Webinar: Development Swarm Cluster with Docker Compose V3
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Docker 1.13 introduced a new version of Compose that simplifies deployment. In our last webinar, Alexei Ledenev (Cheif Researcher at Codefresh) walked us through the new features in Compose V3 developers can use for deployment. In case you missed it, we recorded it for you to view on demand. During the session, you’ll learn how to quickly create a multi-node Swarm cluster on your laptop, (without needing to install and manage additional VMs).
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Practical Docker for OpenStack - NYC / PHL OpenStack meetup (4-23-2014)
Practical Docker for OpenStack - NYC / PHL OpenStack meetup (4-23-2014)
Devoxx France 2015 - The Docker Orchestration Ecosystem on Azure
Devoxx France 2015 - The Docker Orchestration Ecosystem on Azure
Docker-Hanoi @DKT , Presentation about Docker Ecosystem
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An improved modulation technique suitable for a three level flying capacitor ...
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
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Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
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Comparative analysis between traditional aquaponics and reconstructed aquapon...
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
artificial intelligence and data science contents.pptx
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
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Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Software Engineering and Project Management - Introduction, Modeling Concepts...
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
cnn.pptx Convolutional neural network used for image classication
Convolutional Neural Network used for image classification
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building
Rainfall intensity duration frequency curve statistical analysis and modeling...
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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Webinar: Development Swarm Cluster with Docker Compose V3
- 1. Development Swarm Cluster docker for Mac/Windows docker-compose v3 docker-in-docker
- 2. About me… •Chief of Research @codefresh.io •github.com/alexei-led/pumba •#docker, #golang, #aws •medium.com/@alexeiled •@alexeiled
- 3. Docker 4 Mac/Windows • Easy installation and setup of a complete Docker development environment • Fast and reliable performance with native macOS virtualization (xhyve for OS X) or native Windows Hyper V virtualization (for Windows) running a custom minimal Linux distro (Alpine) • Seamless volume mounting for code and data, including file change notifications that unlock fast edit-test cycles • Excellent network support allows working transparently with VPNs and proxies • Multi-CPU Architecture Support: arm, mips, ppc64le and even s390x (Mac only)
- 4. Docker Swarm Mode by Nigel Poulton http://blog.nigelpoulton.com/docker-launches-kubernetes-killer/
- 5. $ docker stack deploy --compose-file docker-compose.yml myapp
- 6. $ docker stack deploy vs $ docker-compose up • docker client has a built-in docker- compose.yaml v3.x parser • docker client reads deploy section to deploy a service on swarm cluster • docker-compose CLI tool also has a built-in docker-compose.yaml v3.x parser • docker-compose CLI tool ignores deploy section and does not deploy on swarm cluster • docker-compose CLI tool works against single Docker host only Docker Swarm Cluster Single Docker Host
- 7. Compose v3 vs Bash
- 8. Compose v3 `deploy` • mode - global | replicated • replicas • placement - constraints: node.id/hostname/role/labels, engine.labels • update_config - parallelism, delay, failure_action, monitor, max_failure_ratio • resources - cpu_shares, cpu_quota, cpuset, mem_limit, memswap_limit, mem_swappiness • restart_policy - condition, delay, max_attempts, window • labels
- 12. Next Steps • Sign up for a free Codefresh account - www.codefresh.io • Sign up for our newsletter - www.codefresh.io/blog • Check out our upcoming Meetups & Webinars - www.codefresh.io/meetups • Follow us on Twitter - @codefresh