NVCF Configuration for Azure Monitor

Add Telemetry Endpoint

Note

If you have an existing application insights and log analytics workspace, navigate to Overview, followed by JSON View to capture the connection string.

Azure Monitor Setup

• Create a new Application Insights Instance

  • Go to Azure. Select Monitor, followed by Application Insights.

• Under Application Insights, select View, followed by + Create.

  • Choose appropriate values for Subscription, Resource Group, and Log Analytics Workspace

Select Review + create

Navigate to Overview, followed by JSON View and then capture the ConnectionString Value

NVCF Create Telemetry Endpoint

Documentation for creating a Telemetry Endpoint can be found here.

• Via the UI, navigate to https://nvcf.ngc.nvidia.com/, followed by Settings

• Under Telemetry Endpoints, select + Add Endpoint

Provide an appropriate Name under Endpoint Details. We are using azure-monitor-endpoint

• Select Azure Monitor

• From the copied connection string value from Azure, paste the following values:

Endpoint (IngestionEndpoint)	https://xxxx-x.in.applicationinsights.azure.com/
Instrumentation Key	xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
Live Endpoint	https://xxxx.livediagnostics.monitor.azure.com/
Application ID	xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

• Select Logs, followed by Metrics under Telemetry Type

• Select HTTP for the communication protocol

• Select Save Configuration

• Via the CLI, run the following command:

curl -s --location --request POST
'https://api.ngc.nvidia.com/v2/nvcf/telemetries' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer '$NVCF_TOKEN \
--data '{
    "endpoint": "YOUR_AZURE_MONITOR_ENDPOINT",
    "protocol": "HTTP",
    "provider": "AZURE_MONITOR",
    "types": [
        "LOGS",
        "METRICS"
    ],
    "secret": {
        "name": "YOUR_NVCF_TELEMETRY_NAME",
        "value": {
            "instrumentationKey": "YOUR_INSTRUMENTATION_KEY",
            "liveEndpoint": "YOUR_LIVE_ENDPOINT",
            "applicationId": "YOUR_APPLICATION_ID"
        }
    }
}'

Get Telemetry ID

Once you have created the telemetry endpoint, we need to capture the telemetryId for the created Azure Monitor telemetry on NVCF. This is required to create the function via the CLI/Script.

Note

This step is not required for creating the function via the NVCF UI.

echo NVCF_TOKEN="nvapi-xxxxxxxxxxxxxxxxxxxxxx"

Run the following command to get the telemetryId of the created Azure Monitor endpoint:

curl -s --location --request GET
'https://api.ngc.nvidia.com/v2/nvcf/telemetries' \
 --header 'Content-Type: application/json' \
 --header 'Authorization: Bearer '$NVCF_TOKEN'' | jq

Copy the telemetryId field for the created azure-monitor-endpoint:

"telemetryId": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
"name": "azure-monitor-endpoint",
"endpoint": xxx
.
.
"createdAt":xxx

Store the value in a variable called Telemetry_ID:

export TELEMETRY_ID="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"

Environment Variables

The BYOO implementation uses environment variables to control Vector behavior and configuration. These variables are set during NVCF function deployment and control how the container processes logs.

Environment Variable	Possible Values	Function
VECTOR_OTEL_ACTIVE	TRUE ; FALSE / Not set	When TRUE: Container uses Vector for log processing and forwarding to NVCF collector. When FALSE or unset: Container bypasses Vector and runs Kit application directly via /entrypoint.sh
VECTOR_CONF_B64	Base64 encoded string	Provides custom Vector configuration via base64-encoded string

• If you provide your VECTOR_CONF_B64 value the entrypoint decodes and uses your custom Vector configuration.

• When not provided, it uses the default configuration from vector.toml which is copied to the path /opt/vector/static_config.toml inside the container.

To base64 encode, use the following command:

base64 -w 0 vector.toml

Container to Function Flow

• Via the CLI:

curl -s -v --location --request POST 'https://api.ngc.nvidia.com/v2/nvcf/functions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer '$NVCF_TOKEN'' \
--data '{
    "name": "'${STREAMING_FUNCTION_NAME:-usd-composer}'",
    "inferenceUrl": "'${STREAMING_START_ENDPOINT:-/sign_in}'",
    "inferencePort": '${STREAMING_SERVER_PORT:-49100}',
    "health": {
        "protocol": "HTTP",
        "uri": "/v1/streaming/ready",
        "port": '${CONTROL_SERVER_PORT:-8111}',
        "timeout": "PT10S",
        "expectedStatusCode": 200
},
"containerImage": "'$STREAMING_CONTAINER_IMAGE'",
"apiBodyFormat": "CUSTOM",
"description": "'${STREAMING_FUNCTION_NAME:-usd-composer}'",
"functionType": "STREAMING",
"containerEnvironment": [
    {"key": "NVDA_KIT_NUCLEUS", "value": "'$NUCLEUS_SERVER'"},
    {"key": "OMNI_JWT_ENABLED", "value": "1"},
    {"key": "VECTOR_OTEL_ACTIVE", "value": "TRUE"},
    {"key": "NVDA_KIT_ARGS", "value":
"--/app/livestream/nvcf/sessionResumeTimeoutSeconds=300"}
  ],
  “telemetries”: {
    “logsTelemetryId”: “'$TELEMETRY_ID'”,
    "metricsTelemetryId": "'$TELEMETRY_ID'"
}

• Via the UI:

Or if you provide custom B64:

Confirm Telemetry on Azure Monitor

Sample KQL query:

For Logs:

AppTraces
| where Properties.function_id == "xxxxxxxxxxxx"

For Metrics:

AppMetrics
| where Properties.function_id == "xxxxxxxxxxxx"