You can use the AMD Inference Server with KServe to deploy the server on a Kubernetes cluster.

Set up Kubernetes and KServe

To use KServe, you will need a Kubernetes cluster. There are many ways to set up and configure a Kubernetes cluster depending on your use cases. Instructions for installing and configuring Kubernetes are out of this scope.

Install KServe using the instructions provided by KServe. We have tested with KServe 0.8 using the standard serverless installation but other versions/configurations may work as well. Once KServe is installed, verify basic functionality of the cluster using KServe’s basic tutorial. If this succeeds, KServe should be installed correctly. KServe installation help and debugging are also out of scope for these instructions. If you run into problems, reach out to the KServe project.

If you want to use FPGAs for your inferences, install the Xilinx FPGA Kubernetes plugin. This plugin adds FPGAs as a resource for Kubernetes so you can request them when launching services on your cluster.

You may also want to install monitoring and tracing tools such as Prometheus, Jaeger, and Grafana to your Kubernetes cluster. Refer to the documentation for these respective projects on installation details. The kube-prometheus project is a good starting point to install some of these tools.

Get or build the AMD Inference Server Image

To use with KServe, you will need a deployment image. Once you have it somewhere, make sure you can use docker pull <image> on all the nodes in the Kubernetes cluster to get the image.

Start an inference service

Services in Kubernetes can be started with YAML configuration files. To add a service to your cluster, create the configuration file and use kubectl apply -f <path to yaml file>. KServe provides a number of Custom Resource Definitions (CRDs) that you can use to serve inferences with the AMD Inference Server. The current recommended approach from KServe is to use the ServingRuntime method.

As you start an inference service, you will need models to serve. These models should be in the format that the inference server expects for its model repository. You can put the model up on any of the cloud storage platforms that KServe supports like GCS, S3 and HTTP. If you use HTTP, the model should be zipped and should unzip in the expected model repository directory structure. Other archive formats such as .tar.gz may not work as expected. Wherever you store it, the URI will be needed to start inference services.

Serving Runtime

KServe defines two CRDs called ServingRuntime and ClusterServingRuntime, where the only difference is that the former is namespace-scoped and the latter is cluster-scoped. You can see more information about these CRDs in KServe’s documentation. The AMD Inference Server is not included by default in the standard KServe installation but you can add the runtime to your cluster. A sample ClusterServingRuntime definition is provided below.

apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
  # this is the name of the runtime to add
  name: kserve-amdserver
    # depending on the image you're using, and which backends are added,
    # the supported formats could be different. For example, this assumes
    # that a ZenDNN image was created with both TF+ZenDNN and PT+ZenDNN
    # support
    - name: tensorflow
      version: "2"
    - name: pytorch
      version: "1"
    # depending on the image you're using, it may not support both HTTP/REST
    # and gRPC, respectively. By default, both protocols are supported.
    - v2
    - grpc-v2
    - name: kserve-container
      # provide the image name. The usual rules around images apply (see
      # above in the section "Build the AMD Inference Server Image")
      image: <your image>
      # when the image starts, it will automatically launch the server
      # executable with the following arguments. While the ports used by
      # the server are configurable, there are some assumptions in KServe
      # with the default port values so it is recommended to not change them
        - amdinfer-server
        - --model-repository=/mnt/models
        - --repository-monitoring
        - --grpc-port=9000
        - --http-port=8080
      # the resources allowed to the service. If the image needs access to
      # hardware like FPGAs or GPUs, then those resources need to be added
      # here so Kubernetes can schedule pods on the appropriate nodes.
          cpu: "1"
          memory: 2Gi
          cpu: "1"
          memory: 2Gi

Adding a ClusterServingRuntime or a ServingRuntime is a one-time action per cluster. Once it’s added, you can launch inference services using the runtime like:

apiVersion: "serving.kserve.io/v1beta1"
kind: InferenceService
    # The autoscaling target defines how the service should be auto-scale in
    # response to incoming requests. The value of 5 indicates that
    # additional containers should be deployed when the number of concurrent
    # requests exceeds 5.
    autoscaling.knative.dev/target: "5"
    controller-tools.k8s.io: "1.0"
    app: example-amdserver-runtime-isvc
  name: example-amdserver-runtime-isvc
        name: tensorflow
      storageUri: url/to/model
      # while it's optional for KServe, the runtime should be explicitly
      # specified to make sure the runtime you've added for the AMD Inference
      # Server is used
      runtime: kserve-amdserver

Custom container

This approach uses an older method of starting inference services using the InferenceService and TrainedModel CRDs, where you start a custom container directly and add models to it. Initially, no models are loaded on the server as it uses the multi-model serving mechanism of KServe that was a precursor to ModelMesh to support inference servers running multiple models. Once an InferenceService is up, you can load models to it by applying one or more TrainedModel CRDs. Each such load adds a model to the server and makes it available for inference requests. A sample YAML file is provided below.

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata: null
  # The autoscaling target defines how the service should be auto-scaled in
  # response to incoming requests. The value of 5 indicates that additional
  # containers should be deployed when the number of concurrent requests
  # exceeds 5.
  autoscaling.knative.dev/target: '5'
  controller-tools.k8s.io: '1.0'
  app: example-amdserver-multi-isvc
name: example-amdserver-multi-isvc
spec: null
    - name: custom
      image: <your image>
        - name: MULTI_MODEL_SERVER
          value: 'true'
        - amdinfer-server
        - --model-repository=/mnt/models
        - --http-port=8080
        - --grpc-port=9000
        - containerPort: 8080
          protocol: TCP
        - containerPort: 9000
          protocol: TCP
apiVersion: "serving.kserve.io/v1alpha1"
kind: TrainedModel
  # this name is significant and must match the top-level directory in the
  # downloaded model at the storageUri. This string becomes the endpoint u
  # used to make inferences
  name: <name of the model>
  # the name used here must match an existing InferenceService to load
  # this TrainedModel to
  inferenceService: example-amdserver-multi-isvc
    framework: tensorflow
    storageUri: url/to/model
    memory: 1Gi

Making Requests

The method by which you communicate with your service depends on your Kubernetes cluster configuration. For example, one way to make requests is to get the address of the INGRESS_HOST and INGRESS_PORT, and then make requests to this URL by setting the Host header on all requests to your targeted service. This use case may be needed if your cluster doesn’t have a load-balancer and/or DNS enabled.

Once you can communicate with your service, you can make requests to the Inference Server using REST with the Python client library or the KServe Python API. The request will be routed to the server and the response will be returned. You can see some examples of using the KServe Python API to make requests in the tests.


Debugging the inference server with KServe adds some additional complexity. You may have issues with your KServe installation itself (in which case you need to debug KServe alone until you can run a basic InferenceService). Once the default KServe example works, then you can begin debugging any inference server specific issues.

Use kubectl logs <pod_name> <container> to see the logs associated with the failing pod. You’ll need to use kubectl get pods to get the name of the pods corresponding to the InferenceService you’re attempting to debug. The logs command will list the containers in this pod (if more than one exist) and prompt you to specify the container whose logs you’re interested in. These logs may have helpful error messages.

You can also directly connect to the inference server container that’s running in KServe with Docker. The easiest way to do this is with the amdinfer script in the inference server repository. You’ll need to first connect to the node where the container is running. On that host:

# this lists the running Inference Server containers
amdinfer list

# get the container ID of the container you want to connect to

# provide the ID as an argument to the attach command to open a bash shell
# in the container
amdinfer attach -n <container ID>

Once in the container, you can find the running amdinfer-server executable and then follow the regular debugging guide to debug the inference server.