1. Project Overview kind is a tool for managing Kubernetes clusters using containers. Project address https://github.com/kubernetes-sigs/kind . It is mainly used for: Local development environments Temporary environments for learning Automated testing 2. Installing kind macOS 1 brew install kind Linux 1 2 curl -Lo /usr/local/bin/kind https://kind.sigs.k8s.io/dl/v0.21.0/kind-linux-amd64 chmod +x /usr/local/bin/kind 3. Creating a kind Cluster If you have a PROXY configured locally, it is recommended to reset the environment variables before creating:

1. dcgm-exporter dcgm-exporter is the GPU monitoring tool provided by the official NVIDIA community. Project address https://github.com/NVIDIA/dcgm-exporter 1.1 Installation Add the Helm chart repository 1 helm repo add gpu-helm-charts https://nvidia.github.io/dcgm-exporter/helm-charts 1 helm repo update Install 1 2 3 4 5 helm install dcgm-exporter gpu-helm-charts/dcgm-exporter --namespace monitor --create-namespace \ --set serviceMonitor.enabled=false \ --set image.

1. Compiling Models with TensorRT-LLM 1.1 Introduction to TensorRT-LLM When using TensorRT, you usually need to convert the model to ONNX format, then convert the ONNX to TensorRT format, and finally run inference in TensorRT or Triton Server. But this conversion process is not simple, and you often run into

1. What Is nvidia-smi nvidia-smi stands for NVIDIA System Management Interface, the interface NVIDIA provides for managing and monitoring GPUs. nvidia-smi calls NVML. NVML stands for NVIDIA Management Library, a library that provides a set of C APIs for NVIDIA GPU monitoring and management. 1.1 Queryable State ECC error counts GPU utilization Active compute processes Clocks and PState Temperature and fan speed Power management Hardware identification 1.

1. Introduction to Fluid Below is the Fluid architecture diagram from https://github.com/fluid-cloudnative/fluid: Fluid abstracts two concepts: Dataset, a collection of data, the abstraction from the user’s point of view Runtime, the abstraction of the real services such as data storage and acceleration Fluid mainly solves the problems of using traditional caching systems on Kubernetes: