├── .gitignore ├── .python-version ├── Dockerfile ├── Makefile ├── README.md ├── api.py ├── kind.yaml ├── manifests ├── deployment.yaml └── service.yaml ├── pyproject.toml └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | # Python-generated files 2 | __pycache__/ 3 | *.py[oc] 4 | build/ 5 | dist/ 6 | wheels/ 7 | *.egg-info 8 | 9 | # Virtual environments 10 | .venv 11 | -------------------------------------------------------------------------------- /.python-version: -------------------------------------------------------------------------------- 1 | 3.12 2 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # An example using multi-stage image builds to create a final image without uv. 2 | 3 | # First, build the application in the `/app` directory. 4 | # See `Dockerfile` for details. 5 | FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder 6 | ENV UV_COMPILE_BYTECODE=1 UV_LINK_MODE=copy 7 | 8 | # Disable Python downloads, because we want to use the system interpreter 9 | # across both images. If using a managed Python version, it needs to be 10 | # copied from the build image into the final image; see `standalone.Dockerfile` 11 | # for an example. 12 | ENV UV_PYTHON_DOWNLOADS=0 13 | 14 | WORKDIR /app 15 | RUN --mount=type=cache,target=/root/.cache/uv \ 16 | --mount=type=bind,source=uv.lock,target=uv.lock \ 17 | --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ 18 | uv sync --frozen --no-install-project --no-dev 19 | ADD . /app 20 | RUN --mount=type=cache,target=/root/.cache/uv \ 21 | uv sync --frozen --no-dev 22 | 23 | 24 | # Then, use a final image without uv 25 | FROM python:3.12-slim-bookworm 26 | # It is important to use the image that matches the builder, as the path to the 27 | # Python executable must be the same, e.g., using `python:3.11-slim-bookworm` 28 | # will fail. 29 | 30 | # Copy the application from the builder 31 | COPY --from=builder --chown=app:app /app /app 32 | 33 | # Place executables in the environment at the front of the path 34 | ENV PATH="/app/.venv/bin:$PATH" 35 | 36 | # Run the FastAPI application by default 37 | CMD ["fastapi", "run", "--host", "0.0.0.0", "--port", "5000", "/app/api.py"] -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | ######################################################################## 2 | # Kubernetes cluster operations (MLOps) 3 | ######################################################################## 4 | # create the local Kubernetes cluster 5 | export CLUSTER_NAME=cluster-789 6 | 7 | cluster: 8 | kind create cluster --config kind.yaml --name $(CLUSTER_NAME) 9 | kubectl config use-context kind-$(CLUSTER_NAME) 10 | 11 | @echo "Listing the nodes in the cluster" 12 | kubectl get nodes 13 | 14 | # delete the local Kubernetes cluster 15 | delete-cluster: 16 | kind delete cluster --name $(CLUSTER_NAME) 17 | 18 | # list Docker images registered in the local cluster 19 | list-images: 20 | docker exec -it $(CLUSTER_NAME)-control-plane crictl images 21 | 22 | ######################################################################## 23 | # ML engineer operations (ML) 24 | ######################################################################## 25 | export PORT=5005 26 | 27 | # run with hot reloading for development 28 | dev: 29 | uv run fastapi dev api.py --port $(PORT) 30 | 31 | # build the Docker image for our api 32 | build: 33 | docker build -t simple-api:v1.0.0 . 34 | 35 | # run the Docker container for our api as a standalone docker in your machine 36 | run: 37 | docker run -it -p $(PORT):5000 simple-api:v1.0.0 38 | 39 | # push the Docker image to the local Kubernetes image registry 40 | push: 41 | kind load docker-image simple-api:v1.0.0 --name $(CLUSTER_NAME) 42 | 43 | # deploy the Docker image to the local Kubernetes cluster 44 | deploy: build push 45 | kubectl apply -f manifests/deployment.yaml 46 | kubectl apply -f manifests/service.yaml 47 | kubectl wait --for=condition=ready pod -l app=simple-api --timeout=60s 48 | kubectl port-forward svc/simple-api $(PORT):5000 49 | 50 | # ping the api to check if it's running 51 | test: 52 | curl http://localhost:$(PORT)/health -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Kubernetes for ML Engineers 2 | 3 | #### Table of contents 4 | 5 | - [What is this repo about?](#what-is-this-repo-about) 6 | - [Steps](#steps) 7 | - [1. Install the tools](#1-install-the-tools) 8 | - [2. Create a local Kubernetes cluster](#2-create-a-local-kubernetes-cluster) 9 | - [3. Write the business logic of your app](#3-write-the-business-logic-of-your-app) 10 | - [4. Containerize your app with Docker](#4-containerize-your-app-with-docker) 11 | - [5. Build the Docker image and run it locally](#5-build-the-docker-image-and-run-it-locally-optional) 12 | - [6. Push the Docker image to the local Kubernetes cluster](#6-push-the-docker-image-to-the-local-kubernetes-cluster) 13 | - [7. Deploy the app as a Kubernetes service](#7-deploy-the-app-as-a-kubernetes-service) 14 | - [8. Test it works](#8-test-it-works) 15 | - [9. Run the whole thing in one go](#9-run-the-whole-thing-in-one-go) 16 | - [Wanna learn more Real World ML/MLOps?](#wanna-learn-more-real-world-mlmlops) 17 | 18 | 19 | ### What is this repo about? 20 | This is a step by step guide to help you understand the basics of Kubernetes. 21 | 22 | You will learn how to build and deploy a containerized app (in this case, a simple FastAPI app) 23 | to a Kubernetes cluster. 24 | 25 | Because I want you to become a Real World ML/MLOps Ninja. 26 | 27 | Let's get started. 28 | 29 | ### Steps 30 | 31 | ### 1. Install the tools 32 | 33 | - [uv](https://docs.astral.sh/uv/getting-started/installation/) to create the project and manage the dependencies. 34 | - [Docker](https://docs.docker.com/get-docker/) to build and run docker images, including the nodes of the `kind` cluster. 35 | - [Kind](https://kind.sigs.k8s.io/docs/user/quick-start/) to create a local Kubernetes cluster. 36 | - [kubectl](https://kubernetes.io/docs/tasks/tools/) to interact with the Kubernetes cluster. 37 | 38 | ### 2. Create a local Kubernetes cluster 39 | 40 | We will use `kind` to create a local Kubernetes cluster. It will be a simple cluster that 41 | will run entirely on your machine, using as Kubernetes nodes simple Docker containers. 42 | 43 | A local cluster like the one we are creating here is useful for development and CI pipelines, 44 | where you need a minimal cluster to run integration tests for your applications. 45 | 46 | > **What about production-ready clusters?** 47 | > 48 | > Production clusters typically consist of multiple nodes, running in a cloud provider, or 49 | > a private data center. 50 | > 51 | > Production cluster creation, configuration, and maintenance is something you won't be doing in your day to day as ML Engineer. This is something that you either: 52 | > 53 | > - pay your cloud provider to do it for you, like when you use AWS EKS, Google GKE, Azure AKS, etc 54 | > or 55 | > - hire a top-notch engineer like **[Marius](https://lluminarr.com/)** to do it for you, so 56 | > you get a solution that is cost-effective and scalable. 57 | 58 | We will create a cluster consisting of 59 | - 1 control plane node -> where the core Kubernetes components run 60 | - 2 worker nodes -> where the apps we will deploy will run. 61 | 62 | The configuration file for the cluster is the following: 63 | 64 | ```yaml 65 | # kind.yaml 66 | kind: Cluster 67 | apiVersion: kind.x-k8s.io/v1alpha4 68 | nodes: 69 | - role: control-plane 70 | kubeadmConfigPatches: 71 | - | 72 | kind: InitConfiguration 73 | nodeRegistration: 74 | kubeletExtraArgs: 75 | node-labels: "CriticalAddonsOnly=true,eks-k8s-version=1.29" 76 | 77 | - role: worker 78 | kubeadmConfigPatches: 79 | - | 80 | kind: JoinConfiguration 81 | nodeRegistration: 82 | kubeletExtraArgs: 83 | node-labels: "CriticalAddonsOnly=true,eks-k8s-version=1.29" 84 | 85 | - role: worker 86 | labels: 87 | "CriticalAddonsOnly": "true" 88 | "eks-k8s-version": "1.29" 89 | ``` 90 | 91 | Create the cluster with the name you want (e.g. `cluster-123`) using the above configuration: 92 | ```bash 93 | kind create cluster --config kind.yaml --name cluster-123 94 | ``` 95 | 96 | Set the kubectl context to the cluster we just created, so you can interact with the cluster using `kubectl`: 97 | 98 | ```bash 99 | kubectl config use-context kind-cluster-123 100 | ``` 101 | 102 | Get the list of nodes in the cluster: 103 | 104 | ```bash 105 | kubectl get nodes 106 | 107 | NAME STATUS ROLES AGE VERSION 108 | cluster-123-control-plane Ready control-plane 15m v1.32.2 109 | cluster-123-worker Ready 14m v1.32.2 110 | cluster-123-worker2 Ready 14m v1.32.2 111 | ``` 112 | 113 | Voila! You have a local Kubernetes cluster running on your machine. 114 | 115 | Let's now move on to the ML engineering work. 116 | 117 | #### 3. Write the business logic of your app 118 | 119 | In this case, we will create a simple FastAPI app that returns the current time when you hit the `/health` endpoint. 120 | 121 | We will use `uv` to create the project, which is the most ergonomic way to create and package your Python code. 122 | 123 | 1. Create the boilerplate code with: 124 | ```bash 125 | uv init simple-api 126 | ``` 127 | 128 | 2. Add FastAPI to the project: 129 | ```bash 130 | uv add fastapi --extra standard 131 | ``` 132 | 133 | 3. Rename the `hello.py` file to `api.py` and copy this code: 134 | ```python 135 | from fastapi import FastAPI 136 | from datetime import datetime 137 | 138 | app = FastAPI() 139 | 140 | @app.get('/health') 141 | async def health(): 142 | return { 143 | 'status': 'healthy', 144 | 'timestamp': datetime.now().isoformat() 145 | } 146 | ``` 147 | 148 | Feel free to adjust this code to your needs. 149 | 150 | #### 4. Containerize your app with Docker 151 | 152 | We write a [multi-stage Dockerfile](./Dockerfile) to reduce the final image size. 153 | Change the python version to that one in your .venv environment. 154 | 155 | It has 2 stages: 156 | 157 | - builder -> where we install the project dependencies with `uv` and copy the code 158 | - runner -> where we run the FastAPI app 159 | 160 | 161 | #### 5. Build the Docker image and run it locally (optional) 162 | 163 | To build the image, run the following command: 164 | 165 | ```bash 166 | docker build -t simple-api:v1.0.0 . 167 | ``` 168 | 169 | And to run it locally, run the following command: 170 | 171 | ```bash 172 | docker run -it -p 5005:5000 simple-api:v1.0.0 173 | ``` 174 | Observe how we forward the container's port 5000 to the host's port 5005. 175 | 176 | At this point, you should be able to hit the `/health` endpoint at `http://localhost:5005/health` and get the current time. 177 | 178 | ```bash 179 | curl http://localhost:5005/health 180 | ``` 181 | 182 | Congratulations! You have just built and run a Docker container locally. 183 | 184 | Let's now take things to the next level and run it in a Kubernetes cluster. 185 | 186 | 187 | #### 6. Push the Docker image to the local Kubernetes cluster 188 | 189 | Before we can deploy our app to the cluster, we need to push the Docker image to the local Kubernetes cluster. 190 | 191 | To do that, we will use the `kind` CLI to load the image into the cluster. 192 | 193 | ```bash 194 | kind load docker-image simple-api:v1.0.0 --name cluster-123 195 | ``` 196 | 197 | #### 7. Deploy the app as a Kubernetes service 198 | 199 | Now that we have the image in the cluster, we can deploy the app as a Kubernetes service. 200 | 201 | We will need to create 2 resources: 202 | 203 | - a [deployment.yaml](./deployment.yaml) -> which will define the pods that will run the app. In our case, we will have 3 replicas of the app. 204 | - a [service.yaml](./service.yaml) -> which will define how to access the app from outside the cluster 205 | 206 | Don't worry about the manifests for now. Kubernetes YAML files are notoriously verbose and hard to read. 207 | And if you are scared of them, you are not alone. I am scared of them too. 208 | 209 | To deploy the app, we use the `kubectl` CLI to apply the Kubernetes manifests: 210 | 211 | ```bash 212 | kubectl apply -f deployment.yaml 213 | kubectl apply -f service.yaml 214 | ``` 215 | 216 | You can check the status of the deployment with: 217 | 218 | ```bash 219 | kubectl get pods 220 | 221 | NAME READY STATUS RESTARTS AGE 222 | simple-api-7f4bbc478b-f7wdx 1/1 Running 0 5m26s 223 | simple-api-7f4bbc478b-fjx2m 1/1 Running 0 5m26s 224 | simple-api-7f4bbc478b-gfntx 1/1 Running 0 5m26s 225 | ``` 226 | 227 | 228 | #### 8. Test it works 229 | 230 | To test that the app is working, we can use the `kubectl` CLI to port-forward the service to our local machine: 231 | 232 | ```bash 233 | kubectl port-forward svc/simple-api 5005:5000 234 | ``` 235 | 236 | And then we can hit the `/health` endpoint at `http://localhost:5005/health` and get the current time. 237 | 238 | ```bash 239 | curl http://localhost:5005/health 240 | 241 | {"status":"healthy","timestamp":"2025-02-21T15:25:55.445524"} 242 | ``` 243 | 244 | #### 9. Run the whole thing in one go 245 | 246 | ```bash 247 | # Create the Kubernetes cluster 248 | make cluster 249 | 250 | # Deploy the FastAPI app to the cluster 251 | make deploy 252 | 253 | # Test connecting to the app 254 | make test 255 | ``` 256 | 257 | CONGRATULATIONS! You have just deployed a FastAPI app to a local Kubernetes cluster. 258 | 259 | The sky is the limit from here. 260 | 261 | ### Wanna learn more Real World ML/MLOps? 262 | 263 | Subscribe for free to my newsletter to get notified when I publish new articles and courses: 264 | ### [👉👉🏻👉🏼👉🏽👉🏾👉🏿 Subscribe](https://paulabartabajo.substack.com/) 265 | 266 | ### [👉👉🏻👉🏼👉🏽👉🏾👉🏿 Courses](https://realworldml.net/courses) 267 | -------------------------------------------------------------------------------- /api.py: -------------------------------------------------------------------------------- 1 | from fastapi import FastAPI 2 | from datetime import datetime 3 | 4 | app = FastAPI() 5 | 6 | @app.get('/health') 7 | async def health(): 8 | return { 9 | 'status': 'healthy', 10 | 'timestamp': datetime.now().isoformat() 11 | } -------------------------------------------------------------------------------- /kind.yaml: -------------------------------------------------------------------------------- 1 | kind: Cluster 2 | apiVersion: kind.x-k8s.io/v1alpha4 3 | nodes: 4 | - role: control-plane 5 | kubeadmConfigPatches: 6 | - | 7 | kind: InitConfiguration 8 | nodeRegistration: 9 | kubeletExtraArgs: 10 | node-labels: "CriticalAddonsOnly=true,eks-k8s-version=1.29" 11 | 12 | - role: worker 13 | kubeadmConfigPatches: 14 | - | 15 | kind: JoinConfiguration 16 | nodeRegistration: 17 | kubeletExtraArgs: 18 | node-labels: "CriticalAddonsOnly=true,eks-k8s-version=1.29" 19 | 20 | - role: worker 21 | labels: 22 | "CriticalAddonsOnly": "true" 23 | "eks-k8s-version": "1.29" -------------------------------------------------------------------------------- /manifests/deployment.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: apps/v1 2 | kind: Deployment 3 | metadata: 4 | name: simple-api 5 | labels: 6 | app: simple-api 7 | spec: 8 | replicas: 3 9 | selector: 10 | matchLabels: 11 | app: simple-api 12 | template: 13 | metadata: 14 | labels: 15 | app: simple-api 16 | spec: 17 | containers: 18 | - name: simple-api 19 | image: simple-api:v1.0.0 20 | imagePullPolicy: Never # Use the local image 21 | ports: 22 | - containerPort: 5000 23 | livenessProbe: 24 | httpGet: 25 | path: /health 26 | port: 5000 27 | initialDelaySeconds: 5 28 | periodSeconds: 10 29 | readinessProbe: 30 | httpGet: 31 | path: /health 32 | port: 5000 33 | initialDelaySeconds: 5 34 | periodSeconds: 10 -------------------------------------------------------------------------------- /manifests/service.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: v1 2 | kind: Service 3 | metadata: 4 | name: simple-api 5 | spec: 6 | selector: 7 | app: simple-api 8 | ports: 9 | - protocol: TCP 10 | port: 5000 11 | targetPort: 5000 12 | type: NodePort -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | 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