In this video we will guide you through two different AI based workflows, involving ChatUI and CVAT apps.
You will learn how to:
Use advanced CVAT features including auto-annotation, algorithmic assistance, management and analytics.
Use Chat UI as a flexible interface for hosting of various LLM models, and interact via a chat or API environment.
Use ChatUI for semantic search in a knowledge base.
Use CVAT as a powerful annotation tool, including image classification, object detection, semantic and instance segmentation, and video / 3D annotations.
In this video we will guide you through the complete pipeline of transcribing audio files from speech to text and editing and classifying transcription segments.
In this session, you’ll learn how to:
Use Transcriber for transcribing audio/video files. Transcriber is based on Open AI’s Whisper language model. The app can transcribe speech audio to text in various formats and uses the WhisperX package to perform speaker recognition.
Navigate the new, simple, drag and drop Transcriber user interface to make it easier for you to use AI to transcribe audio files.
Edit and classify the transcriptions with Speech Analyzer. Speech Analyzer is an application built on top of Label Studio, specifically optimized for dialogue analysis. It enables you to label, edit, and annotate transcriptions generated using Transcriber.
Perform a comprehensive dialogue analysis on UCloud involving transcribing audio files using Transcriber, followed by transcription analysis with Speech Analyzer.
All workflows will be executed inside a UCloud project environment with access to GPU resources.
Target audience: Researchers across all Departments, particularly Digital Humanities and Social Science, Students, AI interested.
In this video we will guide you through the complete pipeline of fine-tuning large language models (LLMs) for specialised tasks such as medical question-answering using NeMo Framework and Triton Inference Server.
Prepare and preprocess open-source datasets for fine-tuning.
Apply Parameter-Efficient Fine-Tuning (PEFT) using LoRA with NVIDIA NeMo Framework.
Deploy optimised LLMs using NVIDIATriton Inference Server and TensorRT-LLM.
Generate a synthetic Q&A dataset using Label Studio connected to a live inference backend.
Fine-tune and evaluate your customised LLM for domain-specific applications.
All workflows will be executed inside a UCloud project environment with access to GPU resources.
Target audience: Machine learning practitioners, researchers, and engineers interested in LLM customisation, domain adaptation, or scalable model deployment.
In this video we will go through the process of developing a course on UCloud, using UCloud Courses – a tool for hosting and managing university courses on UCloud. Want to know more about this new feature, check out our webinar recording introducing UCloud Courses.
Introduction
00:00 – Outline of the workshop agenda 03:10 – Introduction to the UCloud Courses concept 04:23 – Advantages of using UCloud Courses 05:56 – Outline of the steps involved in developing a UCloud Course 09:54 – Requesting and planning a UCloud Course 10:20 – Example of the planning and structuring of an existing UCloud Course
Developing a UCloud Course step by step
Setting up the environment 15:53 – Preliminary remarks 17:15 – Showing the existing UCloud Course that will be re-developed in the workshop 18:02 – Showing the UCloud-Courses GitHub repository 18:40 – Software requirements and -recommendations 20:22 – Short introduction to Git and GitHub 24:27 – Cloning the repository 25:15 – Creating a working branch 26:12 – What should go in the UCloud-Courses repository and what shouldn’t 27:24 – Installing the required dependencies
Creating a new UCloud Course and modifying the templates 28:00 – Creating a UCloud Course using a prepared script 30:31 – Walk-through of the different auto-generated files/folders in the course folder 33:35 – Short introduction to Docker 38:15 – Modifying the Dockerfile 42:32 – What should go in the UCloud-Courses repository and what shouldn’t 45:28 – Building the Docker image locally using a prepared script 47:54 – Modifying the starting script 01:00:50 – What can, shouldn’t, and mustn’t be changed while the course is running
Building and testing the course locally 01:05:00 – Re-building the Docker image locally using a prepared script 01:05:25 – Running a Docker container locally using a prepared script 01:10:58 – Opening the JupyterLab interface on localhost
Finalising the course app 01:12:48 – Opening a pull request and requesting code review 01:19:44 – Testing the course app on UCloud before it’s deployed
Closing remarks
01:25:44 – Reusing/updating an existing UCloud course 01:28:19 – The financial model in brief 01:29:38 – Useful links and resources
In this video we will introduce the new UCloud Courses app – a tool for hosting and managing university courses on UCloud.
Dr. Federica Lo Verso will walk you through the concept and background of the app. You’ll get a hands-on demonstration of how to access and use the tool, explore its integration with GitHub, and hear directly from Dr. Himanshu Khandelia, who shares his experience using UCloud Courses in real teaching scenarios.
We’ll also guide you through the application process, show you the technical and financial requirements, and give you a preview of an upcoming hands-on course.
00:00 – Introduction by Dr. Federica Lo Verso 01:23 – Background: Launch of the new UCloud Courses app 04:12 – Walkthrough: Where to find the Courses app and how to use it 07:00 – Recap of the UCloud Courses app demonstration 07:56 – Walkthrough: How the GitHub repository works 10:07 – Advantages of using UCloud Courses 11:12 – Use case introduction: Dr. Himanshu Khandelia 11:58 – Testimonial: Dr. Khandelia shares his experience using UCloud Courses 14:00 – Live demo: Dr. Khandelia shows his use of Courses and GitHub integration 23:00 – Application procedure 24:21 – Necessary resources: Compute and storage 25:28 – Re-use and update existing UCloud Courses 26:20 – Financial model for support 27:12 – Outro and teaser for the upcoming hands-on course
00:00 – Introduction and welcome 00:50 – Introduction to UCloud 05:27 – DeiC Interactive HPC website 06:07 – UCloud: Log in 07:06 – UCloud: HPC providers on the UCloud platform 11:08 – UCloud: Initial resource allocations in “My workspace” 12:27 – UCloud: Storage in “My workspace” 13:28 – UCloud: Resources and applications for new resource allocations 13:50 – UCloud: Completing the resource application 21:05 – UCloud: Resource pools and limits (what does it cost?) 23:05 – UCloud: Apps, the app store, applications index in UCloud docs 26:00 – UCloud: Advanced use cases and integration patterns
27:45 – UCloud: Transcriber: Intro and resource needs 30:08 – UCloud: Transcriber: Uploading files inside a project 31:20 – UCloud: Transcriber: Finding and launching Transcriber 32:00 – UCloud: Transcriber: Run Transcriber for the first time (Completing the app launch screen) 34:28 – UCloud: Transcriber: Running multiple Transcriber jobs simultaneously 35:00 – UCloud: Transcriber: Import previous Transcriber job parameters 35:35 – UCloud: Transcriber: Opening running jobs from the “Recent runs” pane 37:15 – UCloud: Transcriber: Transcriber output directories (Jobs folder) 38:26 – UCloud: Transcriber: Transcriber outputs in “Recent runs” 39:54 – UCloud: Transcriber: Output inspection and data download of zip file
41:36 – UCloud: Chat UI: Introduction 43:27 – UCloud: Chat UI: Run Chat UI for the first time (Completing the app launch screen) 48:40 – UCloud: Chat UI: First look at the Chat UI interface (disable new sign-ups and download a model) 52:36 – UCloud: Chat UI: Including documents to support Retrieval Augmented Generation (RAG) (i.e. supplementing the model with an additional document) 57:18 – UCloud: Chat UI: Extend the job time on any UCloud job (if needed) 58:28 – UCloud: Chat UI: Text-to-image generation (stable diffusion with a standard LLM model) 1:01:45 – UCloud: Chat UI: RAG for (best guess) document summarization (beware of model hallucinations)
1:04:45 – UCloud: Label Studio: Introduction 1:05:15 – UCloud: Label Studio: Run Label Studio for the first time (Completing the app launch screen) 1:07:55 – UCloud: Label Studio: First look at the Label Studio interface 1:09:40 – UCloud: Label Studio: Brief view of the Label Studio documentation 1:10:52 – UCloud: Label Studio: Introduction to the coming “Speech Analyser” application 1:15:45 – UCloud: Label Studio: Documentation
