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Included in the NGC Helm Repository is a chart designed to automate for push-button deployment to a Kubernetes cluster.

The Riva Speech AI Helm Chart deploys the ASR, NLP, and TTS services automatically. The Helm chart performs a number of functions:

  • Pulls Docker images from NGC for the Riva Speech AI server and utility containers for downloading and converting models.

  • Downloads the requested model artifacts from NGC as configured in the values.yaml file.

  • Generates the Triton Inference Server model repository.

  • Starts the Riva Speech AI server as configured in a Kubernetes pod.

  • Exposes the Riva Speech AI server as a configured service.

Examples of pretrained models are released with Riva for each of the services. The Helm chart comes preconfigured for downloading and deploying all of these models.


The Helm chart configuration can be modified for your use case by modifying the values.yaml file. In this file, you can change the settings related to which models to deploy, where to store them, and how to expose the services.


To deploy Riva, a functioning Kubernetes environment with a GPU (NVIDIA Volta or later) is required. This can be either on-premise or in a cloud provider, or within a managed Kubernetes environment so long as the environment has GPU support enabled.

Installation with Helm#

  1. Validate Kubernetes with NVIDIA GPU support.

    Kubernetes with GPU is well supported by NVIDIA. For more information, refer to the Install Kubernetes instructions to ensure that your environment is properly setup.

    If using an NVIDIA A100 GPU with Multi-Instance GPU (MIG) support, refer to MIG Support in Kubernetes.

  2. Download and modify the Helm chart for your use case.

    export NGC_API_KEY=<your_api_key>
    helm fetch https://helm.ngc.nvidia.com/nvidia/riva/charts/riva-api-2.13.1.tgz \
            --username=\$oauthtoken --password=$NGC_API_KEY --untar

    The above comment creates a new directory called riva-api in your current working directory. Within that directory is a values.yaml file that can be modified to suit your use case (refer to the Kubernetes Secrets and Riva Settings sections).

  3. After the values.yaml file has been updated to reflect the deployment requirements, Riva can be deployed to the Kubernetes cluster:

    helm install riva-api riva-api

    Alternatively, use the --set option to install without modifying the values.yaml file. Ensure you set the NGC API key, email, and model_key_string to the appropriate values. By default, model_key_string is tlt_encode.

    helm install riva-api \
        --set ngcCredentials.password=`echo -n $NGC_API_KEY | base64 -w0` \
        --set ngcCredentials.email=your_email@your_domain.com \
        --set modelRepoGenerator.modelDeployKey=`echo -n model_key_string | base64 -w0`
  4. Helm configuration. The following sections highlight key areas of the values.yaml file and considerations for deployment. Consult the individual service documentation for more details as well as the Helm chart’s values.yaml file, which contains inline comments explaining the configuration options.


Depending on the number of models enabled, a higher value of failureThreshold for Startup probe in deployment.yaml might be needed to accomodate increased startup time.

Kubernetes Secrets#

The Helm deployment uses multiple Kubernetes secrets for obtaining access to NGC:

  • imagepullsecret: one for Docker images

  • modelpullsecret: one for model artifacts

  • riva-model-deploy-key: one for encrypted models

The names of the secrets can be modified in the values.yaml file, however, if you are deploying into an NVIDIA EGX™ or NVIDIA Fleet Command™ managed environment, your environment must have support for imagepullsecret and modelpullsecret. These secrets are managed by the chart and can be manipulated by setting the respective values within the ngcCredentials section within values.yaml.

Riva Settings#

The values.yaml for Riva is intended to provide maximum flexibility in deployment configurations.

The replicaCount field is used to configure the number of identical instances (or pods) of the services that are deployed. When load-balanced appropriately, increasing this number (as resources permit) enables horizontal scaling for increased load.

By default, the Riva API server and Triton server are deployed inside the same container in a single pod. This works fine in most environments with 1 GPU and numerous models. In the case of a multi-GPU environment, models can be distributed across GPUs for better utilization of GPUs. In such a case, the modelRepoGenerator.useSeparateTriton flag can be used to deploy the Triton server in a separate pod with 1 GPU each. There can be as many Triton server pods as the number of available GPUs. The number of Triton server pods is controlled by the number of dict entries under modelRepoGenerator.ngcModelConfigs. For each Triton server entry, the models value specifies the list of models to be loaded. Deployment of each Triton server can be controlled by dict entry enabled under each Triton. By default, a single Triton server pod modelRepoGenerator.ngcModelConfigs.tritonGroup0 is configured to load the default list of models.

Prebuilt models not required for your deployment can be deleted from the default list in modelRepoGenerator.ngcModelConfigs.tritonGroup0.models. We recommend you remove models that are not used to reduce deployment time and GPU memory usage.

By default, models are downloaded from NGC, optimized for TensorRT (if necessary) before the service starts, and stored in a short-lived location. When the pod terminates, these model artifacts are deleted and the storage is freed for other workloads. This behavior is controlled by the modelDeployVolume field. Refer to the Kubernetes Volumes documentation for alternative options that can be used for persistent storage. For scale-out deployments, having a model store shared across pods greatly improves scale-up time since the models are prebuilt and already available to the Riva container.

  • Persistent storage should only be used in homogenous deployments where GPU models are identical.

  • Currently, provided models nearly fill a T4’s memory (16 GB). We recommend running a subset of models/services if using a single GPU.

Deploying custom RMIR models with Helm#

If you have trained a custom model, you would have generated an .rmir file. Perform the following steps to deploy a custom RMIR model. It’s assumed that you are using the host path for storing the model repository.

  1. Specify the host path for the model repository. Update the modelRepoGenerator.modelDeployVolume.hostPath.path parameter in the values.yaml file. It uses the default value of /data/riva. You can configure it as needed.

  2. Create a directory at the specified host path. The default value is /data/riva.

  3. Create a directory rmir at path /data/riva/rmir.

  4. Create a directory under /data/riva/rmir/ for keeping the custom RMIR model. Directory name should follow <model_name>_v<model_version> format. For example /data/riva/rmir/custom_asr_model_v1.0, where model_name is custom_asr_model with version 1.0

  5. Copy the custom RMIR model file inside the directory created in the previous step. For example /data/riva/rmir/custom_asr_model_v1.0/model.rmir. The RMIR filename can be <any>.rmir.

  6. Add a model entry in values.yaml under modelRepoGenerator.ngcModelConfigs.tritonGroup0.models. Use <model_name>:<model_version> as the naming format. For example:

          enabled: true
          - custom_asr_model:1.0
  1. Configure any other values as required and install the Helm chart as per Installation with Helm.

Ingress Controller#

There is a base configuration for a simple ingress controller using Traefik. This can be configured through the values.yaml, or can be replaced with any controller supporting http2 and grpc.

Ingress controllers are found in both on-premise and cloud-based deployments. For this to work correctly, you must have a functional name resolution using whatever mechanism (DNS, /etc/host files, and so on).

For any sort of multi-pod scaling, you must have a correctly configured ingress controller performing HTTP/2 or gRPC load balancing including name resolution.

Further details can be found in the ingress: section in the values.yaml file.