Skill Guide

Containerization and cloud deployment of GPU and CPU workloads using Docker, Kubernetes, and serverless

The practice of packaging applications and their dependencies into isolated containers and orchestrating their deployment across distributed compute resources (CPU/GPU) using tools like Docker and Kubernetes, or abstracting infrastructure management entirely via serverless platforms.

This skill enables organizations to achieve consistent, reproducible deployments across hybrid environments, drastically reducing 'it works on my machine' failures and scaling costs. It directly impacts business agility and operational efficiency by accelerating time-to-market for compute-intensive applications like AI/ML and data processing pipelines.

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How to Learn Containerization and cloud deployment of GPU and CPU workloads using Docker, Kubernetes, and serverless

Focus on core Docker concepts (Dockerfile, image layers, volumes, networking), fundamental Kubernetes objects (Pod, Deployment, Service, ConfigMap), and understanding the distinction between stateful and stateless workloads.

Master Kubernetes resource management (requests/limits, namespaces, RBAC), Helm chart templating, and CI/CD pipeline integration (e.g., GitOps with ArgoCD/Flux). Practice profiling and right-sizing GPU workloads using tools like `nvidia-smi` and Kubernetes device plugins. Avoid over-provisioning and neglecting network policies.

Architect multi-cluster federation (e.g., KubeFed, Rancher), implement sophisticated scheduling strategies for GPU-sharing (e.g., time-slicing, MIG with NVIDIA A100/H100), and design hybrid serverless/Kubernetes event-driven systems (e.g., KEDA + Knative). Focus on cost optimization across cloud providers and building robust platform engineering (PaaS) capabilities for internal teams.

Practice Projects

Beginner

Project

Containerize a Simple Python CPU-Bound Application

Scenario

You have a Python script that processes CSV files using pandas and numpy. It needs to run consistently on any developer's laptop and a cloud VM.

How to Execute

1. Write a Dockerfile using a multi-stage build (build stage with full Python, final slim stage). 2. Use `COPY` and `RUN pip install` to install dependencies from `requirements.txt`. 3. Expose a port and define an `ENTRYPOINT`. 4. Build, run locally with `docker run`, and push to a container registry (e.g., Docker Hub).

Intermediate

Project

Deploy a Stateful Machine Learning Model with GPU Access on Kubernetes

Scenario

A PyTorch model requires a GPU for inference and needs to load a 5GB model file from a persistent storage volume at startup.

How to Execute

1. Create a Dockerfile that installs CUDA toolkit and your ML framework. 2. Write a Kubernetes `Deployment` manifest specifying `resources.limits: nvidia.com/gpu: 1`. 3. Use a `PersistentVolumeClaim` (PVC) and `volumeMounts` to attach the model storage. 4. Deploy using `kubectl apply` and verify GPU allocation with `kubectl describe pod`.

Advanced

Project

Build a Hybrid Serverless/Kubernetes Data Processing Pipeline

Scenario

Design a system where a serverless function (AWS Lambda/Google Cloud Functions) is triggered by an event (e.g., new file in S3), which then dispatches a large-scale CPU data transformation job to a Kubernetes cluster (EKS/GKE) for cost-efficient batch processing.

How to Execute

1. Architect the event flow using a message broker (e.g., AWS SQS, Pub/Sub). 2. Write the serverless function to validate the event and push a job manifest to a Kubernetes queue. 3. Use KEDA (Kubernetes Event-Driven Autoscaling) to scale a `Job` or `Deployment` of worker pods based on the queue length. 4. Implement monitoring with Prometheus/Grafana to track latency and cost per event.

Tools & Frameworks

Software & Platforms

DockerKubernetesAWS EKS / GCP GKE / Azure AKSNVIDIA Container ToolkitHelm

Docker is the standard for container creation. Kubernetes is the industry-standard orchestrator for managing containerized workloads at scale. Managed K8s services (EKS, GKE, AKS) abstract control-plane management. The NVIDIA toolkit is essential for enabling GPU passthrough to containers. Helm is the package manager for defining, installing, and upgrading complex K8s applications.

Infrastructure & DevOps Tools

TerraformArgoCDKnativeKEDA

Terraform is used for provisioning the underlying cloud infrastructure (VPCs, clusters, node pools). ArgoCD enables GitOps for declarative, version-controlled deployments to Kubernetes. Knative provides a serverless runtime layer on top of Kubernetes. KEDA is critical for event-driven autoscaling of workloads, bridging serverless and Kubernetes paradigms.

Interview Questions

Answer Strategy

Structure the answer using Docker best practices: 1) Use an official NVIDIA CUDA base image. 2) Employ multi-stage builds to separate build dependencies from the runtime image. 3) Leverage Docker build cache by copying `requirements.txt` before application code. 4) Use specific version tags for all dependencies and base images. 5) Run as a non-root user. 6) Mention using `.dockerignore` to exclude unnecessary files.

Answer Strategy

This tests practical knowledge of Kubernetes scheduling and cloud economics. The answer should cover both technical and operational levers. Strategies include: 1) Implementing resource requests/limits correctly to enable bin-packing. 2) Using node affinity and taints/tolerations to control pod placement. 3) Exploring GPU time-slicing or MIG (Multi-Instance GPU) for sharing. 4) Right-sizing instance types and leveraging spot/preemptible instances for fault-tolerant workloads. 5) Using cluster autoscaler to scale down node pools during off-peak hours.