#java ml

[pattem]

A Deep Dive into the Java Weka ML Library

Listing some Pivotal Points of Java Libraries and their Merits 

Java is an object-oriented programming language that can be used as a platform in itself. It is a fast, secure, and reliable programming language used for developing more innovative and interactive applications, from mobile applications and enterprise software to server-side technologies and big-data applications. To make the development more effective and easier, specific tools, libraries, and frameworks are used. By choosing the perfect library for the project, companies can streamline the development process, enhance code efficiency and quality, and also speed up the company’s development lifecycle. These libraries are very essential in the developer’s toolkit, which significantly improves code maintainability and productivity. 

A Java library is a collection of classes that were previously pre-written by somebody. Jackson, Apache, Maven, JUnit, and Google JSON are some of the most widely used Java libraries. Moreover, Java provides a lot of libraries, except for standard libraries. Java ML libraries can also be used to make the process much more effective and easier. Java ML is a collection of data mining and machine learning algorithms that include a common interface for each. In this blog, we will learn some magnificent insights about Java ML libraries and their key features and advantages. So, without wasting any further time, let us jump into the blog!

Peregrinating Java ML Libraries and Their Key Benefits and Advantages 

Generally, WEKA offers a wide range of machine learning and data mining algorithms. These, combined with the user-friendly and adaptable interface, make it an ideal choice for generative AI and academic research rather than traditional applications such as regression, classification, and clustering. Moreover, WEKA, specifically abbreviated as Waikato Environment for Knowledge Analysis, is a very popular and widely used software suite for machine learning and data mining tasks. It also provides a user-friendly graphical interface for numerous algorithms. Furthermore, the graphical user interface of WEKA makes it easy to prototype and test characteristic algorithms. The user-friendly environments enable companies to experiment with visualizing results in different settings and understand the behavior of the algorithm. WEKA offers multiple data filters like normalization, cleansing, and transformation, along with specific tools to explore and build new features before applying machine learning algorithms. 

WEKA provides a booming Java Application Program Interface (API), which is very beneficial in integrating custom algorithms into larger Java-based workflows and applications. Moreover, it also provides tools to evaluate and integrate the algorithm's performance. This capacity is crucial to comparing approaches against some established methods using the regulated evaluation metrics. In addition to that, WEKA possesses a diverse collection of algorithms that cover supervised learning, unsupervised learning, and evaluation metrics. This versatility allows companies to tackle numerous machine learning problems without the help of any particular tool. Being an open-source technology offers customization, transparency, and community support. WEKA runs effortlessly on Windows, MacOS, and Linux, offering great flexibility for multiple computing environments. 

Noting Down some Key features and Key Takeaways from the Blog

WEKA is the perfect technology to be used for academic research and personal projects, as it completely eliminates license costs. Moreover, it can also be used for diverse ML solutions, including regression, classification, dimensionality reduction, clustering, and more. Preferring the best Java development services company also plays an important role in a company’s elevation and growth. 

Pattem Digital is the leading Java development services company that provides its clients with the best feature-rich applications in the industry. We possess a combined team of talented professionals to make their applications more innovative and user-friendly. We also make sure to provide our clients with applications that meet their business needs and user requirements. We also maintain an impeccable track record of customer satisfaction.

[grey-space-computing]

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[briskwinits]

Kickstart Your IT Career, Briskwin IT Solutions Is Hiring Freshers!

For more, visit: https://briskwinit.com/

[qisacdemyoffpage]

Kickstart Your Tech Career: Why Internships Are More Important Than Ever

In the rapidly changing digital economy we live in today, a degree no longer suffices. What truly makes you stand out is practical experience—and that's where internships fit in.

If you are a computer science or IT bachelor's or master's degree holder, applying for a Java internship for freshers can prove to be one of the best decisions you ever took. Java remains a basis of enterprise software, and hence it is extremely important to study Java for those who are interested in working on backend development, application security, or web systems with scalability. Internships provide freshers with hands-on experience in writing optimized code, debugging, version control, and project collaboration.

On the opposite end, the world of technology is also eager for developers who excel at everything. This is why an full stack web development internship is a first preference for future professionals. With these internships, you get exposed to frontend and backend technologies—HTML, CSS, JavaScript, React, Node.js, Express, MongoDB, etc.—and you become a jack-of-all-trades of the world.

But above all, it is not that these internships simply teach you how to code, but how they teach you how to work, manage teams, deadlines, and deployable applications that solve real problems.

From product companies to tech startups or freelance work, the hands-on experience you learn through a concerted internship can define your career path. Theory is fine to learn, but experience is what gets you ready for a job.

[lit-lakshyainstitute]

Top bca,bsc,data science colleges bhubaneswar

Lakshya Institute of Technology, Bhubaneswar offers excellent placement support in leading IT companies for BCA, BSc ITM, BSc CS and BSc Data Science.

[questacademy]

Unlock Your Career Potential with QIS Academy: The Best Software Training Institute in Kochi

QIS Academy, a division of Quest Innovative Solutions, is dedicated to empowering aspiring professionals with industry-relevant skills and knowledge. As the Top Software Training Institute & Placement in Kochi, QIS Academy offers a wide array of courses tailored to the demands of the tech industry. With options for both online and offline classes, expert guidance, and comprehensive placement assistance, we ensure your success in today’s competitive job market.

Courses Offered at QIS Academy

1. Data Science and Machine Learning Course in Kerala

Dive into the world of data with the Best Data Science Course in Kerala. Learn data analysis, machine learning, and AI techniques through hands-on projects and real-world case studies. This course is ideal for those aspiring to become data scientists or AI specialists.

2. Python Full Stack Developer Training in Kochi

Master front-end and back-end development with our Python Full Stack Developer Course in Kochi. Learn Python, Django, React, and database management to become a versatile developer. Recognized as the Best Python Training Course in Kochi, this program offers practical training and placement support to launch your tech career.

3. Full Stack Developer Course in Kochi

Our Full Stack Developer Course in Kochi equips you with the skills needed to build dynamic web applications. From HTML, CSS, and JavaScript to advanced frameworks, this course covers it all. Ideal for those looking to excel in front-end and back-end development.

4. .NET Full Stack Developer Course in Kochi

Learn to build robust and scalable applications with our .NET Full Stack Developer Course in Kochi. This course includes training in C#, ASP.NET, and SQL Server, making it perfect for individuals aiming for careers in enterprise-level software development.

5. Best Python Training in Kochi & Calicut

Our Python training program in Kochi and Calicut is designed for beginners and professionals alike. Known as the Best Python Training in Kochi & Calicut, this course emphasizes practical coding and problem-solving to enhance your programming expertise.

Why Choose QIS Academy?

Industry-Relevant Curriculum: All courses are designed to meet current industry standards, ensuring you’re job-ready upon completion.

Flexible Learning Modes: QIS Academy offers both online and offline classes, providing convenience for students with diverse schedules.

Expert Mentors: Learn from experienced professionals who bring real-world insights to the classroom.

Hands-On Training: Engage in live projects, practical assignments, and case studies to build your expertise.

Placement Assistance: We provide comprehensive placement support to help you secure your dream job in top companies.

Placement Assistance at QIS Academy

Our dedicated placement team ensures you’re equipped with the right skills to succeed in interviews and land high-paying jobs. Many of our students have been placed in top MNCs as full stack developers, Python programmers, .NET specialists, and data scientists.

Learn from the Best in Kerala

Located in Kochi, Kerala, QIS Academy is renowned for delivering the Best Software Training in Kochi. Whether it’s data science, full stack development, or Python programming, we provide unmatched quality and results.

Conclusion

QIS Academy stands out as the Top Software Training Institute & Placement in Kochi, offering a range of industry-relevant courses to help you excel in the competitive tech world. With expert trainers, hands-on projects, and dedicated placement support, we are proud to be recognized as the Best Software Training Institute in Kochi. Join QIS Academy today and take the first step toward building a successful career in technology!

[healthcare-solution]

Java vs Python – Which Language Will Rule the Future?

Both Java and Python have dominated the programming world, but which one will lead the future? Java's robustness and scalability make it a go-to for enterprise solutions, while Python's simplicity and versatility fuel AI, ML, and data science.

With AI on the rise, Python’s popularity is soaring, but Java continues to reign in industries that demand reliability and performance.

Which language do you think will rule the future? Let’s discuss!

[malik5rizwan]

Software Development: Building Tomorrow's World Today

Welcome to the realm where lines of code shape the future, where creativity meets logic, and where innovation knows no bounds. Software development isn't just about crafting programs; it's about sculpting solutions that revolutionize industries, streamline processes, and enrich lives. Whether you're a seasoned developer or just dipping your toes into the digital domain, join me on this journey as we delve into the captivating world of software development.

1. Introduction

So, what exactly is software development? In its simplest form, it's the art and science of creating software programs. From mobile apps to enterprise systems, software development encompasses a vast array of technologies and methodologies aimed at addressing real-world needs. Its importance? Well, imagine a world without software – no smartphones, no social media, no online shopping. It's safe to say we'd be stuck in the Stone Age of technology.

2. The Software Development Process

Now, let's peek behind the curtain and uncover the intricate dance known as the software development lifecycle. Picture it as a well-orchestrated symphony, with each phase harmonizing to produce a masterpiece. We kick things off with requirements gathering, where we sit down with stakeholders to understand their needs and desires. Then comes design, where we sketch out the blueprint of our creation. Next up, is implementation, where lines of code come to life like actors on a stage. But wait, we're not done yet! We must put our creation through its paces with testing, ensuring it behaves as expected. Finally, we roll out the red carpet for deployment, unleashing our creation into the wild, followed by the ongoing saga of maintenance to keep it running smoothly.

3. Different Approaches to Software Development

Ah, but there's more than one way to skin a cat – or in our case, develop software. Enter the diverse array of methodologies, each with its own flavor and flair. The waterfall model takes a sequential approach, flowing steadily from one phase to the next. Meanwhile, agile methodology embraces change like a chameleon, adapting to evolving requirements with grace and agility. And let's not forget the dynamic duo of DevOps, where development and operations join forces to deliver software at warp speed.

4. Essential Skills for Software Developers

Now, let's talk turkey – or rather, skills. What does it take to thrive in the world of software development? Sure, knowing your way around a programming language is essential, but it's only the tip of the iceberg. Picture yourself as a detective, solving mysteries and unraveling puzzles. That's the essence of software development – a blend of technical prowess and Sherlock Holmes-level problem-solving abilities. But hold your horses; communication skills are equally vital. After all, what good is a brilliant solution if you can't explain it to others?

5. Tools and Technologies in Software Development

In the toolbox of a software developer, you'll find an arsenal of gadgets and gizmos, each serving a specific purpose in our quest for digital greatness. Integrated Development Environments (IDEs) provide a cozy sanctuary where we can code, debug, and refactor to our heart's content. Meanwhile, version control systems keep our codebase in check, allowing us to travel back in time with the flick of a virtual switch. And let's not forget about testing frameworks, the unsung heroes who ensure our creations are battle-ready before facing the real world.

6. Common Challenges in Software Development

But alas, not every day is sunshine and rainbows in the world of software development. Like a rollercoaster ride through a maze, we encounter twists, turns, and the occasional loop-de-loop. Project management can feel like herding cats, with deadlines looming overhead like storm clouds on the horizon. And then there's the ever-present specter of budget constraints, threatening to rain on our parade with their penny-pinching ways. But fear not, dear reader, for every challenge is but an opportunity in disguise.

7. Trends in Software Development

Ah, the winds of change are blowing once again, carrying whispers of new technologies and emerging trends. Brace yourselves as we embark on a journey into the future, where artificial intelligence (AI) and machine learning (ML) reign supreme, unlocking new realms of possibility with each passing day. Meanwhile, the Internet of Things (IoT) connects the dots, transforming everyday objects into smart, sentient beings. Looming on the horizon like a gathering storm, cloud computing casts its shadow over the digital landscape, promising scalability and flexibility on a scale never before imagined.

8. Importance of Documentation in Software Development

Now, let's talk paperwork – everyone's favorite pastime, right? But before you roll your eyes and reach for the nearest exit, hear me out. Documentation may not be the sexiest aspect of software development, but it's arguably one of the most important. From user manuals to technical specifications, documentation serves as a roadmap for future generations, guiding them through the labyrinth of code like a beacon in the night.

9. Quality Assurance in Software Development

In the realm of software development, quality is king – or should I say, queen? Regardless of royal titles, one thing's for sure: quality assurance is essential for ensuring our creations meet the lofty standards set forth by our discerning clientele. From unit tests to user acceptance testing, each method serves as a guardian at the gate, warding off bugs and gremlins with a flick of their metaphorical swords.

10. Ethics and Security in Software Development

But with great power comes great responsibility

[canmom]

you read ML research (e.g. arxiv, state of ai, various summaries), you find an overwhelming blizzard of new techniques, clever new applications and combinations of existing techniques, new benchmarks to refine this or that limitation, relentless jumps in capabilities that seem unstoppable (e.g. AI video generation took off way faster than I ever anticipated). at some point you start to see how Károly Zsolnai-Fehér became such a parody of himself!

you read ed zitron & similar writers and you hear about an incomprehensibly unprofitable industry, an obscene last-gasp con from a cancerous, self-cannibalising tech sector that seems poised to take the rest of the system down with it once the investors realise nobody actually cares to pay for AI anything like what it costs to run. and you think, while perhaps he presents the most negative possible read on what the models are capable of, it's hard to disagree with his analysis of the economics.

you read lesswrong & cousins, and everyone's talking about shoggoths wearing masks and the proper interpretation of next-token-prediction as they probe the LLMs for deceptive behaviour with an atmosphere of paranoid but fascinated fervour. or else compile poetic writing with a mystic air as they celebrate a new form of linguistic life. and sooner or later someone will casually say something really offputting about eugenics. they have fiercely latched onto playing with the new AI models, and some users seem to have better models than most of how they do what they do. but their whole deal from day 1 was conjuring wild fantasies about AI gods taking over the world (written in Java of course) and telling you how rational they are for worrying about this. so... y'know.

you talk to an actual LLM and it produces a surprisingly sharp, playful and erudite conversation about philosophy of mind and an equally surprising ability to carry out specific programming tasks and pull up deep cuts, but you have to be constantly on guard against the inherent tendency to bullshit, to keep in mind what the LLM can't do and learn how to elicit the type of response you want and clean up its output. is it worth the trouble? what costs should be borne to see such a brilliant toy, an art piece that mirrors a slice of the human mind?

you think about the news from a few months ago where israel claimed to be using an AI model to select palestinians in gaza to kill with missiles and drones. an obscene form of statswashing, but they'd probably kill about the same number of people, equally at random, regardless. probably more of that to come. the joke of all the 'constitutional AI', 'helpful harmless assistant' stuff is that the same techniques would work equally well to make the model be anything you want. that twat elon musk already made a racist LLM.

one day the present AI summer and corresponding panics will burn out, and all this noise will cohere into a clear picture of what these new ML techniques are actually good for and what they aren't. we'll have a pile of trained models, probably some work on making them smaller and more efficient to run, and our culture will have absorbed their existence and figured out a suitable set of narratives and habits around using them in this or that context. but i'm damned if I know how it will look by then, and what we'll be left with after the bubble.

if i'm gonna spend all this time reading shit on my computer i should get back to umineko lmao

[web-scraping-tutorial-blog]

Top 5 Programming Languages to Master in 2025

Programming language theory is the subfield of computer science that studies the design, implementation, analysis, characterization, and classification of programming languages.

1. Java

You might ask, “Is Java obsolete?” Of course not.

Why is Java still popular? Java is one of the oldest and most robust programming languages. It is also an object-oriented language mainly used for Android application development. This is one of the main reasons it is still used today. However, with the advent of programming languages ​​like Kotlin (also suitable for Android development), Java is becoming less popular.

2. Swift

3. SQL

4. JavaScript

5. Python

The amazing thing about Python is that it’s a general-purpose programming language used to build a wide range of applications. Furthermore, it is active in artificial intelligence. Self-driving cars, Wal-Mart auto-payment, and many automation and machine learning (ML) apps were developed through Python. This makes this language more important and rapidly popularizes. In addition, Python is easier to learn than all other languages ​​and is easy for beginners. You can also build complex applications relatively easily and quickly. In the United States, the average salary for Python developers is about $ 78,000, while experienced developers can be as high as $ 122,000.

[grey-space-computing]

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[redarmyscreaming]

Curtiss Hawks 75A-7 of the ML KNIL (Dutch colonial air force of the colonial armed forces) on Andir near Bandung on Java may 1940

[aionlinemoney]

The Role of Machine Learning Engineer: Combining Technology and Artificial Intelligence

Artificial intelligence has transformed our daily lives in a greater way than we can’t imagine over the past year, Impacting how we work, communicate, and solve problems. Today, Artificial intelligence furiously drives the world in all sectors from daily life to the healthcare industry. In this blog we will learn how machine learning engineer build systems that learn from data and get better over time, playing a huge part in the development of artificial intelligence (AI). Artificial intelligence is an important field, making it more innovative in every industry. In the blog, we will look career in Machine learning in the field of engineering.

What is Machine Learning Engineering?

Machine Learning engineer is a specialist who designs and builds AI models to make complex challenges easy. The role in this field merges data science and software engineering making both fields important in this field. The main role of a Machine learning engineer is to build and design software that can automate AI models. The demand for this field has grown in recent years. As Artificial intelligence is a driving force in our daily needs, it become important to run the AI in a clear and automated way.

A machine learning engineer creates systems that help computers to learn and make decisions, similar to human tasks like recognizing voices, identifying images, or predicting results. Not similar to regular programming, which follows strict rules, machine learning focuses on teaching computers to find patterns in data and improve their predictions over time.

Responsibility of a Machine Learning Engineer:

Collecting and Preparing Data

Machine learning needs a lot of data to work well. These engineers spend a lot of time finding and organizing data. That means looking for useful data sources and fixing any missing information. Good data preparation is essential because it sets the foundation for building successful models.

Building and Training Models

The main task of Machine learning engineer is creating models that learn from data. Using tools like TensorFlow, PyTorch, and many more, they build proper algorithms for specific tasks. Training a model is challenging and requires careful adjustments and monitoring to ensure it’s accurate and useful.

Checking Model Performance

When a model is trained, then it is important to check how well it works. Machine learning engineers use scores like accuracy to see model performance. They usually test the model with separate data to see how it performs in real-world situations and make improvements as needed.

Arranging and Maintaining the Model

After testing, ML engineers put the model into action so it can work with real-time data. They monitor the model to make sure it stays accurate over time, as data can change and affect results. Regular updates help keep the model effective.

Working with Other Teams

ML engineers often work closely with data scientists, software engineers, and experts in the field. This teamwork ensures that the machine learning solution fits the business goals and integrates smoothly with other systems.

Important skill that should have to become Machine Learning Engineer:

Programming Languages

Python and R are popular options in machine learning, also other languages like Java or C++ can also help, especially for projects needing high performance.

Data Handling and Processing

Working with large datasets is necessary in Machine Learning. ML engineers should know how to use SQL and other database tools and be skilled in preparing and cleaning data before using it in models.

Machine Learning Structure

ML engineers need to know structure like TensorFlow, Keras, PyTorch, and sci-kit-learn. Each of these tools has unique strengths for building and training models, so choosing the right one depends on the project.

Mathematics and Statistics

A strong background in math, including calculus, linear algebra, probability, and statistics, helps ML engineers understand how algorithms work and make accurate predictions.

Why to become a Machine Learning engineer?

A career as a machine learning engineer is both challenging and creative, allowing you to work with the latest technology. This field is always changing, with new tools and ideas coming up every year. If you like to enjoy solving complex problems and want to make a real impact, ML engineering offers an exciting path.

Conclusion 

Machine learning engineer plays an important role in AI and data science, turning data into useful insights and creating systems that learn on their own. This career is great for people who love technology, enjoy learning, and want to make a difference in their lives. With many opportunities and uses, Artificial intelligence is a growing field that promises exciting innovations that will shape our future. Artificial Intelligence is changing the world and we should also keep updated our knowledge in this field, Read AI related latest blogs here.

[lit-lakshyainstitute]

Empowering Future Technocrats- Lakshya Institute of Technology

1. Introduction:- LIT was established with a vision to nurture talent and foster innovation. Lakshya Institute of Technology (LIT) is a beacon of excellence in Bhubaneswar's educational landscape, with a commitment to providing quality education and holistic development,  LIT is recognised as the best college for BCA, BSc CS, ITM, and Data Science.

LIT's vision is to be a centre of excellence in technical education, innovation and entrepreneurship. Its mission is to impart quality education, instil ethical values, and equip students with the skills required to excel in their chosen fields, which are suitable for industry needs.

2. Infrastructure:

LIT boasts of a sprawling campus equipped with state-of-the-art facilities conducive to learning and research.

The institute houses well-equipped laboratories, advanced computing facilities, and a library with a vast collection of books, journals, and online resources. We also have modern classrooms, seminar halls, and auditoriums, which provide a conducive environment for academic discourse and extracurricular activities and ensure the all-round development of one’s personality.

3. Academic Programs:

BCA (Bachelor of Computer Applications): LIT offers a comprehensive BCA program designed to equip students with a strong foundation in computer science, programming languages, database management, and software development. The knowledge acquired enables one to get a suitable MNC job and equips one to start an enterprise.

BSc (Computer Science): The BSc program in Computer Science at LIT emphasizes theoretical concepts and practical skills in areas such as algorithms, data structures, computer architecture, and software engineering. Essential to place a student in a software company and enables to open a software enterprise.

BSc (Information Technology Management): LIT's BSc program in ITM combines the principles of information technology with management concepts, preparing students for roles at the intersection of technology and business. In collocating the IT knowledge and business acumen for both industry and own enterprise.  

BSc (Data Science): Recognizing the growing demand for skilled data professionals, LIT offers specialized courses in Data Science, covering topics such as data analysis, machine learning, statistical modeling, and data visualization towards enabling one to suitable placed in top IT industry.

4. Curriculum and Pedagogy:

The curriculum for each program in LIT is meticulously crafted to align with best industry trends and emerging technologies.

LIT employs a blend of traditional teaching methods, experiential learning, and hands-on practical sessions to ensure conceptual clarity and skill development among students.

Project-based learning, industry internships, and collaborative research projects are an integral part of our curriculum, enabling students to gain real-world experience and industry exposure most suitable for getting quality placement and also getting the opportunity to start their own enterprise.

5. Faculty and Research:

LIT prides itself on its team of dedicated faculty members comprising experienced academicians, industry experts, and researchers.

The faculty members at LIT are not only passionate educators but also active contributors to research and innovation in their respective fields.

6. Industry Collaborations and Placements:

LIT has forged strong partnerships with leading multinational companies (MNCs)  in the IT and allied industries, facilitating internships, training programs, and placement opportunities for all our students.

The institute's placement cell works tirelessly to connect students with prospective employers, organize campus recruitment drives, and provide career counselling and guidance.

Graduates of LIT's programs are highly sought after by top IT MNCs for their strong technical skills, problem-solving abilities, and professional demeanour.

7. Student Life and Extracurricular Activities:

Beyond academics, LIT offers a vibrant campus life enriched with a plethora of co- curricular and extracurricular activities, clubs, activities and events, shaping up one’s personality.

Students actively participate in technical workshops, hackathons, coding competitions, and industry interactions to enhance their skills and broaden their horizons.

The institute encourages leadership development, teamwork, and social responsibility through initiatives such as student clubs, community service projects, and cultural events.

8. Alumni Network:

LIT takes pride in its alumni network comprising successful professionals, entrepreneurs, and leaders across various industries.

Alumni of LIT serve as mentors, role models, and ambassadors, guiding current students, providing career advice, and fostering a sense of camaraderie and belonging.

Conclusion:

Lakshya Institute of Technology stands as a bright star of excellence in technical education, empowering students to realize their full potential and become future-ready technocrats. With its comprehensive programs, industry-aligned curriculum, experienced faculty, and robust placement assistance, LIT continues to nurture talent and shape the leaders of tomorrow in the fields of Computer Applications, Computer Science, Information Technology Management, and Data Science.

[govindhtech]

Intel VTune Profiler For Data Parallel Python Applications

Intel VTune Profiler tutorial

This brief tutorial will show you how to use Intel VTune Profiler to profile the performance of a Python application using the NumPy and Numba example applications.

Analysing Performance in Applications and Systems

For HPC, cloud, IoT, media, storage, and other applications, Intel VTune Profiler optimises system performance, application performance, and system configuration.

Optimise the performance of the entire application not just the accelerated part using the CPU, GPU, and FPGA.

Profile SYCL, C, C++, C#, Fortran, OpenCL code, Python, Google Go, Java,.NET, Assembly, or any combination of languages can be multilingual.

Application or System: Obtain detailed results mapped to source code or coarse-grained system data for a longer time period.

Power: Maximise efficiency without resorting to thermal or power-related throttling.

VTune platform profiler

It has following Features.

Optimisation of Algorithms

Find your code’s “hot spots,” or the sections that take the longest.

Use Flame Graph to see hot code routes and the amount of time spent in each function and with its callees.

Bottlenecks in Microarchitecture and Memory

Use microarchitecture exploration analysis to pinpoint the major hardware problems affecting your application’s performance.

Identify memory-access-related concerns, such as cache misses and difficulty with high bandwidth.

Inductors and XPUs

Improve data transfers and GPU offload schema for SYCL, OpenCL, Microsoft DirectX, or OpenMP offload code. Determine which GPU kernels take the longest to optimise further.

Examine GPU-bound programs for inefficient kernel algorithms or microarchitectural restrictions that may be causing performance problems.

Examine FPGA utilisation and the interactions between CPU and FPGA.

Technical summary: Determine the most time-consuming operations that are executing on the neural processing unit (NPU) and learn how much data is exchanged between the NPU and DDR memory.

In parallelism

Check the threading efficiency of the code. Determine which threading problems are affecting performance.

Examine compute-intensive or throughput HPC programs to determine how well they utilise memory, vectorisation, and the CPU.

Interface and Platform

Find the points in I/O-intensive applications where performance is stalled. Examine the hardware’s ability to handle I/O traffic produced by integrated accelerators or external PCIe devices.

Use System Overview to get a detailed overview of short-term workloads.

Multiple Nodes

Describe the performance characteristics of workloads involving OpenMP and large-scale message passing interfaces (MPI).

Determine any scalability problems and receive suggestions for a thorough investigation.

Intel VTune Profiler

To improve Python performance while using Intel systems, install and utilise the Intel Distribution for Python and Data Parallel Extensions for Python with your applications.

Configure your Python-using VTune Profiler setup.

To find performance issues and areas for improvement, profile three distinct Python application implementations. The pairwise distance calculation algorithm commonly used in machine learning and data analytics will be demonstrated in this article using the NumPy example.

The following packages are used by the three distinct implementations.

Numpy Optimised for Intel

NumPy’s Data Parallel Extension

Extensions for Numba on GPU with Data Parallelism

Python’s NumPy and Data Parallel Extension

By providing optimised heterogeneous computing, Intel Distribution for Python and Intel Data Parallel Extension for Python offer a fantastic and straightforward approach to develop high-performance machine learning (ML) and scientific applications.

Added to the Python Intel Distribution is:

Scalability on PCs, powerful servers, and laptops utilising every CPU core available.

Assistance with the most recent Intel CPU instruction sets.

Accelerating core numerical and machine learning packages with libraries such as the Intel oneAPI Math Kernel Library (oneMKL) and Intel oneAPI Data Analytics Library (oneDAL) allows for near-native performance.

Tools for optimising Python code into instructions with more productivity.

Important Python bindings to help your Python project integrate Intel native tools more easily.

Three core packages make up the Data Parallel Extensions for Python:

The NumPy Data Parallel Extensions (dpnp)

Data Parallel Extensions for Numba, aka numba_dpex

Tensor data structure support, device selection, data allocation on devices, and user-defined data parallel extensions for Python are all provided by the dpctl (Data Parallel Control library).

It is best to obtain insights with comprehensive source code level analysis into compute and memory bottlenecks in order to promptly identify and resolve unanticipated performance difficulties in Machine Learning (ML),  Artificial Intelligence ( AI), and other scientific workloads. This may be done with Python-based ML and AI programs as well as C/C++ code using Intel VTune Profiler. The methods for profiling these kinds of Python apps are the main topic of this paper.

Using highly optimised Intel Optimised Numpy and Data Parallel Extension for Python libraries, developers can replace the source lines causing performance loss with the help of Intel VTune Profiler, a sophisticated tool.

Setting up and Installing

1. Install Intel Distribution for Python

2. Create a Python Virtual Environment

   python -m venv pyenv

   pyenv\Scripts\activate

3. Install Python packages

   pip install numpy

   pip install dpnp

   pip install numba

   pip install numba-dpex

   pip install pyitt

Make Use of Reference Configuration

The hardware and software components used for the reference example code we use are:

Software Components:

dpnp 0.14.0+189.gfcddad2474

mkl-fft 1.3.8

mkl-random 1.2.4

mkl-service 2.4.0

mkl-umath 0.1.1

numba 0.59.0

numba-dpex 0.21.4

numpy 1.26.4

pyitt 1.1.0

Operating System:

Linux, Ubuntu 22.04.3 LTS

CPU:

Intel Xeon Platinum 8480+

GPU:

Intel Data Center GPU Max 1550

The Example Application for NumPy

Intel will demonstrate how to use Intel VTune Profiler and its Intel Instrumentation and Tracing Technology (ITT) API to optimise a NumPy application step-by-step. The pairwise distance application, a well-liked approach in fields including biology, high performance computing (HPC), machine learning, and geographic data analytics, will be used in this article.

Summary

The three stages of optimisation that we will discuss in this post are summarised as follows:

Step 1: Examining the Intel Optimised Numpy Pairwise Distance Implementation: Here, we’ll attempt to comprehend the obstacles affecting the NumPy implementation’s performance.

Step 2: Profiling Data Parallel Extension for Pairwise Distance NumPy Implementation: We intend to examine the implementation and see whether there is a performance disparity.

Step 3: Profiling Data Parallel Extension for Pairwise Distance Implementation on Numba GPU: Analysing the numba-dpex implementation’s GPU performance

Boost Your Python NumPy Application

Intel has shown how to quickly discover compute and memory bottlenecks in a Python application using Intel VTune Profiler.

Intel VTune Profiler aids in identifying bottlenecks’ root causes and strategies for enhancing application performance.

It can assist in mapping the main bottleneck jobs to the source code/assembly level and displaying the related CPU/GPU time.

Even more comprehensive, developer-friendly profiling results can be obtained by using the Instrumentation and Tracing API (ITT APIs).

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