Discover a streamlined approach to GitLab installation, repository migration, and CI/CD optimization. This comprehensive guide covers setting up GitLab in your home lab, upgrading to the latest version, configuring external URLs, and migrating repositories. Learn how to install GitLab Runner, Docker, and seamlessly integrate them into your development workflow. The guide concludes with optional steps for registering GitLab Runner as a service. Elevate your version control and continuous integration experience with this concise and practical tutorial.

In this post, I expand my Home Lab by adding a perpetual LLAMA model for on-demand inferencing. The steps involve crafting a Dockerfile, packaging Microsoft's Phi2 model, and deploying it with K3S. This ensures a continuously accessible LLAMA server for seamless integration into various inferencing projects.

Embarking on my journey with vLLM, I explore its potential for streamlined Large Language Model (LLM) inference and deployment. The blog details my personal experience setting up vLLM on a Windows Subsystem for Linux (WSL) instance running Ubuntu 22.04. I meticulously guide through installing WSL, NVIDIA GPU drivers, CUDA Toolkit, and Docker for efficient utilization. Delving into vLLM setup within the NVIDIA PyTorch Docker image, I navigate through the installation process and launch the API server. The blog provides insights into querying the model and creating a Docker image snapshot, offering a comprehensive guide to efficient language model serving.

In my latest blog post, I share my journey setting up K3S, a lightweight Kubernetes distribution, in my home lab. With a step-by-step guide, I install K3S on an Ubuntu Server 22.04.2 LTS, offering a seamless experience. The post covers creating useful aliases for simplifying interactions with K3S and verifying the installation. Additionally, I introduce Portainer to manage Docker and Kubernetes in my home lab. I walk through setting up Portainer, adding a Kubernetes environment, and connecting it to the K3S cluster. Furthermore, I establish a local Docker registry and demonstrate optional steps for pushing and deploying Docker images within the K3S cluster.

After completing the Generative AI with Large Language Models course, I'm thrilled to share my Dockerized experience running the LLaMA model. The guide covers setting up the project structure, creating a FastAPI application, and Dockerizing it. Additionally, I showcase building an AI chatbot, integrating it with FastAPI, HuggingFace embeddings, and LLaMA. The Docker environment loads the LLM and allows seamless interactions with PDFs. I conclude by enhancing performance with OpenBLAS, significantly reducing inferencing time. Explore the power of LLaMA Server in a Docker container for transformative AI experiences! 🚀

I recently spent 70 minutes setting up a Kubernetes Cluster on Raspberry Pi using Ubuntu Server 20.04 LTS. After burning the OS image and configuring a headless setup, I updated the OS, changed the hostname, and enabled memory cgroup. The Docker installation involved setting up external storage and configuring Docker daemon. Installing kubeadm and creating the Kubernetes cluster took an additional 45 minutes. I verified the cluster status, installed networking addons (Calico), and added leaf nodes. Troubleshooting included resolving conntrack and socat issues. Overall, the Raspberry Pi Kubernetes Cluster provides full control over Docker container orchestration.

Setting up Docker on my Raspberry Pi Cluster took just 15 minutes. After installing Docker, I added the ubuntu user to the Docker group. Configuring Docker included adjusting the daemon settings for external storage. Testing Docker with a hello-world container went smoothly. To use local images for MicroK8s, I exported and injected the image successfully. Troubleshooting involved resolving daemon start errors, addressing connection issues, and handling permission errors. Formatting the existing NTFS HDD to ext4 and adjusting boot-up settings resolved challenges, making Docker work seamlessly on my Raspberry Pi Cluster.