> ## Content Index > Fetch the complete content index at: https://devopscube.com/llms.txt > Use this file to discover other available public pages before exploring further. # How to Mount OCI Image as Volume in Kubernetes Pods - URL: https://devopscube.com/oci-image-volume-kubernetes-pods/ - Published: 2026-07-12T08:28:00.000Z - Updated: 2026-07-14T12:14:03.000Z - Author: Aswin Vijayan - Tags: #blog, Kubernetes, HOW TO GUIDES, #syntax-highlight In this guide, you will learn how to mount OCI container images as volumes in Kubernetes Pods using the **`ImageVolume`** feature. This feature is useful in ML projects that work with LLMs, as it allows you to package model data in OCI images and easily switch between different models. ## What is ImageVolume The Image Volume feature allows you to use OCI images as volumes directly within [Kubernetes pods](https://devopscube.com/kubernetes-pod/). In Kubernetes version 1.31, the [Image Volume feature](https://kubernetes.io/docs/tasks/configure-pod-container/image-volumes/?ref=devopscube.com) was introduced, and in Kubernetes version 1.36, the image volume feature was marked as a stable release and enabled by default. The below image gives you a high level overview of how OCI images are mounted to pods using the image volume feature. ![Mounting oci image as volume in Kubernetes pods](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/07/image-98.png) So what are OCI images? OCI images follow the rules set by [Open Container Initiative](https://opencontainers.org/?ref=devopscube.com). For example, [Docker](https://devopscube.com/what-is-docker/), [Podman](https://devopscube.com/podman-tutorial-beginners/), and container runtimes like containerd and **CRI-O** use OCI image rules. You can use the **`ImageVolume`** feature to store model files, binary files, configuration files, or other data in images which can be mounted to pods. Also, a key thing about this feature is that it is a **read only Volume.** ![Mount OCI Image as Volume in Kubernetes Pods](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2025/09/image-52.png) Now, lets get started with the practical example. ## How to Mount OCI Image as a Volume in Kubernetes Pods In this section, we will build an OCI image and a sample predictor application, then deploy them in the Kubernetes cluster to test the **`ImageVolume`** feature. Follow the below steps to mount an OCI image as a volume for a Kubernetes pod. 💡 If you just want to check this feature and you dont have an active cluster with v1.36, you can create a local cluster using [Kind](https://devopscube.com/kubernetes-kind-cluster-tutorial-setup-and-deploy-apps) or [Kubeadm](https://devopscube.com/setup-kubernetes-cluster-kubeadm/). ### Step 1: Build an OCI Image The first step is to build an OCI image. For this example, I am using a prediction model that I have locally. You can replace the model file with any file for testing. Here is the Dockerfile. ``` FROM scratch COPY model.pkl /models/model.pkl ``` You can see, unlike other Dockerfiles that build images on top of a base images, the OCI image is built using `FROM scratch`. I have uploaded this image to Docker Hub as `devopscube/oci-image:1.0`. You can use it directly for testing. ### Step 2: Build a Sample Predictor Application To test the `**ImageVolume**`, we will build a simple Python predictor application. This application loads the `model.pkl` file directly from the mounted image volume. I have already built the app and published it as `devopscube/predictor:1.0`, which you can use for testing. Here is the Python code that is part of the predictor image. ```python import os import joblib import numpy as np from fastapi import FastAPI from pydantic import BaseModel MODEL_PATH = os.environ.get("MODEL_PATH", "/models/model.pkl") model = joblib.load(MODEL_PATH) app = FastAPI() class PredictIn(BaseModel): instances: list @app.get("/healthz") def healthz(): return {"ok": True, "model_path": MODEL_PATH} @app.post("/v1/models/model:predict") def predict(p: PredictIn): X = p.instances try: X_arr = np.array(X, dtype=float) preds = model.predict(X_arr).tolist() except Exception: preds = model.predict(X).tolist() return {"predictions": preds} ``` ### Step 3: Mount the ImageVolume With Deployment Now, lets create a deployment with the predictor container image and the OCI volume image to test the image volume. Here are the Deployment and Service manifests. ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: predictor spec: replicas: 1 selector: matchLabels: app: predictor template: metadata: labels: app: predictor spec: containers: - name: app image: devopscube/predictor:1.0 ports: - containerPort: 8000 env: - name: MODEL_PATH value: /volume/models/model.pkl volumeMounts: - name: model-vol mountPath: /volume volumes: - name: model-vol image: reference: devopscube/oci-image:1.0 pullPolicy: IfNotPresent --- apiVersion: v1 kind: Service metadata: name: predictor-svc spec: selector: app: predictor ports: - port: 80 targetPort: 8000 ``` Deploy the above manifest and check if the pod is running without issues. ```bash $ kubectl get po NAME READY STATUS RESTARTS AGE predictor-5f8bb486d-jd7k2 1/1 Running 0 64s ``` Now, exec into the pod and check if the `model.pkl` file is inside the volume. ```bash $ kubectl exec -it predictor-5f8bb486d-jd7k2 -- ls /volume/models model.pkl ``` Now run the following command to port-forward the predictor service so that we can test the prediction endpoint. ```bash kubectl port-forward svc/predictor-svc 8080:80 ``` Now, use the following `curl` command from your workstation to send a prediction request to the predictor application. This will validate whether the application is able to access the `model.pkl` file from the **`ImageVolume`**. ```bash curl -X POST \ -H "Content-Type: application/json" \ -d '{ "instances": [ "sparrow", "elephant", "rose" ] }' \ "http://127.0.0.1:8080/v1/models/model:predict" ``` You will get the following output. ![](https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/6984c43d-842f-456e-a738-d847f8ded191/image.png?t=1756183770) This is the expected output, `0` means animal, `1` means bird, and `2` means plant. ## ImageVolume With subPath Lets say you have multiple models in different folders of the OCI image and you want to mount a specific model or want to mount multiple models to the pod. This is why `subPath` was introduced for ImageVolumes in Kubernetes v1.33. The image below shows how the subPath mounts a specific model to the pod. ![mounting a single directory of the image with subpath](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2026/07/image-99.png) And the volumeMounts section in the manifest will look like below. ```yaml env: - name: MODEL_PATH value: /volume/model.pkl volumeMounts: - name: model-vol mountPath: /volume subPath: models/prediction volumes: - name: model-vol image: reference: devopscube/oci-image:1.0 pullPolicy: IfNotPresent ``` In this way, you can use same image for multiple models and choose model path in the subPath. That’s a wrap! 🎉 ## Conclusion In this post, you learned about the **`ImageVolume`** feature in Kubernetes, built an OCI image, and tested it with a sample predictor ML application. This feature makes it easy to package data, tools , or models and switch between models without managing complex storage setups. Refer the [Kubernetes ML features](https://devopscube.com/kubernetes-ai-ml-features/) blog to know more about native ML support in Kubernetes. If you want to learn more Kubernetes concepts, look at our [Kubernetes tutorial](https://devopscube.com/kubernetes-tutorials-beginners/) blog. Try this feature and let me know how it goes!