In this tutorial, we will demonstrate how to leverage the open-source software SkyPilot to help GKE customers efficiently obtain accelerators across regions, ensuring workload continuity and optimized resource utilization.
This guide illustrates the deployment of Flyte on Google Kubernetes Engine (GKE) using Helm, utilizing Google Cloud Storage for scalable data storage and Cloud SQL PostgreSQL for a reliable metadata store. By the end of this tutorial, you will have a fully functional Flyte instance on GKE, offering businesses seamless integration with the GCP ecosystem, improved resource efficiency, and cost-effectiveness.
This guide demonstrates how to deploy a Hugging Face Text Generation Inference (TGI) server on Google Kubernetes Engine (GKE) using NVIDIA L4 GPUs, enabling you to serve large language models like Mistral-7b-instruct. It walks you through creating a GKE cluster, deploying the TGI application, sending prompts to the model, and monitoring the service’s performance using metrics, while also providing instructions for cleaning up the cluster.
This tutorial will guide you step by step through the process of installing KServe in a GKE Autopilot cluster.
This tutorial will provide instructions on how to deploy and use the Metaflow framework on GKE (Google Kubernetes Engine) and operate AI/ML workloads using Argo-Workflows.
In this tutorial we will fine-tune gemma-2-9b using LoRA as an experiment in MLFlow. We will deploy MLFlow on a GKE cluster and set up MLFlow to store artifacts inside a GCS bucket. In the end, we will deploy a fine-tuned model using KServe.
This tutorial expands on the SkyPilot Tutorial by leveraging Dynamic Workload Scheduler with the help of an open-source project called Kueue
In this tutorial, we will demonstrate how to leverage the open-source software SkyPilot to help GKE customers efficiently obtain accelerators across regions, ensuring workload continuity and optimized resource utilization.