#IntelTiberDeveloperCloud

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Intel Tiber Developer Cloud, Text- to-Image Stable Diffusion

Check Out GenAI for Text-to-Image with a Stable Diffusion Intel Tiber Developer Cloud Workshop.

What is Intel Tiber Developer Cloud?

With access to state-of-the-art Intel hardware and software solutions, developers, AI/ML researchers, ecosystem partners, AI startups, and enterprise customers can build, test, run, and optimize AI and High-Performance Computing applications at a low cost and overhead thanks to the Intel Tiber Developer Cloud, a cloud-based platform. With access to AI-optimized software like oneAPI, the Intel Tiber Developer Cloud offers developers a simple way to create with small or large workloads on Intel CPUs, GPUs, and the AI PC.

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Developers and enterprise clients have the option to use free shared workspaces and Jupyter notebooks to explore the possibilities of the platform and hardware and discover what Intel can accomplish.

Text-to-Image

This article will guide you through a workshop that uses the Stable Diffusion model practically to produce visuals in response to a written challenge. You will discover how to conduct inference using the Stable Diffusion text-to-image generation model using PyTorch and Intel Gaudi AI Accelerators. Additionally, you will see how the Intel Tiber Developer Cloud can assist you in creating and implementing generative AI workloads.

Text To Image AI Generator

AI Generation and Steady Diffusion

Industry-wide, generative artificial intelligence (GenAI) is quickly taking off, revolutionizing content creation and offering fresh approaches to problem-solving and creative expression. One prominent GenAI application is text-to-image generation, which uses an understanding of the context and meaning of a user-provided description to generate images based on text prompts. To learn correlations between words and visual attributes, the model is trained on massive datasets of photos linked with associated textual descriptions.

A well-liked GenAI deep learning model called Stable Diffusion uses text-to-image synthesis to produce images. Diffusion models work by progressively transforming random noise into a visually significant result. Due to its efficiency, scalability, and open-source nature, stable diffusion is widely used in a variety of creative applications.

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The Stable Diffusion model in this training is run using PyTorch and the Intel Gaudi AI Accelerator. The Intel Extension for PyTorch, which maximizes deep learning training and inference performance on Intel CPUs for a variety of applications, including large language models (LLMs) and Generative AI (GenAI), is another option for GPU support and improved performance.

Stable Diffusion

To access the Training page once on the platform, click the Menu icon in the upper left corner.

The Intel Tiber Developer Cloud‘s Training website features a number of JupyterLab workshops that you may try out, including as those in AI, AI with Intel Gaudi 2 Accelerators, C++ SYCL, Gen AI, and the Rendering Toolkit.

Workshop on Inference Using Stable Diffusion

Thwy will look at the Inference with Stable Diffusion v2.1 workshop and browse to the AI with Intel Gaudi 2 Accelerator course in this tutorial.

Make that Python 3 (ipykernel) is selected in the upper right corner of the Jupyter notebook training window once it launches. To see an example of inference using stable diffusion and creating an image from your prompt, run the cells and adhere to the notebook’s instructions. An expanded description of the procedures listed in the training notebook can be found below.

Note: the Jupyter notebook contains the complete code; the cells shown here are merely for reference and lack important lines that are necessary for proper operation.

Configuring the Environment

Installing all the Python package prerequisites and cloning the Habana Model-References repository branch to this docker will come first. Additionally, They are going to download the Hugging Face model checkpoint.%cd ~/Gaudi-tutorials/PyTorch/Single_card_tutorials !git clone -b 1.15.1 https://github.com/habanaai/Model-References %cd Model-References/PyTorch/generative_models/stable-diffusion-v-2-1 !pip install -q -r requirements.txt !wget https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/ v2-1_512-ema-pruned.ckpt

Executing the Inference

prompt = input("Enter a prompt for image generation: ")

The prompt field is created by the aforementioned line of code, from which the model generates the image. To generate an image, you can enter any text; in this tutorial, for instance, they’ll use the prompt “cat wearing a hat.”cmd = f'python3 scripts/txt2img.py --prompt "{prompt}" 1 --ckpt v2-1_512-ema-pruned.ckpt \ --config configs/stable-diffusion/v2-inference.yaml \ --H 512 --W 512 \ --n_samples 1 \ --n_iter 2 --steps 35 \ --k_sampler dpmpp_2m \ --use_hpu_graph'

print(cmd) import os os.system(cmd)

Examining the Outcomes

Stable Diffusion will be used to produce their image, and Intel can verify the outcome. To view the created image, you can either run the cells in the notebook or navigate to the output folder using the File Browser on the left-hand panel:

/Gaudi-tutorials/PyTorch/Single_card_tutorials/Model-References /PyTorch/generative_models/stable-diffusion-v-2-1/outputs/txt2img-samples/Image Credit To Intel

Once you locate the outputs folder and locate your image, grid-0000.png, you may examine the resulting image. This is the image that resulted from the prompt in this tutorial:

You will have effectively been introduced to the capabilities of GenAI and Stable Diffusion on Intel Gaudi AI Accelerators, including PyTorch, model inference, and quick engineering, after completing the tasks in the notebook.

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Intel Webinar: Experienced Assistance To Implement LLMs

How Prediction Guard Uses Intel Gaudi 2 AI Accelerators to Provide Reliable AI.

Intel webinar

Large language models (LLMs) and generative AI are two areas where the growing use of open-source tools and software at the corporate level makes it necessary to talk about the key tactics and technologies needed to build safe, scalable, and effective LLMs for business applications. In this Intel webinar, Rahul Unnikrishnan Nair, Engineering Lead at Intel Liftoff for Startups, and Daniel Whitenack, Ph.D., creator of Prediction Guard, lead us through the important topics of implementing LLMs utilizing open models, protecting data privacy, and preserving high accuracy.

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Intel AI webinar

Important Conditions for Enterprise LLM Adoption

Three essential elements are identified in the Intel webinar for an enterprise LLM adoption to be successful: using open models, protecting data privacy, and retaining high accuracy. Enterprises may have more control and customization using open models like Mistral and Llama 3, which allow them to obtain model weights and access inference code. In contrast, closed models lack insight into underlying processes and are accessible via APIs.

Businesses that handle sensitive data like PHI and PII must secure data privacy. HIPAA compliance is typically essential in these scenarios. High accuracy is also crucial, necessitating strong procedures to compare the LLM outputs with ground truth data in order to reduce problems like as hallucinations, in which the output generates erroneous or misleading information even while it is grammatically and coherently accurate.

Obstacles in Closed Models

Closed models like those offered by Cohere and OpenAI have a number of drawbacks. Businesses may be biased and make mistakes because they are unable to observe how their inputs and outputs are handled. In the absence of transparency, consumers could experience latency variations and moderation failures without knowing why they occur. Prompt injection attacks can provide serious security threats because they may use closed models to expose confidential information. These problems highlight how crucial it is to use open models in corporate applications.

Prediction Guard

The Method Used by Prediction Guard

The platform from Prediction Guard tackles these issues by combining performance enhancements, strong security measures, and safe hosting. To ensure security, models are hosted in private settings inside the Intel Tiber Developer Cloud. To improve speed and save costs, Intel Gaudi 2 AI accelerators are used. Before PII reaches the LLM, input filters are employed to disguise or substitute it and prevent prompt injections. By comparing LLM outputs to ground truth data, output validators guarantee the factual consistency of the data.

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During the optimization phase, which lasted from September 2023 to April 2024, load balancing over many Gaudi 2 machines, improving prompt processing performance by bucketing and padding similar-sized prompts, and switching to the TGI Gaudi framework for easier model server administration were all done.

Prediction Guard moved to Kubernetes-based architecture in Intel Tiber Developer Cloud during the current growth phase (April 2024 to the present), merging CPU and Gaudi node groups. Implemented include deployment automation, performance and uptime monitoring, and integration with Cloudflare for DDoS protection and CDN services.

Performance and Financial Gains

There were notable gains when switching to Gaudi 2. Compared to earlier GPU systems, Prediction Guard accomplished a 10x decrease in computation costs and a 2x gain in throughput for corporate applications. Prediction Guard’s sub-200ms time-to-first-token latency reduction puts it at the top of the industry performance rankings. These advantages were obtained without performance loss, demonstrating Gaudi 2’s scalability and cost-effectiveness.

Technical Analysis and Suggestions

The presenters stressed that having access to an LLM API alone is not enough for a strong corporate AI solution. Thorough validation against ground truth data is necessary to guarantee the outputs’ correctness and reliability. Data management is a crucial factor in AI system design as integrating sensitive data requires robust privacy and security safeguards. Prediction Guard offers other developers a blueprint for optimizing Gaudi 2 consumption via a staged approach. The secret to a successful deployment is to validate core functionality first, then gradually scale and optimize depending on performance data and user input.

Additional Information on Technical Execution

In order to optimize memory and compute utilization, handling static forms during the first migration phase required setting up model servers to manage varying prompt lengths by padding them to specified sizes. By processing a window of requests in bulk via dynamic batching, the system was able to increase throughput and decrease delay.

In order to properly handle traffic and prevent bottlenecks, load balancing among numerous Gaudi 2 servers was deployed during the optimization process. Performance was further improved by streamlining the processing of input prompts by grouping them into buckets according to size and padding within each bucket. Changing to the TGI Gaudi framework made managing model servers easier.

Scalable and robust deployment was made possible during the scaling phase by the implementation of an Intel Kubernetes Service (IKS) cluster that integrates CPU and Gaudi node groups. High availability and performance were guaranteed by automating deployment procedures and putting monitoring systems in place. Model serving efficiency was maximized by setting up inference parameters and controlling key-value caches.

Useful Implementation Advice

It is advised that developers and businesses wishing to use comparable AI solutions begin with open models in order to maintain control and customization options. It is crucial to make sure that sensitive data is handled safely and in accordance with applicable regulations. Successful deployment also requires taking a staged approach to optimization, beginning with fundamental features and progressively improving performance depending on measurements and feedback. Finally, optimizing and integrating processes may be streamlined by using frameworks like TGI Gaudi and Optimum Habana.

In summary

Webinar Intel

Prediction Guard’s all-encompassing strategy, developed in partnership with Intel, exemplifies how businesses may implement scalable, effective, and safe AI solutions. Prediction Guard offers a strong foundation for corporate AI adoption by using Intel Gaudi 2 and Intel Tiber Developer Cloud to handle important issues related to control, personalization, data protection, and accuracy. The Intel webinar‘s technical insights and useful suggestions provide developers and businesses with invaluable direction for negotiating the challenges associated with LLM adoption.

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The Veda App Resolved Asia’s Challenges Regarding Education

Asia’s Educational Challenges Solved by the Veda App.

As the school year draws to a close, the Veda app will still be beneficial to over a million kids in Brunei, Japan, and hundreds of schools in Nepal.

Developed by Intel Liftoff program member InGrails Software, the Veda app is a cloud-based all-in-one school and college administration solution that combines all of the separate systems that educational institutions have historically required to employ.

The Veda App Solutions

The Veda app helps schools streamline their operations, provide parents real-time information, and enhance student instruction by automating administrative duties.

All administrative tasks at the school, such as payroll, inventory management, billing and finance, and academic (class, test, report, assignments, attendance, alerts, etc.), are now automated on a single platform. Schools were more conscious of the need to improve remote learning options as a result of the COVID-19 epidemic.

Teachers and students may interact with the curriculum at various times and from different places thanks to the platform’s support for asynchronous learning. This guarantees that learners may go on with their study in spite of outside disturbances.

Technological Accessibility

Although the requirements of all the schools are similar, their technological competency varies. The platform facilitates the accessibility and use of instructional products.

Veda gives schools simple-to-use tools that help them save time while making choices that will have a lasting effect on their children by using data and visualization. They are able to change the way they give education and learning.

The Role of Intel Liftoff

The Veda team and InGrails credit the Intel Liftoff program for helping them with product development. They also credit the mentorship and resources, such as the Intel Tiber Developer Cloud, for helping them hone their technology and optimize their platform for efficiency and scalability by incorporating artificial intelligence.

Their team members were able to learn new techniques for enhancing system performance while managing system load via a number of virtual workshops. In the future, they want to use the hardware resources made available by the Intel developer platform to further innovate Intel product and stay true to their goal of making education the greatest possible user of rapidly changing technology.

The Veda App‘s founder, Nirdesh Dwa, states, “they are now an all-in-one cloud-based school software and digital learning system for growing, big and ambitious names in education.”

The Effect

Veda has significantly changed the education industry by developing a single solution. The more than 1,200 schools that serve 1.3 million kids in Nepal, Japan, and Brunei have benefited from simplified procedures, a 30% average increase in parent participation, and enhanced instruction thanks to data-driven insights.

Education’s Future in Asia and Africa

The Veda platform is now reaching Central Africa, South East Asia, and the Middle East and North Africa. Additionally, they’re keeping up their goal of completely integrating AI capabilities, such social-emotional learning (SEL) tools and decision support systems, to build even more encouraging and productive learning environments by the middle of 2025.

Their mission is to keep pushing the boundaries of innovation and giving educators the resources they need to thrive in a world becoming more and more digital.

Future ideas for Veda include integrating AI to create a Decision Support System based on instructor input and making predictions about students’ talents for advanced coursework possible.

An all-inclusive MIS and digital learning platform for schools and colleges

They are the greatest all-in-one cloud-based educational software and digital learning platform for rapidly expanding, well-known, and aspirational educational brands.

Designed to Be Used by All

Parents and children may access all the information and study directly from their mobile devices with the help of an app. accessible on Android and iOS platforms.

Designed even for parents without technological experience, this comprehensive solution gives parents access to all the information they need about their kids, school, and important details like bills.

With the aid of their mobile devices, students may access resources and complete assignments, complete online courses, and much more.

Features

All you need to put your college and school on autopilot is Veda. Veda takes care of every facet of education, freeing you up to focus on what really matters: assisting students and securing their future.

Why should your school’s ERP be Veda?

Their superior product quality, first-rate customer support, industry experience, and ever-expanding expertise make us the ideal software for schools and colleges.Image Credit To Intel

More than one thousand universities and institutions vouch for it.

99% of clients have renewed for over six years.

Market leader in 45 Nepalese districts

superior after-sale support

Operating within ten days of the agreed-upon date

MIS Veda & E-learning

enabling online education in over a thousand schools. With only one school administration software, they provide Zoom Integrated Online Classes, Auto Attendance, Assignment with annotation, Subjective and Objective Exams, Learning Materials, Online Admissions, and Online Fee payment.Image Credit To Intel

Your software should reflect the uniqueness of your institution

Every kind of educational institution, including private schools, public schools, foreign schools, Montessori schools, and universities, uses the tried-and-true Veda school management system.

What is the Veda?

“Veda” is a comprehensive platform for digital learning and school management. Veda assists schools with automating daily operations and activities; facilitating effective and economical communication and information sharing among staff, parents, and school administration; and assisting schools in centrally storing, retrieving, and analyzing data produced by various school processes.

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