Introduction to Helm Charts

A Helm chart is a package that simplifies the deployment and management of applications on Kubernetes. It bundles together all the necessary Kubernetes resources (like Deployments, Services, ConfigMaps, etc.) and their configurations into a single, reusable unit. Think of it as a way to package and share pre-configured applications for Kubernetes, making deployments easier and more consistent.

It is a powerful package manager for Kubernetes, designed to simplify the deployment and management of applications on Kubernetes clusters. Just like apt for Ubuntu or yum for CentOS, Helm enables you to define, install, and upgrade even the most complex Kubernetes applications through a consistent and repeatable process.

Brief History of Helm Charts

Helm was initially created by a company called Deis (acquired by Microsoft) in 2015 and was later donated to the Cloud Native Computing Foundation (CNCF), the same foundation that maintains Kubernetes. Helm v2 introduced the concept of Tiller, a server-side component, which was removed in Helm v3 due to security concerns. Helm v3, the current major version, offers a client-only architecture and has become the de facto standard for deploying applications on Kubernetes.

Running AI Workloads Using Helm Charts

Running AI workloads like deep learning models or large-scale data processing tasks on Kubernetes can be complex due to the number of components involved (e.g., GPUs, storage, job schedulers, monitoring tools). Helm charts simplify this process by packaging everything needed into a single deployable unit.

To run AI workloads:

– Use GPU-enabled Kubernetes nodes (e.g., using NVIDIA device plugin).
– Deploy Helm Charts for AI frameworks like TensorFlow Serving, PyTorch, Triton Inference Server, Kubeflow, or MLFlow.
– Customize values.yaml files to configure resources like number of replicas, GPU allocation, environment variables, model paths, etc.
– Use Helm commands like helm install or helm upgrade to deploy or update workloads easily.

For example, to deploy NVIDIA’s Triton Inference Server:

$ helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
$ helm install triton nvidia/triton-inference-server -f custom-values.yaml

Benefits of Using Helm Charts

Some of the benefits of using Helm Charts are as follows:

Simplified Deployment: Manage complex Kubernetes apps with a single chart.

Version Control: Charts can be versioned for consistent deployments across environments.

Customization: values.yaml allows fine-tuned control over configurations.

Reusable: Helm Charts can be shared across teams and organizations.

Scalable: Easily scale AI workloads using Helm parameters and Kubernetes features.

Ecosystem Support: Many popular AI/ML tools have official Helm charts available.

Examples

Some of the examples are as follows:

Deploying JupyterHub: Use the JupyterHub Helm Chart to provide multi-user notebook access on Kubernetes.

$ helm repo add jupyterhub https://jupyterhub.github.io/helm-chart/

$ helm upgrade –install jhub jupyterhub/jupyterhub –values config.yaml

Kubeflow Deployment: Kubeflow, a full-stack ML platform, provides Helm charts for various components like Katib (hyperparameter tuning), pipelines, and notebooks.

MLflow: MLflow tracking server can be deployed using Helm to manage experiments and models.

 

Helm Charts act like blueprints of Kubernetes resources, bundling together all configuration files required to run an application or workload.