Deploy NVIDIA NIM for Image OCR on Kubernetes#

You can deploy NVIDIA NIM for Image OCR on Kubernetes by using Helm.

The Helm chart simplifies NIM deployment on Kubernetes. The chart supports deployment with a variety of possible cluster, GPU, and storage configurations.

NIM microservices run on hosts with NVIDIA GPUs. The GPU model and count depend on the model. To use Helm, you must have a Kubernetes cluster with GPU nodes.

To simplify cluster administration for GPU driver installation, consider installing NVIDIA GPU Operator to manage the driver installation and lifecycle.

Benefits of Helm Chart Deployment#

The benefits of Helm Charts are as follows:

  • Help manage the deployment and lifecycle of the microservice.

  • Use a centralized distribution mechanism for charts. The charts are hosted on NVIDIA NGC.

  • Enable you to customize the deployment when you specify a custom values file.

Setting Up the Environment#

If you haven’t set up your NGC API key and do not know exactly which NIM you want to download and deploy, see the information in the User Guide.

This helm chart requires that you have a secret with your NGC API key configured for downloading private images, and one with your NGC API key (below named ngc-api). These will likely have the same key in it, but they will have different formats (dockerconfig.json vs opaque). See Creating Secrets below.

These instructions will assume that you have your NGC_API_KEY exported in the environment.

export NGC_API_KEY="<YOUR NGC API KEY>"

Fetching the Helm Chart#

You can fetch the helm chart from NGC by executing the following command:

helm fetch https://helm.ngc.nvidia.com/nim/baidu/charts/nvidia-nim-paddleocr-1.2.0.tgz --username='$oauthtoken' --password=$NGC_API_KEY

Namespace#

You can choose to deploy to whichever namespace is appropriate, but for documentation purposes we will deploy to a namespace named paddleocr-nim.

kubectl create namespace paddleocr-nim

Creating Secrets#

Use the following procedure to create the secrets for this chart.

  1. Add an image pull secret for downloading the container image from NVIDIA NGC.

    kubectl create secret -n paddleocr-nim docker-registry ngc-secret \
 --docker-server=nvcr.io \
 --docker-username='$oauthtoken' \
 --docker-password="${NGC_API_KEY}"

  2. Add a generic secret that the container uses for downloading models from NVIDIA NGC.

    kubectl create -n paddleocr-nim secret generic ngc-api --from-literal=NGC_API_KEY=${NGC_API_KEY}

Configuration Considerations#

The following deployment commands will by default create a single deployment with one replica using the paddleocr model. The following options can be used to make modifications to the behavior. See Parameters for a description of the Helm parameters.

  • image.repository – The container (paddleocr) to deploy.

  • image.tag – The version of that container (1.2.0).

  • Storage options, based on the environment and cluster in use.

  • resources – Use this option when a model requires more than the default of one GPU. See below for support matrix and resource requirements.

  • env – Which is an array of environment variables presented to the container, if advanced configuration is needed.

Storage#

This NIM uses persistent storage for storing downloaded models. These instructions require that you have a local-nfs storage class provisioner installed in your cluster.

helm repo add nfs-ganesha-server-and-external-provisioner https://kubernetes-sigs.github.io/nfs-ganesha-server-and-external-provisioner/
helm install nfs-server nfs-ganesha-server-and-external-provisioner/nfs-server-provisioner --set storageClass.name=local-nfs

Advanced Storage Configuration#

Storage is a particular concern when setting up NIMs. Models can be quite large, and you can fill a disk downloading things to emptyDirs or other locations around your pod image. We recommend that you mount persistent storage of some kind on your pod.

This chart supports two general categories:

  1. Persistent Volume Claims (enabled with persistence.enabled)

  2. hostPath (enabled with persistences.hostPath)

By default, the chart uses the standard storage class and creates a PersistentVolume and a PersistentVolumeClaim.

If you do not have a Storage Class Provisioner that creates PersistentVolume automatically, set the value persistence.createPV=true. This is also necessary when you use persistence.hostPath on minikube.

If you have an existing PersistentVolumeClaim where you’d like the models to be stored at, pass that value in at persistence.existingClaimName.

See the Helm options in Parameters.

Deploying#

Use the following command to create a basic deployment.

helm upgrade --install \
  --namespace paddleocr-nim \
  image-ocr \
  --set persistence.class="local-nfs" \
  nvidia-nim-paddleocr-1.2.0.tgz

You can also change the version of the model in use by adding the following after --namespace.

--set image.tag=1.2.0 \

After deploying check the pods to ensure that it is running, initial image pull and model download can take upwards of 15 minutes.

kubectl get pods -n paddleocr-nim

The pod should eventually end up in the running state.

NAME              READY   STATUS    RESTARTS   AGE
nvidia-nim-paddleocr-0   1/1     Running   0          8m44s

Check events for failures:

kubectl get events -n paddleocr-nim

Running Inference#

In the previous example the API endpoint is exposed on port 8000 through the Kubernetes service of the default type with no ingress, since authentication is not handled by the NIM itself. The following commands require that the nvidia/paddleocr model has been deployed.

If required, change the “model” value in the request JSON body to use a different model.

Use the following command to port-forward the service to your local machine to test inference.

kubectl port-forward -n paddleocr-nim service/image-ocr-nvidia-nim-paddleocr 8000:8000

Create a directory data/structured-imgs and copy in some .png formatted images so that data looks like so:

$ mkdir -p data/structured-imgs

$ ls -l data/structured-imgs
sample1.png
sample2.png
sample3.png
sample4.png

Send an inference request by running the following commands and Python 3.11 script.

# Create a virtual env (venv) for this test to isolate the dependencies
python3 -m venv paddleocr_venv
source paddleocr_venv/bin/activate

# Install pillow and requests libraries into your python 3 environment
pip3 install requests pillow

# paddleocr_inference_test.py
import base64
import json
import time
from io import BytesIO
from pathlib import Path

import requests
from PIL import Image

images = []
image_paths = list(Path("data/structured-imgs").glob("*"))
for image_path in image_paths:
    image = Image.open(image_path)
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    base64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
    image_url = f"data:image/png;base64,{base64_image}"
    image = {"type": "image_url", "url": image_url}
    images.append(image)

payload = {"input": images}

start = time.time()
print(json.dumps(requests.post("http://localhost:8000/v1/infer", json=payload).json()))
print(f"{len(image_paths)} images completed in {time.time() - start} seconds")


# Run inference on the .png files in the /data directory
python3 paddleocr_inference_test.py

Viewing Log Messages#

Use the following command to view the container log messages in the docker logs.

kubectl logs -f nvidia-nim-paddleocr-0
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