Nemo Retriever Embedding Helm Chart

This Helm Chart simplifies Embedding NIM deployment on Kubernetes. It aims to support deployment with a variety of possible cluster, GPU and storage configurations.

NIMs are intended to be run on a system with NVIDIA GPUs, with the type and number of GPUs depending on the model. To use helm, you must have a Kubernetes cluster with appropriate GPU nodes and the GPU Operator installed.

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>"

Namespace

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

kubectl create namespace nrem

Creating Secrets

Use the following script below to create the expected secrets for this helm chart.

DOCKER_CONFIG='{"auths":{"nvcr.io":{"username":"$oauthtoken", "password":"'${NGC_API_KEY}'" }}}'
echo -n $DOCKER_CONFIG | base64 -w0
NGC_REGISTRY_PASSWORD=$(echo -n $DOCKER_CONFIG | base64 -w0 )

cat <<EOF > imagepull.yaml
apiVersion: v1
kind: Secret
metadata:
  name: nvcrimagepullsecret
type: kubernetes.io/dockerconfigjson
data:
  .dockerconfigjson: ${NGC_REGISTRY_PASSWORD}
EOF

cat <<EOF > ngc-cli.yaml
apiVersion: v1
kind: Secret
metadata:
  name: ngc-api
type: Opaque
data:
  NGC_CLI_API_KEY: ${NGC_API_KEY}
  NGC_API_KEY: ${NGC_API_KEY}
EOF

kubectl apply -n nrem -f imagepull.yaml
kubectl apply -n nrem -f ngc-cli.yaml

Configuration Considerations

The following deployment commands will by default create a single deployment with one replica using the NV-EmbedQA-E5-V5 model. The following options can be used to make modifications to the behavior. See below for all parameters.

• image.repository -- The container (Embedder NIM) to deploy
• image.tag -- The version of that container (Embedder NIM)
• 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

Deploying

Basic deploymnet

helm upgrade --install \
  --namespace nrem \
  --username '$oauthtoken' \
  --password "${NGC_API_KEY}" \
  nemo-embedder \
  https://helm.ngc.nvidia.com/nim/nvidia/charts/text-embedding-nim-1.1.0.tgz

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 nrem

The pod should eventually end up in the running state.

NAME              READY   STATUS    RESTARTS   AGE
nemo-embedding-ms-0   1/1     Running   0          8m44s

Set the Container Image

Add the following to update and change the container image and version for the NIM Container Image:

--set image.repository="nvcr.io/nim/nvidia/nv-embedqa-e5-v5" \
--set image.tag="1.1.0" \

See below for different images compatible with this helm chart.

Storage

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

This chart supports two general categories:

1. Persistent Volume Claims (enabled with persistence.enabled)
2. hostPath (enabled with persistences.hostPath)

The chart will default to use the standard storage class and will create a PersistentVolume and a PersistentVolumeClaim.

If you do not have a Storage Class Provisioner which creates PersistentVolumes automatically, then set the value persistence.createPV=true. This is also necessary when you are using persistence.hostPath on minikube.

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

See additional options below in Parameters.

Recommended configuration for Minikube

Minkube will create a hostPath based PV and PVC by default with this chart. We recommend that you add the following to your helm commands.

--set persistence.class=standard

Deploying Snowflake Arctic Embedding

Run the helm command with the following parameters, update your version in image.tag:

helm upgrade --install \
  --namespace nrem \
  --username '$oauthtoken' \
  --password "${NGC_API_KEY}" \
  --set image.repository=nvcr.io/nim/snowflake/arctic-embed-l \
  --set image.tag=1.0.1 \
  nemo-embedder \
  https://helm.ngc.nvidia.com/nim/nvidia/charts/text-embedding-nim-1.1.0.tgz

Deploying Mistral 7B

Create a values files for the resource requirements of the 7B Model:

# values-mistral.yaml
resources:
  limits:
    ephemeral-storage: 28Gi
    nvidia.com/gpu: 1
    memory: 32Gi
    cpu: "16000m"
  requests:
    ephemeral-storage: 28Gi
    nvidia.com/gpu: 1
    memory: 16Gi
    cpu: "4000m"

envVars:
  NIM_NUM_TOKENIZERS: 8

Then deploy the model:

helm upgrade --install \
  --namespace nrem \
  --username '$oauthtoken' \
  --password "${NGC_API_KEY}" \
  -f values-mistral.yaml \
  --set image.repository=nvcr.io/nim/nvidia/nv-embedqa-mistral-7b-v2 \
  --set image.tag=1.0.1 \
  nemo-embedder \
  https://helm.ngc.nvidia.com/nim/nvidia/charts/text-embedding-nim-1.1.0.tgz

Running inference

In the previous example the API endpoint is exposed on port 8080 through the Kubernetes service of the default type with no ingress, since authentication is not handled by the NIM itself. The following commands assume the NV-EmbedQA-E5-V5 model was deployed.

Adjust 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 nrem service/nemo-embedding-ms 8080:8080

Then try a request:

curl -X 'POST' \
  'http://localhost:8080/v1/embeddings' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "input": "hello world",
    "model": "nv-embedqa-e5-v5",
    "input_type": "passage"
  }'

Parameters

Deployment parameters

Name	Description	Value
affinity	Affinity settings for deployment. Allows to constraint pods to nodes.	{}
securityContext	Specify privilege and access control settings for Container(Only affects the main container)	{}
envVars	Adds arbitrary environment variables to the main container - Key Value Pairs	{}
extraVolumes	Adds arbitrary additional volumes to the deployment set definition	{}
image.repository	NIM-LLM Image Repository	""
image.tag	Image tag	""
image.pullPolicy	Image pull policy	""
imagePullSecrets	Specify secret names that are needed for the main container and any init containers. Object keys are the names of the secrets	{}
nodeSelector	Specify labels to ensure that NeMo Inference is deployed only on certain nodes (likely best to set this to nvidia.com/gpu.present: "true" depending on cluster setup).	{}
podAnnotations	Specify additional annotation to the main deployment pods	{}
podSecurityContext	Specify privilege and access control settings for pod (Only affects the main pod).
podSecurityContext.runAsUser	Specify user UID for pod.	1000
podSecurityContext.runAsGroup	Specify group ID for pod.	1000
podSecurityContext.fsGroup	Specify file system owner group id.	1000
replicaCount	Specify replica count for deployment.	1
resources	Specify resources limits and requests for the running service.
resources.limits.nvidia.com/gpu	Specify number of GPUs to present to the running service.	1
serviceAccount.create	Specifies whether a service account should be created.	false
serviceAccount.annotations	Specifies annotations to be added to the service account.	{}
serviceAccount.automount	Specifies whether to automatically mount the service account to the container.	{}
serviceAccount.name	Specify name of the service account to use. If it is not set and create is true, a name is generated using a fullname template.	""
tolerations	Specify tolerations for pod assignment. Allows the scheduler to schedule pods with matching taints.

Autoscaling parameters

Values used for autoscaling. If autoscaling is not enabled, these are ignored. They should be overridden on a per-model basis based on quality-of-service metrics as well as cost metrics. This isn't recommended except with usage of the custom metrics API using something like the prometheus-adapter. Standard metrics of CPU and memory are of limited use in scaling NIM

Name	Description	Value
autoscaling.enabled	Enable horizontal pod autoscaler.	false
autoscaling.minReplicas	Specify minimum replicas for autoscaling.	1
autoscaling.maxReplicas	Specify maximum replicas for autoscaling.	10
autoscaling.metrics	Array of metrics for autoscaling.	[]

Ingress parameters

Name	Description	Value
ingress.enabled	Enables ingress.	false
ingress.className	Specify class name for Ingress.	""
ingress.annotations	Specify additional annotations for ingress.	{}
ingress.hosts	Specify list of hosts each containing lists of paths.
ingress.hosts[0].host	Specify name of host.	chart-example.local
ingress.hosts[0].paths[0].path	Specify ingress path.	/
ingress.hosts[0].paths[0].pathType	Specify path type.	ImplementationSpecific
ingress.hosts[0].paths[0].serviceType	Specify service type. It can be can be nemo or openai -- make sure your model serves the appropriate port(s).	openai
ingress.tls	Specify list of pairs of TLS secretName and hosts.	[]

Probe parameters

Name	Description	Value
livenessProbe.enabled	Enable livenessProbe	true
livenessProbe.method	LivenessProbe http or script, but no script is currently provided	http
livenessProbe.path	LivenessProbe endpoint path	/v1/health/live
livenessProbe.initialDelaySeconds	Initial delay seconds for livenessProbe	15
livenessProbe.timeoutSeconds	Timeout seconds for livenessProbe	1
livenessProbe.periodSeconds	Period seconds for livenessProbe	10
livenessProbe.successThreshold	Success threshold for livenessProbe	1
livenessProbe.failureThreshold	Failure threshold for livenessProbe	3
readinessProbe.enabled	Enable readinessProbe	true
readinessProbe.path	Readiness Endpoint Path	/v1/health/ready
readinessProbe.initialDelaySeconds	Initial delay seconds for readinessProbe	15
readinessProbe.timeoutSeconds	Timeout seconds for readinessProbe	1
readinessProbe.periodSeconds	Period seconds for readinessProbe	10
readinessProbe.successThreshold	Success threshold for readinessProbe	1
readinessProbe.failureThreshold	Failure threshold for readinessProbe	3
startupProbe.enabled	Enable startupProbe	true
startupProbe.path	StartupProbe Endpoint Path	/v1/health/ready
startupProbe.initialDelaySeconds	Initial delay seconds for startupProbe	40
startupProbe.timeoutSeconds	Timeout seconds for startupProbe	1
startupProbe.periodSeconds	Period seconds for startupProbe	10
startupProbe.successThreshold	Success threshold for startupProbe	1
startupProbe.failureThreshold	Failure threshold for startupProbe	180

Storage parameters

Name	Description	Value
persistence	Specify settings to modify the path /model-store if model.legacyCompat is enabled else /.cache volume where the model is served from.
persistence.enabled	Enable persistent volumes.	false
persistence.existingClaimName	Secify existing claim. If using existingClaim, run only one replica or use a ReadWriteMany storage setup.	""
persistence.class	Specify persistent volume storage class. If null (the default), no storageClassName spec is set, choosing the default provisioner.	""
persistence.retain	Specify whether the Persistent Volume should survive when the helm chart is upgraded or deleted.	""
persistence.createPV	True if you need to have the chart create a PV for hostPath use cases.	false
persistence.accessMode	Specify accessModes. If using an NFS or similar setup, you can use ReadWriteMany.	ReadWriteOnce
persistence.size	Specify size of claim (e.g. 8Gi).	50Gi
lhostPath	Configures model cache on local disk on the nodes using hostPath -- for special cases. One should investigate and understand the security implications before using this option.	""

Service parameters

Name	Description	Value
service.type	Specify service type for the deployment.	ClusterIP
service.name	Override the default service name	""
service.http_port	Specify HTTP Port for the service.	8080
service.annotations	Specify additional annotations to be added to service.	{}

Monitoring

Name	Description	Value
zipkinDeployed	Specify if this chart should deploy zipkin for metrics.	false
otelDeployed	Specify if this chart should deploy OpenTelemetry for metrics.	false
otelEnabled	Specify if this chart should sink metrics to OpenTelemetry.	false
otelEnvVars	Env variables to configure OTEL in the container, sane defaults in chart.	{}
logLevel	Log Level to set for the container and metrics collection.	{}

Opentelemetry configurations can be found here

Support Matrix

Hardware

NVIDIA NIMs will run on any NVIDIA GPU, as long as the GPU has sufficient memory, or on multiple, homogeneous NVIDIA GPUs with sufficient aggregate memory and CUDA compute capability > 7.0 or higher (8.0 for bfloat16). Some model/GPU combinations are optimized. See the following Supported Models section for further information.

Software

• Linux operating systems (Ubuntu 20.04 or later recommended)
• Docker >= 23.0.1
• NVIDIA Driver >= 535

Supported Models

These models are optimized using TRT-LLM and are available as pre-built, optimized engines on NGC.

NV-EmbedQA-Mistral-7b-v2

Load this NIM by adding the following to the helm upgrade command.

--set image.repository=nvcr.io/nim/nvidia/nv-embedqa-mistral-7b-v2 \
--set image.tag=1.0.1 \

Optimized configurations

The GPU Memory and Disk Space values are in GB; Disk Space is for both the container and the model; Profile is for what the model is optimized.

GPU	GPU Memory	Precision	Disk Space
H100	7	FP8	14
A100	14	FP16	28
L40S PCIe	7	FP8	14
L40S PCIe	14	FP16	28
A10G PCIe	14	FP16	28

Non-optimized configuration

The GPU Memory and Disk Space values are in GB; Disk Space is for both the container and the model.

GPUs	GPU Memory	Precision	Disk Space
Any NVIDIA GPU with sufficient GPU memory or on multiple, homogeneous NVIDIA GPUs with sufficient aggregate memory and compute capability > 7.0 or higher (8.0 for bfloat16)	26	FP16	16

NV-EmbedQA-E5-V5

Load this NIM by adding the following to the helm upgrade command.

--set image.repository=nvcr.io/nim/nvidia/nv-embedqa-e5-v5 \
--set image.tag=1.1.0 \

Optimized configurations

The GPU Memory and Disk Space values are in GB; Disk Space is for both the container and the model; Profile is for what the model is optimized.

GPU	GPU Memory	Precision	Disk Space
H100	4	FP16	8
A100	4	FP16	8
L40S PCIe	4	FP16	8
A10G PCIe	4	FP16	8
A10 PCIe	4	FP16	8

Non-optimized configuration

The GPU Memory and Disk Space values are in GB; Disk Space is for both the container and the model.

GPUs	GPU Memory	Precision	Disk Space
Any NVIDIA GPU with sufficient GPU memory or on multiple, homogeneous NVIDIA GPUs with sufficient aggregate memory and compute capability > 7.0 or higher (8.0 for bfloat16)	26	FP16	16

Snowflake Arctic

Load this NIM by adding the following to the helm upgrade command.

--set image.repository=nvcr.io/nim/snowflake/arctic-embed-l \
--set image.tag=1.0.1 \

Optimized configurations

The GPU Memory and Disk Space values are in GB; Disk Space is for both the container and the model; Profile is for what the model is optimized.

GPU	GPU Memory	Precision	Disk Space
H100	4	FP16	4
A100	4	FP16	8
L4 PCIe	4	FP16	8
L40S PCIe	4	FP16	8
A10G PCIe	4	FP16	8
A10 PCIe	4	FP16	8

Non-optimized configuration

The GPU Memory and Disk Space values are in GB; Disk Space is for both the container and the model.

GPUs	GPU Memory	Precision	Disk Space
Any NVIDIA GPU with sufficient GPU memory or on multiple, homogeneous NVIDIA GPUs with sufficient aggregate memory and compute capability > 7.0 or higher (8.0 for bfloat16)	26	FP16	16