_fre_HH(2,1))/A_1_PE_HH(2,1))*100 %finding percentage differnce between 6 and 12 columns
same i have to find for VV also i m getting stuck at this place i hv to find percentage difference values for columns 3,6,9,12 for 2 pol HH and VV separatelyand find out difference at each frequency
later at x axis frequency and y axis these indivdual percentage values has to be plotted in bar chart
A=csvread(‘baseline.csv’,1,0)
freq=A(1:6,2);
A_1_PE_HH=A(1:6,3);
B_2_PE_HH=A(1:6,9);
A_1_fre_HH=A(1:6,6);
B_2_fre_HH=A(1:6,12);
HH_1st_example=((A_1_PE_HH(2,1)-B_2_PE_HH(2,1))/A_1_PE_HH(2,1))*100 %finding percentage differnce between 3 and 6 columns
HH_2nd_example=((A_1_PE_HH(2,1)-B_2_fre_HH(2,1))A_1_PE_HH(2,1))*100 %finding percentage differnce between 3 and 12 columns
HH_3rd_example=((A_1_fre_HH(2,1)-B_2_PE_HH(2,1))/A_1_fre_HH(2,1))*100 %finding percentage differnce between 6 and 9 columns
HH_4th_example=((A_1_fre_HH(2,1)-B_2_fre_HH(2,1))/A_1_PE_HH(2,1))*100 %finding percentage differnce between 6 and 12 columns
same i have to find for VV also i m getting stuck at this place plot MATLAB Answers — New Questions
Combining the 2 kinds of array (CSE) formulae in Excel
0th question: If I write “=VLOOKUP($B3;Items;J$71)+…+VLOOKUP($G3;Items;J$71)” it does the correct thing. As I understand the single value CSE is supposed to work in these cases as
“{=VLOOKUP($B3:$G3;Items;J$71)}” (obviously entered without curly brackets and pressing ctrl+shift+enter. For me, it only works in the singular case when the value is found in the first row by vlookup. What am I doing wrong?
The question itself, after the 0th case works:
Say, the result of the previous goes into the cell J1. Now I would like the same for J1:V1 with the last argument of vlookup being J71:V71 respectively. If I select the whole J1:V1 interval and enter {=VLOOKUP($B3:$G3;Items;J$71:V$71)} the same way it should work right? Unfortunately neither works for me and I am kinda lost.
ps: ignore or correct $ marks, I think they are not the source of my problem.
ps2: Items is a table I made and excel likes to move around the columns, which I hardly ever want, so I tried with absolute range reference instead as well. Didn’t solve the problem. (1: how to do absolute reference with a table?)
I dare ask a completely unrelated side question, for which the answer I couldn’t manage to find anywhere. Found similar issues with suggestions, none solved mine. (2: A lot of functions seem to be missing for me. One day they were there and I could use them, and the other day Excel just decided that those functions simply do not exist. Previously written formulae didn’t work, it wouldn’t suggest the formula as auto complete and so on. XLOOKUP and FILTER were 2 I noticed, but I am convinced they are not the only ones)
Thank you very much for your answer! Extra special thanks if I can get answers for my 2 extra questions, too! Have a nice day!
0th question: If I write “=VLOOKUP($B3;Items;J$71)+…+VLOOKUP($G3;Items;J$71)” it does the correct thing. As I understand the single value CSE is supposed to work in these cases as”{=VLOOKUP($B3:$G3;Items;J$71)}” (obviously entered without curly brackets and pressing ctrl+shift+enter. For me, it only works in the singular case when the value is found in the first row by vlookup. What am I doing wrong? The question itself, after the 0th case works:Say, the result of the previous goes into the cell J1. Now I would like the same for J1:V1 with the last argument of vlookup being J71:V71 respectively. If I select the whole J1:V1 interval and enter {=VLOOKUP($B3:$G3;Items;J$71:V$71)} the same way it should work right? Unfortunately neither works for me and I am kinda lost. ps: ignore or correct $ marks, I think they are not the source of my problem.ps2: Items is a table I made and excel likes to move around the columns, which I hardly ever want, so I tried with absolute range reference instead as well. Didn’t solve the problem. (1: how to do absolute reference with a table?) I dare ask a completely unrelated side question, for which the answer I couldn’t manage to find anywhere. Found similar issues with suggestions, none solved mine. (2: A lot of functions seem to be missing for me. One day they were there and I could use them, and the other day Excel just decided that those functions simply do not exist. Previously written formulae didn’t work, it wouldn’t suggest the formula as auto complete and so on. XLOOKUP and FILTER were 2 I noticed, but I am convinced they are not the only ones) Thank you very much for your answer! Extra special thanks if I can get answers for my 2 extra questions, too! Have a nice day! Read More
How to set specific dates for appointment bookings?
Hi!
First of all need to get it off my chest – booking tool is extremely user UNFRIENDLY! It is such a great help when its working, but the process of setting it up is a pure nightmare. Pfu…
Anyhow, I have made 2 bookings in the past, using my “step list”. However, this time its not working and I am in despair…
I do not understand why it opens the whole calendar for booking, when I specifically chose dates that I want people to book appointment with me???
Both on “booking page” and under “edit services” I have chosen specific “availability during these dates”. However, it still shows options to make appointments prior to selected dates as well as after.
Please help!
Thank you!
Hi! First of all need to get it off my chest – booking tool is extremely user UNFRIENDLY! It is such a great help when its working, but the process of setting it up is a pure nightmare. Pfu… Anyhow, I have made 2 bookings in the past, using my “step list”. However, this time its not working and I am in despair… I do not understand why it opens the whole calendar for booking, when I specifically chose dates that I want people to book appointment with me??? Both on “booking page” and under “edit services” I have chosen specific “availability during these dates”. However, it still shows options to make appointments prior to selected dates as well as after. Please help! Thank you! Read More
Creating Workflows is disabled in somewhere
Hi,
I was following this guide: Prerequisites to get the the predefined workflow (Post to a channel when a webhook request is received) done. In the next section in step seven instead of having positive sign, I do have a message “Looks like this workflow is disabled by your organization“.
Anybody knows what other prerequisites the Workflow could have? What else I should have enabled? Somewhere I saw info that I should have “Entra ID Logic Apps” enabled, but that is not so clearly stated in anywhere. Something should be enabled (or not locked) in Power Apps site?
Hi, I was following this guide: Prerequisites to get the the predefined workflow (Post to a channel when a webhook request is received) done. In the next section in step seven instead of having positive sign, I do have a message “Looks like this workflow is disabled by your organization”. Anybody knows what other prerequisites the Workflow could have? What else I should have enabled? Somewhere I saw info that I should have “Entra ID Logic Apps” enabled, but that is not so clearly stated in anywhere. Something should be enabled (or not locked) in Power Apps site? Read More
Email deleted,
I was reading my Email and suddenly it started to delete itself. and it deleted all of it. Why?
It also deleted the Windows Live 11Mail account.
I was reading my Email and suddenly it started to delete itself. and it deleted all of it. Why? It also deleted the Windows Live 11Mail account. Read More
Differences between Pre-Training and Supervised Fine-Tuning (SFT)
When we talk about the differences between Pre-Training and Supervised Fine-Tuning (SFT), the goals, datasets used, and number of GPUs required are all different. However, if we are to explain the difference from the essence of deep learning training, it is:
Pre-training involves randomly initializing model parameters, constructing the model, and then training it on a large amount of unlabeled data to learn general features of the corpus; whereas fine-tuning loads parameters from the pre-trained model, retains the general features learned during pre-training, and trains the model on a small amount of high-quality labeled data to enhance the model’s capability and performance on specific tasks.
The parameters mentioned above include: weights, biases, Word Embeddings, Positional Encoding, attention mechanism parameters, etc.
More detail explanations
Pre-Training aims to learn the fundamental structure and semantic features of a language using large-scale unsupervised datasets (such as text corpora). Pre-training typically involves the following steps:
Random Initialization of Weights: The model’s parameters, such as weights and biases, are randomly initialized at the start of pre-training.
Large-Scale Dataset: Training is conducted using a vast amount of unsupervised data.
Learning General Features: The model learns the general features of the language by optimizing a loss function (e.g., the cross-entropy loss of a language model).
Key Points of Pre-Training
Random Initialization: All model parameters (weights, biases, etc.) are random at the beginning of pre-training.
Large-Scale Data: Training is done using a large-scale unsupervised dataset.
General Features: The model learns the basic structure and semantic features of the language, providing a good starting point for subsequent tasks.
Fine-Tuning aims to optimize the model’s performance on a specific task using a task-specific dataset. Fine-tuning typically involves the following steps:
Loading Pre-Trained Weights: The model’s weights and biases are loaded from the pre-trained model.
Task-Specific Data: Training is conducted using a dataset specific to the task.
Optimizing Task Performance: The model adjusts its parameters by optimizing a loss function to improve performance on the specific task.
Key Points of Fine-Tuning
Loading Pre-Trained Weights: The model’s parameters are loaded from the pre-trained model, retaining the general features learned during pre-training.
Task-Specific Data: Training is done using a dataset specific to the task.
Task Optimization: The model’s parameters are further adjusted to optimize performance on the specific task.
Summary
Training Efficiency: Pre-training usually requires substantial computational resources and time because it involves training all model parameters on a large-scale dataset. Fine-tuning is relatively efficient as it builds on the pre-trained model and only requires further optimization on task-specific data.
Model Performance: The pre-trained model has already learned general language features, allowing fine-tuning to converge faster and perform better on specific tasks. Training a task-specific model from random initialization typically requires more data and time, and its performance may not match that of the pre-training + fine-tuning approach.
Application Scenarios: Pre-trained models can serve as general-purpose base models suitable for various downstream tasks. Fine-tuning allows for quick adaptation to different task requirements without the need to train a model from scratch.
Pre-training Code Demonstration
Taking GPT-2 as an Example
https://huggingface.co/docs/transformers/v4.44.0/en/model_doc/gpt2#transformers.GPT2LMHeadModel
To pre-train GPT-2, we need to use the classes GPT2LMHeadModel and GPT2Config:
config = GPT2Config()
model = GPT2LMHeadModel(config)
tokenizer = GPT2Tokenizer.from_pretrained(“gpt2”)
tokenizer.pad_token = tokenizer.eos_token
dataset = load_dataset(“wikitext”, “wikitext-2-raw-v1”)
def tokenize_function(examples):
return tokenizer(examples[“text”], truncation=True, padding=”max_length”, max_length=512, return_special_tokens_mask=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=[“text”])
print(“Train dataset size:”, len(tokenized_datasets[“train”]))
print(“Validation dataset size:”, len(tokenized_datasets[“validation”]))
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
training_args = TrainingArguments(
output_dir=”./results”,
overwrite_output_dir=True,
num_train_epochs=5,
per_device_train_batch_size=64,
save_steps=10_000,
save_total_limit=2,
remove_unused_columns=False,
report_to=[],
learning_rate=5e-4
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=tokenized_datasets[“train”],
eval_dataset=tokenized_datasets[“validation”]
)
if torch.cuda.is_available():
model.cuda()
trainer.train()
Since the model is small, pre-training can be done with a single H100 GPU.
Training result is as following:
Step
Training Loss
500
6.505700
1000
5.657100
1500
5.269900
2000
4.972000
2500
4.725000
The trained model can be used for inference validation.
model = GPT2LMHeadModel.from_pretrained(“./results/checkpoint-2870”)
tokenizer = GPT2Tokenizer.from_pretrained(“gpt2”)
tokenizer.pad_token = tokenizer.eos_token
device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)
model.to(device)
model.eval()
input_text = “Once upon a time”
inputs = tokenizer(input_text, return_tensors=”pt”, padding=True).to(device)
with torch.no_grad():
outputs = model.generate(
inputs.input_ids,
attention_mask=inputs.attention_mask,
max_length=100,
num_return_sequences=1,
no_repeat_ngram_size=2,
early_stopping=True,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
Inference result is as following:
Once upon a time of the earthquake, the local community of local and a new new government, a military government who had begun with the ” the most prominent “.
Fine-tuning Code Demonstration
When we fine-tune a model, it usually refers to Supervised Fine Tuning (SFT). SFT can be divided into Parameter-Efficient Fine-Tuning (PEFT) and Full Fine Tuning.In PEFT implementations, methods like LoRA, QLoRA, and GA-LoRA are quite popular.
Let’s first look at how to load a model for Full Fine Tuning. We use the AutoModelForCausalLM.from_pretrained class, which retrieves the parameters of the pre-trained model.
model = AutoModelForCausalLM.from_pretrained(
model_name, attn_implementation=attn_implementation, device_map={“”: 0}
)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={‘use_reentrant’:True})
For the complete Full fine tuning code, refer to the repository:
https://github.com/davidsajare/david-share/tree/master/Deep-Learning/SmolLM-Full-Fine-Tuning
Next, let’s look at the differences in code implementation for fine-tuning, LoRA, and QLoRA. In terms of loading models and training parameters, Full Fine-Tuning, LoRA, and QLoRA have the following differences:
Difference in Loading Models
Full Fine-Tuning
Directly load the complete model for training.
Use AutoModelForCausalLM.from_pretrained to load the model.
LoRA
Load the model and then use LoRA configuration for parameter-efficient fine-tuning.
Use LoraConfig from the peft library to configure LoRA parameters.
Target modules are usually specific projection layers, such as k_proj, q_proj, etc.
QLoRA
Based on LoRA, it combines quantization techniques (e.g., 4-bit quantization) to reduce memory usage.
Use BitsAndBytesConfig for quantization configuration.
Call prepare_model_for_kbit_training to prepare the model.
Difference in Training Parameters
Full Fine-Tuning
Train all model parameters.
Typically requires more memory and computational resources.
Use standard optimizers like adamw_torch.
LoRA
Only train the low-rank matrices inserted by LoRA, keeping other parameters unchanged.
Faster training speed and less memory usage.
Use optimizers like paged_adamw_8bit.
QLoRA
Combine LoRA and quantization techniques to further reduce memory usage.
Suitable for fine-tuning large models in resource-constrained environments.
Also use the paged_adamw_8bit optimizer.
It should be noted that when performing LoRA or QLoRA fine-tuning, we can specify the modules to be trained, such as:
model = FastLanguageModel.get_peft_model(
model,
r = 128,
target_modules = [“q_proj”, “k_proj”, “v_proj”, “o_proj”,
“gate_proj”, “up_proj”, “down_proj”,
“embed_tokens”, “lm_head”,], # Add for continual pretraining
lora_alpha = 32,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = “none”, # Supports any, but = “none” is optimized
use_gradient_checkpointing = “unsloth”, # True or “unsloth” for very long context
random_state = 3407,
use_rslora = True,
)
For detailed information, refer to:
https://github.com/davidsajare/david-share/tree/master/Deep-Learning/Continue-Pre-training
Distributed Implementation of Training
There is no doubt that pre-training large language models requires multi-node and multi-GPU setups. This necessitates distributed training. Currently, the underlying distributed pre-training can be implemented by calling NCCL. Higher-level tools such as Megatron, DeepSpeed, and HF’s accelerate library (which currently supports FSDP) can be used. These tools effectively implement DP/PP/TP.
Megatron-DeepSpeed
For detailed information on pre-training using Megatron combined with DeepSpeed, refer to:
DeepSpeed
For an example of SFT implementation using DeepSpeed, refer to:
Axolotl
Currently, some open-source fine-tuning tools like Axolotl can also directly interface with DeepSpeed. For an example, refer to:
https://github.com/davidsajare/david-share/tree/master/Deep-Learning/Fine-tuning-with-Axolotl
Accelerate
When using FSDP with accelerate, other parallel strategies can be combined to achieve more efficient training.
Data Parallelism (DP)
FSDP itself is a data parallel strategy, achieved by sharding model parameters.
Pipeline Parallelism (PP)
The model can be divided into multiple stages, with each stage running on different devices. This requires manual partitioning of the model and managing the data flow.
Tensor Parallelism (TP)
The computation of a single layer is distributed across multiple devices. This requires modifications to the model’s computation graph.
Combining these strategies usually requires significant customization and adjustments to the model and training scripts. accelerate provides some tools to simplify these processes, but specific implementations may require combining other PyTorch libraries (such as torch.distributed) and custom code.
For an example of FSDP with accelerate, refer to:
https://github.com/davidsajare/david-share/tree/master/Deep-Learning/Llama-3.1-70B-FSDP-Fine-Tuning
My github: https://github.com/davidsajare/david-share.git
Microsoft Tech Community – Latest Blogs –Read More
Transforming Optimization Code from Problem-Based to Solver-Based Approach in MATLAB
Hello,
I have successfully implemented an ILP problem using MATLAB’s problem-based optimization approach. However, I am now looking to switch to the solver-based approach to take advantage of its flexibility and efficiency.
In the problem-based approach, I defined binary decision variables, constraints, and an objective function using optimvar, optimconstr, and optimexpr. The code works well, but I need guidance on how to transform this code into a solver-based format using intlinprog.
Here’s a summary of what I have:
Decision Variables:
A 3D binary matrix A(N, numNodes, num_vehicles) for task assignment.
A binary vector chi(num_vehicles) for satisfaction.
Other binary variables like z, t_wait_aux, etc.
Constraints:
Assignment constraints, dependency constraints, and time-based constraints.
I linearized expressions using auxiliary variables and max constraints.
Objective Function:
The objective is to maximize the satisfaction rate, expressed as a linear function of chi.
Could you provide guidance or directions how to systematically convert my existing problem-based variables and constraints into the matrix form required by intlinprog
Here is the code
the problem is attached.
Thank you for your continued help!Hello,
I have successfully implemented an ILP problem using MATLAB’s problem-based optimization approach. However, I am now looking to switch to the solver-based approach to take advantage of its flexibility and efficiency.
In the problem-based approach, I defined binary decision variables, constraints, and an objective function using optimvar, optimconstr, and optimexpr. The code works well, but I need guidance on how to transform this code into a solver-based format using intlinprog.
Here’s a summary of what I have:
Decision Variables:
A 3D binary matrix A(N, numNodes, num_vehicles) for task assignment.
A binary vector chi(num_vehicles) for satisfaction.
Other binary variables like z, t_wait_aux, etc.
Constraints:
Assignment constraints, dependency constraints, and time-based constraints.
I linearized expressions using auxiliary variables and max constraints.
Objective Function:
The objective is to maximize the satisfaction rate, expressed as a linear function of chi.
Could you provide guidance or directions how to systematically convert my existing problem-based variables and constraints into the matrix form required by intlinprog
Here is the code
the problem is attached.
Thank you for your continued help! Hello,
I have successfully implemented an ILP problem using MATLAB’s problem-based optimization approach. However, I am now looking to switch to the solver-based approach to take advantage of its flexibility and efficiency.
In the problem-based approach, I defined binary decision variables, constraints, and an objective function using optimvar, optimconstr, and optimexpr. The code works well, but I need guidance on how to transform this code into a solver-based format using intlinprog.
Here’s a summary of what I have:
Decision Variables:
A 3D binary matrix A(N, numNodes, num_vehicles) for task assignment.
A binary vector chi(num_vehicles) for satisfaction.
Other binary variables like z, t_wait_aux, etc.
Constraints:
Assignment constraints, dependency constraints, and time-based constraints.
I linearized expressions using auxiliary variables and max constraints.
Objective Function:
The objective is to maximize the satisfaction rate, expressed as a linear function of chi.
Could you provide guidance or directions how to systematically convert my existing problem-based variables and constraints into the matrix form required by intlinprog
Here is the code
the problem is attached.
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Hi All,
I hope someone can help me fix this issue on the Viv Engage web part on the SharePoint page. Discussions and posts appear fine, but questions are empty on the SharePoint page. However, when I change the layout to “feed”, it works and shows the questions, whereas it does not work with the Highlights layout. I appreciate your help in solving this issue.
Hi All, I hope someone can help me fix this issue on the Viv Engage web part on the SharePoint page. Discussions and posts appear fine, but questions are empty on the SharePoint page. However, when I change the layout to “feed”, it works and shows the questions, whereas it does not work with the Highlights layout. I appreciate your help in solving this issue. Read More
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Hi everyone,
i want to try to follow up on this discussion – https://techcommunity.microsoft.com/t5/microsoft-sentinel/get-entities-for-a-sentinel-incidient-by-api/m-p/1422643
We are using the recommended in that post “expansionId” to fetch entities for specific alerts, as per documentation Sentinel Incidents API returns “summed” list of entities for Incidents (all entities from all alerts that are part of the same Incident).
This is the expansion id we use for alert related entities: “98b974fd-cc64-48b8-9bd0-3a209f5b944b”
I wanted to check, are there any updates regarding this”expansionId” option since?
How safe is to still use the expansion ids and alert’s entities is particular?
Also, maybe there is a better way now to fetch entities per each alert in Incident via Sentinel REST API?
Thanks in advance!
Hi everyone, i want to try to follow up on this discussion – https://techcommunity.microsoft.com/t5/microsoft-sentinel/get-entities-for-a-sentinel-incidient-by-api/m-p/1422643We are using the recommended in that post “expansionId” to fetch entities for specific alerts, as per documentation Sentinel Incidents API returns “summed” list of entities for Incidents (all entities from all alerts that are part of the same Incident).This is the expansion id we use for alert related entities: “98b974fd-cc64-48b8-9bd0-3a209f5b944b” I wanted to check, are there any updates regarding this”expansionId” option since?How safe is to still use the expansion ids and alert’s entities is particular? Also, maybe there is a better way now to fetch entities per each alert in Incident via Sentinel REST API? Thanks in advance! Read More