NVIDIA TensorRT Inference Server

NVIDIA TensorRT Inference Server is a REST and GRPC service for deep-learning inferencing of TensorRT, TensorFlow and Caffe2 models. The server is optimized deploy machine and deep learning algorithms on both GPUs and CPUs at scale.

These instructions detail how to set up a GKE cluster suitable for running the NVIDIA TensorRT Inference Server and how to use the io.ksonnet.pkg.nvidia-inference-server prototype to generate Kubernetes YAML and deploy to that cluster.

For more information on the NVIDIA TensorRT Inference Server see the NVIDIA TensorRT Inference Server User Guide and the NVIDIA TensorRT Inference Server Clients open-source repository.

Setup

Please refer to the guide to deploying Kubeflow on GCP.

NVIDIA TensorRT Inference Server Image

The docker image for the NVIDIA TensorRT Inference Server is available on the NVIDIA GPU Cloud. Below you will add a Kubernetes secret to allow you to pull this image. As initialization you must first register at NVIDIA GPU Cloud and follow the directions to obtain your API key. You can confirm the key is correct by attempting to login to the registry and checking that you can pull the inference server image. See Pull an Image from a Private Registry for more information about using a private registry.

$ docker login nvcr.io
Username: $oauthtoken
Password: <your-api-key>

Now use the NVIDIA GPU Cloud API key from above to create a kubernetes secret called ngc. This secret allows Kubernetes to pull the inference server image from the NVIDIA GPU Cloud registry. Replace with your API key and with your NVIDIA GPU Cloud email. Make sure that for docker-username you specify the value exactly as shown, including the backslash.

$ kubectl create secret docker-registry ngc --docker-server=nvcr.io --docker-username=\$oauthtoken --docker-password=<api-key> --docker-email=<ngc-email>

Model Repository

The inference server needs a repository of models that it will make available for inferencing. You can find an example repository in the open-source repo and instructions on how to create your own model repository in the NVIDIA Inference Server User Guide.

For this example you will place the model repository in a Google Cloud Storage bucket.

$ gsutil mb gs://inference-server-model-store

Following these instructions download the example model repository to your system and copy it into the GCS bucket.

$ gsutil cp -r model_store gs://inference-server-model-store

Kubernetes Generation and Deploy

Next use ksonnet to generate Kubernetes configuration for the NVIDIA TensorRT Inference Server deployment and service. The –image option points to the NVIDIA Inference Server container in the NVIDIA GPU Cloud Registry. For the current implementation you must use the 18.08.1 container. The –modelRepositoryPath option points to our GCS bucket that contains the model repository that you set up earlier.

$ ks init my-inference-server
$ cd my-inference-server
$ ks registry add kubeflow https://github.com/kubeflow/kubeflow/tree/master/kubeflow
$ ks pkg install kubeflow/nvidia-inference-server
$ ks generate nvidia-inference-server iscomp --name=inference-server --image=nvcr.io/nvidia/inferenceserver:18.08.1-py2 --modelRepositoryPath=gs://inference-server-model-store/tf_model_store

Next deploy the service.

$ ks apply default -c iscomp

Using the TensorRT Inference Server

Now that the inference server is running you can send HTTP or GRPC requests to it to perform inferencing. By default the inferencing service is exposed with a LoadBalancer service type. Use the following to find the external IP for the inference service. In this case it is 35.232.176.113.

$ kubectl get services
NAME         TYPE           CLUSTER-IP    EXTERNAL-IP      PORT(S)                                        AGE
inference-se LoadBalancer   10.7.241.36   35.232.176.113   8000:31220/TCP,8001:32107/TCP,8002:31682/TCP   1m
kubernetes   ClusterIP      10.7.240.1    <none>           443/TCP                                        1h

The inference server exposes an HTTP endpoint on port 8000, and GRPC endpoint on port 8001 and a Prometheus metrics endpoint on port 8002. You can use curl to get the status of the inference server from the HTTP endpoint.

$ curl 35.232.176.113:8000/api/status

Follow the instructions to build the inference server example image and performance clients. You can then use these examples to send requests to the server. For example, for an image classification model use the image_client example to perform classification of an image.

$ image_client -u 35.232.176.113:8000 -m resnet50_netdef -c3 mug.jpg
Output probabilities:
batch 0: 504 (COFFEE MUG) = 0.777365267277
batch 0: 968 (CUP) = 0.213909029961
batch 0: 967 (ESPRESSO) = 0.00294389552437

Cleanup

When done use ks to remove the deployment.

$ ks delete default -c iscomp

If you create a cluster then make sure to also delete that.

$ gcloud container clusters delete myinferenceserver

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