Hackathons

This week’s HP & NVIDIA Developer Challenge Office Hours gave participants a chance to ask questions, troubleshoot roadblocks, and share ideas as we head into the final stretch of the hackathon. Here's a quick recap of what came up:🛠️ Project Collaboration & SetupOne common hurdle: collaborating with teammates in AI Studio. By default, accounts are set up with one seat—but that’s not a hard limit. The team confirmed that if you email aistudio.support@hp.com, they can manually add a collaborator so your teammate can join your project.Another hot topic was installation and compatibility. Participants asked about running AI Studio on services like RunPod and Linux-based systems. While not officially supported yet, the team encouraged folks to try installing the Linux version on Ubuntu 22.04 and share feedback. There’s even a helpful download and setup guide shared during the call.🚀 Running AI Studio Without a GPUA few users asked if AI Studio can run without an NVIDIA GPU. The s

The Installation Guide covers:What AI Studio is and the basic installation process for both Windows and Ubuntu The system requirements needed before starting the installation What to expect after installation and how to get startedHave questions? Drop a comment below!Learn more about AI Studio here and see how Z by HP powers Data Science & AI Solutions.

 Thanks to everyone who joined this week’s Devpost webinar and office hours. We had a great session led by Rick Jacobs, who demonstrated how to build and deploy a tourism recommendation system using BERT in AI Studio. The discussion covered key features of the platform, tips for working without a GPU, and clarified project submission requirements. Below is a summary of what was covered and shared during the session.Live Demo: Tourism Recommendation System in AI StudioPresented by Rick Jacobs, the session featured a walkthrough of building a semantic search recommendation system using a BERT model from the NVIDIA NGC catalog. The demo included: Generating embeddings from a corpus using a pre-trained BERT model Deploying the model with MLflow for tracking Serving the system locally with Swagger UI for testing Key Features of AI Studio HighlightedPreconfigured environments and containerized workflows Support for custom models and datasets Built-in deployment tools including MLflow

General Updates & Reminders Developers were encouraged to share 30-second project videos or screenshots in the Discord announcements channel. Use the AI Studio support tab to submit tickets and logs for technical help. Use the Send Feedback feature in AI Studio to share product improvement ideas. 🔍 Key Questions Answered Is it too late to join the hackathon? ➤ No. There's about one month left, and it's possible to submit a solid project within that time. Can I submit a localhost project? ➤ Yes, as long as the GitHub repo is public and includes setup instructions. Hosting on a live site is optional but earns bonus points. Are some industry categories prioritized? ➤ No. All industries are equal; stronger, more impactful use cases will be favored regardless of sector. Tech Stack Clarification: ➤ React, Node.js, C++, and other stacks are allowed. A React-based UI example was shared via GitHub. Troubleshooting TensorFlow/Nemo issues in AI Studio: ➤ Could be due to image or

 MLflow Model Monitoring OverviewThis tutorial project implements a model monitoring and experiment tracking system using MLflow. The system enables data scientists and ML engineers to track, compare, and deploy machine learning models with full versioning and performance monitoring capabilities. FeaturesExperiment Tracking: Record and compare different model runs with detailed metrics and parameters Model Versioning: Maintain a complete history of model iterations with MLflow's Model Registry Performance Monitoring: Track key metrics like accuracy, response time, and similarity scores Parameter Logging: Automatically record all model parameters and configurations AI Studio with MLflow Benefits for ML ProjectsExperiment Organization: Structured tracking of all model iterations and experiments Reproducibility: Complete environment and parameter tracking ensures reproducible results Collaboration: Team members can easily share and compare experiments Model Lifecycle Management: Streamlin

AI Studio Troubleshooting Guide Getting Started Issues Issue: Unable to create an account Solution:  Check if you're using a supported email domain  Clear browser cache and cookies  Try a different browser  Ensure you have a stable internet connection  If corporate email, check with IT about firewall restrictions  Issue: Can't access dashboard after login Solution:  Verify you're using the correct login credentials  Check if your account has been properly activated  Clear browser cache and try incognito mode  Disable browser extensions that might interfere  Contact support if issue persists   Connection Problems Issue: Frequent disconnections during sessions Solution:  Check your internet connection stability  Close unused browser tabs to free up resources  Try a wired connection instead of Wi-Fi if possible  Clear browser cache and restart  Check if your VPN is causing interference  Issue: API endpoints unreachable Solution:  Verify the correct endpoint URLs in your reques

  This week’s Office Hours was hosted by Sothan Thach and supported by Christian Charles and Suresh Ramanan from HP. It provided live technical help and guidance for participants in the DevPost Hackathon, particularly those using HP AI Studio.Summary  AI Studio Compatibility & Setup AI Studio currently supports Windows 10/11 and Ubuntu 22.04 LTS only. Running on Ubuntu 24.04 or VMs (VirtualBox, WSL, etc.) may cause issues; such configurations are not officially supported. Using GitHub Sample Projects Suresh gave a full walkthrough on how to clone and load projects from GitHub into AI Studio. He highlighted the value of the vacation-recommendation sample and showed how to run notebooks inside the correct AI Studio kernel. Hardware Requirements A GPU is needed; using platforms like Google Colab is not supported. Minimum specs: ~16GB RAM, compatible NVIDIA GPU. RAM needs may vary depending on model size (e.g., 1B parameters ≈ 1GB GPU memory). Gett

 AI Studio 101 (Watch the Recording)What is AI Studio? - Discover key features and capabilities to power your AI projects Getting Started on AI Studio - Follow along with our download experience, first sign-on process, and platform orientation Setting up your first project - Learn practical tips, tricks, and what to anticipate as you begin building 

Ready to dive into the HP AI Studio & NVIDIA Developer Challenge? The process is straightforward: after registering on Devpost, you'll receive an activation email from AI Studio with download links. Simply click the activation link, sign in with your HPID (or create one), download the installer for your platform, and follow the setup wizard. The first team member to sign in receives admin privileges and can invite others through the team settings.Once set up, you can create projects, define workspaces using NVIDIA NGC GPU-optimized pre-trained models, and start coding in notebooks. AI Studio brings together everything you need—data, people, and compute—in one powerful environment optimized for AI development. Be sure to check out the base images and GPU resources available to maximize your project's performance during the challenge! Have questions? Drop a comment below!Learn more about AI Studio here and see how Z by HP powers Data Science & AI Solutions.

You can find the code for the project and README in this public repository: https://github.com/HPInc/aistudio-samples/tree/main/deep-learning-in-ais/image_generation_deepseek_janus_pro

 AI Studio Platform Walkthrough: From Project Creation to Model Deployment This video covers essential features of AI Studio:Project Creation: Learn how to set up new projects with customized descriptions, tags, and privacy settings. We'll show you how to seamlessly connect to GitHub repositories for efficient code management.Asset Management: Discover how to upload, organize, and leverage different assets within your projects, making data management intuitive and efficient.NVIDIA NGC Model Integration: Explore the NGC catalog and see how easily you can search for and incorporate NVIDIA's GPU-optimized pre-trained models into your workflow, accelerating your development process.Workspace Configuration: Master the creation of custom workspaces tailored to your project's computational needs, ensuring you have the right resources for your specific tasks.Notebook Development: Experience the powerful coding environment with Jupyter notebooks, complete with pre-configured dependencies and se

AI Studio is a secure, containerized development environment that enables easy team collaboration.  This document outlines the requirements for the AI Studio platform.   

AI Studio workspaces streamline the entire AI/ML development lifecycle, from initial experimentation to final deployment:• Quickly spin up an environment with the necessary libraries• Collaborate effortlessly with team members, sharing notebooks and resources in real time• Utilize the containerized environment to develop locally and maintain control of sensitive dataThis document walks through the process of creating AI Studio Workspaces.