Configuring Appwrite Functions with K3s

March 10, 2024 - 4 mins read

This guide outlines configuring Appwrite Functions within a K3s environment. It covers essential steps, including installing ngrok for external network access, registering a GitHub App, and setting up Appwrite with required configurations. The process involves updating YAML files for deployment, ensuring proper routing with Traefik, and creating functions through the Appwrite interface. Once set up, the functions are deployed successfully, enabling seamless integration and execution within the K3s infrastructure.

Deploying Budibase in HomeLab

February 25, 2024 - 2 mins read

This post outlines the process of installing Budibase in a HomeLab environment, starting with testing it on Docker Desktop and then deploying it using Helm in Kubernetes. It guides through setting up an admin user, building the first app by creating a database, designing an application form, and configuring submission actions. The summary encapsulates the steps involved in testing, deploying, and building an application with Budibase, highlighting key actions such as Docker Compose setup, Helm installation, app creation, and deployment in a concise manner.

Deploying Appwrite in HomeLab with K3s

February 16, 2024 - 4 mins read

Series: Appwrite with K3s

Learn how to seamlessly integrate Appwrite, an open-source platform, into your HomeLab setup using K3s. Follow step-by-step instructions to deploy K3s with Portainer, prepare Appwrite volumes, and configure miscellaneous services like MariaDB and InfluxDB. Utilize Kompose to convert Docker Compose files to Kubernetes for efficient deployment. Ensure smooth installation by mapping necessary environment variables and applying all required deployments and services. Finally, witness the successful deployment of Appwrite services and access the login page to start building scalable applications. Master the art of HomeLab application deployment with Appwrite and K3s.

Text-to-Image with StableDiffusionPipeline

February 10, 2024 - 3 mins read

In this post, we explore the capabilities of StableDiffusionPipeline for generating photorealistic images from textual inputs. We start with setting up the environment and installing necessary libraries. Then, we dive into Textual Inversion, demonstrating how the model learns new concepts from images. Image-to-Image transformations are also explored, showcasing the pipeline’s versatility. Additionally, we introduce Animagine XL 2.0, a model for high-resolution anime image creation, and provide sample code for its implementation. Lastly, we highlight Stable Diffusion XL, a powerful text-to-image model, and share a festive image generated using it.

Stable Diffusion: Text-to-Image Modeling Journey

February 3, 2024 - 3 mins read

This post explores Stable Diffusion, a latent text-to-image diffusion model in machine learning. Diffusion models, with forward, reverse, and sampling components, understand and generate patterns in datasets. Illustrating applications in image tasks, it introduces the process of installing and utilizing Stable Diffusion. The post details image generation and modification using prompts, with examples and troubleshooting. Notably, it shares an encounter with CUDA out-of-memory errors and the resolution through image resizing. Overall, it offers a comprehensive guide, combining theoretical insights with practical implementation steps in a professional manner.

OpenVINO, Optimum-Intel, CPU: An Exploration in Model Optimization

January 27, 2024 - 2 mins read

Explore the convergence of OpenVINO and Optimum-Intel in this post, where I detail the setup and execution of example code on my aging laptop. Focused on applying Quantization-aware Training and the Token Merging method to optimize the UNet model within the Stable Diffusion pipeline, this journey showcases the synergy of open-source tools for deep learning model deployment. Note that the provided code is tailored for CPU-based inference due to limitations in my aging GeForce graphics card, making it a valuable resource for users with similar hardware constraints. Dive into the world of optimized models and delightful Pokemon creation!

Java Integration with Jupyter Notebooks

January 21, 2024 - 4 mins read

Embark on a seamless integration of Java into Jupyter notebooks with this comprehensive guide. Beginning with the selection of a relevant Jupyter Docker Stack, the post details setup steps and deployment in HomeLab, showcasing application results for verification. The integration of Java Kernel through JBang and testing with “Hello World” and Apache Commons library exemplifies the versatility. Further exploration involves experimenting with Java in a Python kernel using JBang. Concluding with a call to joyful coding, this journey promises a harmonious blend of Java’s robustness and Jupyter’s interactive nature. Discover the joy of coding in this enriched Java-in-Jupyter experience. Happy coding!

Exploring Autogen Studio

January 14, 2024 - 3 mins read

In this exploration of Autogen Studio, we navigated through the AI landscape, harnessing the LM Studio API to compare responses from diverse language models. Employing the Mistral Instruct 7B model, we scrutinized prompts like Stock Price and Paint, visualizing outcomes and delving into key configurations. The post also offered insights into the primary assistant, model configuration, and agent workflows, accompanied by a comparative analysis of Mistral model responses. This comprehensive journey demystifies the power of Autogen Studio and its seamless integration with LM Studio API, providing practical guidance for AI enthusiasts.

Deploying LLMs with WasmEdge in HomeLab

January 13, 2024 - 2 mins read

In this post, we explored deploying Lightweight Language Models (LLMs) using WasmEdge, a high-performance WebAssembly runtime, within a HomeLab environment. The process involved preparing an OpenAI-compatible API server, configuring the Wasi-NN plugin, and deploying the setup to HomeLab using Kubernetes (K3s). The post also detailed the steps for testing the API server and integrating it into a Java application. Overall, the guide provides a comprehensive walkthrough of hosting and utilizing LLMs with WasmEdge in a local environment.

Integrating NFS for Improved Scalability

January 7, 2024 - 4 mins read

In this post, we explored the integration of NFS to enhance scalability in deploying LLM models within a home lab. Setting up NFS involved connecting to a TerraMaster NAS, and the K3s cluster was configured to dynamically provision storage. With NFS in place, the deployment process became more efficient, eliminating the need to rebuild images for each new model introduction. The post detailed the setup steps, from configuring NFS and K3s to deploying LLM models dynamically. This approach simplifies the scaling of Language Models, providing a centralized and scalable storage solution through NFS in a Kubernetes environment.