Self-hosted CLI

This page provides documentation for the NVCF Self-hosted CLI, a command-line interface for managing NVIDIA Cloud Functions in self-hosted deployments.

Overview

The NVCF Self-hosted CLI provides:

• Automatic Token Generation: Generate NVCF_TOKEN and API keys via direct API calls
• Smart State Management: Persistent workflow context eliminates manual ID copying
• Multi-Environment Support: Separate configurations for dev/staging/production
• gRPC Invocation: Native support for gRPC function invocation
• Shell Completion: Autocompletion for bash, zsh, fish, and PowerShell

Prerequisites

• Network access to NVCF API endpoints
• A source checkout with Bazel/Bazelisk when you build from the repository
• NGC CLI installed when you download the CLI release from NGC

Installation

You can build nvcf-cli from this repository or download a packaged CLI release from NGC. Use the source build when you are validating local changes or running the local k3d quickstart from a repository checkout.

Build from the repository

Run the build from the repository root:

$
bazel build //src/clis/nvcf-cli:nvcf-cli

The binary is written to:

bazel-bin/src/clis/nvcf-cli/nvcf-cli_/nvcf-cli

Install it on your PATH:

$
install -m 0755 \
>
  bazel-bin/src/clis/nvcf-cli/nvcf-cli_/nvcf-cli \
>
  /usr/local/bin/nvcf-cli

If your environment cannot reach the configured Bazel remote cache, disable the remote cache for this build:

$
bazel build --remote_cache= //src/clis/nvcf-cli:nvcf-cli

Download from NGC

The CLI is available as a resource from NGC. See download-nvcf-cli for detailed download and extraction instructions.

The downloaded package includes:

• nvcf-cli - The CLI binary
• .nvcf-cli.yaml.template - Configuration template
• examples/ - Sample configuration files
• USAGE-GUIDE.md - Detailed usage documentation

Configuration

The CLI uses YAML configuration files. If you downloaded the packaged CLI, copy the included template:

$
cp .nvcf-cli.yaml.template .nvcf-cli.yaml

If you built the CLI from source, create .nvcf-cli.yaml from the examples below or from src/clis/nvcf-cli/examples/config-dev.yaml.

Configuration files are searched in this order:

1. Explicit path via --config flag (highest priority)
2. Current directory: ./.nvcf-cli.yaml
3. Home directory: ~/.nvcf-cli.yaml

Self-Hosted Configuration

For self-hosted deployments, the CLI must be configured to communicate with your gateway. The gateway uses hostname-based routing for HTTP services.

Prepare Gateway API ingress

For remote Helmfile deployments, set up Gateway API ingress before you configure the CLI. The CLI calls the configured API, API Keys, invocation, and gRPC endpoints during token minting, cluster registration, health checks, and function operations.

Complete Gateway quickstart before you configure the CLI. That procedure installs the Gateway API CRDs, creates and labels the required namespaces, installs Envoy Gateway, creates the GatewayClass and Gateway, waits for the Gateway to be programmed, and exports:

$
echo "$HTTP_GATEWAY_NAMESPACE/$HTTP_GATEWAY_NAME"
$
echo "$GRPC_GATEWAY_NAMESPACE/$GRPC_GATEWAY_NAME"
$
echo "$GATEWAY_ADDR"
$
echo "$GRPC_GATEWAY_ADDR"

These are the same Gateway setup steps used by the Helmfile and standalone install paths. Keep the exported values in your shell, then configure the CLI. The local k3d quickstart uses local route hostnames instead.

For test environments without production DNS, use the Gateway load balancer address as the stack domain:

$
export STACK_DOMAIN="$GATEWAY_ADDR"

For production environments, set STACK_DOMAIN to the DNS name that your HTTPRoute hostnames use.

Configuring the CLI

Create your configuration file:

$
# Copy the template
$
cp .nvcf-cli.yaml.template .nvcf-cli.yaml

Complete self-hosted configuration:

1
# ==============================================================================
2
# API Endpoints - Point to your gateway load balancer
3
# ==============================================================================
4
5
# Main API endpoint (use http:// for non-TLS setups)
6
base_http_url: "http://<GATEWAY_ADDR>"
7
8
# Invocation endpoint (same as base_http_url for self-hosted)
9
invoke_url: "http://<GATEWAY_ADDR>"
10
11
# gRPC endpoint - uses dedicated TCP port (no Host header needed)
12
base_grpc_url: "<GRPC_GATEWAY_ADDR>:10081"
13
14
# API Keys service endpoint
15
api_keys_service_url: "http://<GATEWAY_ADDR>"
16
17
# ==============================================================================
18
# Host Header Overrides (Required for Hostname-Based Routing)
19
# ==============================================================================
20
#
21
# Because the gateway routes HTTP requests based on the Host header,
22
# you must specify the correct Host header for each service.
23
# These values must match the HTTPRoute hostnames rendered from STACK_DOMAIN.
24
#
25
# Without these, the gateway returns 404 because it can't match the route.
26
27
# Host header for API Keys service
28
api_keys_host: "api-keys.<STACK_DOMAIN>"
29
30
# Host header for NVCF API (function management)
31
api_host: "api.<STACK_DOMAIN>"
32
33
# Host header for Invocation service
34
invoke_host: "invocation.<STACK_DOMAIN>"
35
36
# ==============================================================================
37
# API Keys Service Configuration
38
# ==============================================================================
39
40
api_keys_service_id: "nvidia-cloud-functions-ncp-service-id-aketm"
41
api_keys_issuer_service: "nvcf-api"
42
api_keys_owner_id: "svc@nvcf-api.local"
43
44
# ==============================================================================
45
# Account Configuration
46
# ==============================================================================
47
48
client_id: "nvcf-default"
49
50
# ==============================================================================
51
# Debugging (optional - set to true for verbose output)
52
# ==============================================================================
53
54
debug: false

For test environments without production DNS, the URL fields and host fields can use the Gateway load balancer address:

1
# Gateway load balancer address
2
base_http_url: "http://a1b2c3d4e5f6.us-west-2.elb.amazonaws.com"
3
invoke_url: "http://a1b2c3d4e5f6.us-west-2.elb.amazonaws.com"
4
base_grpc_url: "a1b2c3d4e5f6.us-west-2.elb.amazonaws.com:10081"
5
api_keys_service_url: "http://a1b2c3d4e5f6.us-west-2.elb.amazonaws.com"
6
7
# Host headers matching HTTPRoute configuration
8
api_keys_host: "api-keys.a1b2c3d4e5f6.us-west-2.elb.amazonaws.com"
9
api_host: "api.a1b2c3d4e5f6.us-west-2.elb.amazonaws.com"
10
invoke_host: "invocation.a1b2c3d4e5f6.us-west-2.elb.amazonaws.com"
11
12
# API Keys service
13
api_keys_service_id: "nvidia-cloud-functions-ncp-service-id-aketm"
14
api_keys_issuer_service: "nvcf-api"
15
api_keys_owner_id: "svc@nvcf-api.local"
16
17
client_id: "nvcf-default"
18
debug: true

Verifying Your Configuration

After configuring the CLI, verify connectivity:

$
# Test token generation (uses api_keys_host)
$
./nvcf-cli init
$
$
# Expected output:
$
# [INFO] Starting fresh session...
$
# [INFO] Generating admin token from API Keys service...
$
# [DEBUG] Using Host header override: api-keys.<your-gateway>
$
# [SUCCESS] Admin token generated and saved

If you see a 404 error, verify:

1. The api_keys_host value matches your HTTPRoute hostname
2. The gateway load balancer is accessible
3. The API Keys service is running: kubectl get pods -n api-keys

Production Setup: DNS and HTTPS

The Host header configuration above is designed for testing and development. For production deployments, configure proper DNS and TLS to eliminate the need for Host header overrides.

With proper DNS and HTTPS configured:

• DNS records resolve service hostnames directly to your Gateway’s load balancer
• TLS certificates secure all traffic
• The CLI uses simple URLs without Host header overrides
• Browsers and other clients can access services directly

1
# Simple URLs using your domain - no Host header overrides needed!
2
base_http_url: "https://api.nvcf.example.com"
3
invoke_url: "https://invocation.nvcf.example.com"
4
base_grpc_url: "grpc.nvcf.example.com:443"
5
api_keys_service_url: "https://api-keys.nvcf.example.com"
6
7
# No host header overrides required - DNS handles routing
8
# api_keys_host: ""  # Not needed

Multi-Environment Setup

Use the --config flag to manage multiple environments with separate configuration files:

$
# Development
$
./nvcf-cli --config dev.yaml init
$
./nvcf-cli --config dev.yaml function create --input-file function.json
$
$
# Production
$
./nvcf-cli --config prod.yaml init
$
./nvcf-cli --config prod.yaml function list

Each configuration maintains separate state files (e.g., ~/.nvcf-cli.dev.state for dev.yaml).

Debug Mode

Enable debug mode for detailed logging by adding to your configuration file:

1
debug: true

Or use the --debug flag or NVCF_DEBUG=true environment variable per-command.

Quick Start

$
# 1. Initialize - generate admin token
$
./nvcf-cli init
$
$
# 2. Generate API key for invocations
$
./nvcf-cli api-key generate
$
$
# 3. Create a function, update example file with your image
$
./nvcf-cli function create --input-file examples/create-function.json
$
$
# 4. Deploy the function (uses saved context automatically)
$
./nvcf-cli function deploy create
$
$
# 5. Invoke the function
$
./nvcf-cli function invoke --request-body '{"message": "hello world"}'
$
$
# 6. Clean up
$
./nvcf-cli function deploy remove
$
./nvcf-cli function delete

Authentication

The CLI stores three bearer credential types:

• NVCF_TOKEN: Generated by nvcf-cli init. The default CLI credential for management operations and self-hosted cluster management.
• NVCF_API_KEY: Generated by nvcf-cli api-key generate. The default CLI credential for function invocation, function discovery, and queue status.
• NVCF_NVCT_API_KEY: Generated by nvcf-cli api-key generate. Used automatically for all task subcommands.

For NVCF API endpoints, either bearer type can be used when it includes the required scope. The CLI prefers NVCF_API_KEY for read, invoke, and queue commands. It prefers NVCF_TOKEN for management commands when both credentials are configured. Self-hosted SIS cluster management uses NVCF_TOKEN. Task commands always use NVCF_NVCT_API_KEY.

See Scope reference for the self-hosted scope matrix used by CLI commands and API endpoints.

Generate NVCF_TOKEN

$
# Generate fresh NVCF_TOKEN (clears existing state)
$
./nvcf-cli init
$
$
# With debug output
$
./nvcf-cli init --debug
$
$
# Example output:
$
# [INFO] Starting fresh session...
$
# [INFO] Generating admin token from API Keys service...
$
# [SUCCESS] Admin token generated and saved
$
# Token: <admin-token>
$
# Expires: 2025-11-19 06:08:15

Refresh NVCF_TOKEN

Refresh your token while preserving function context:

$
# Refresh NVCF_TOKEN (keeps current function state)
$
./nvcf-cli refresh
$
$
# Example output:
$
# [SUCCESS] Admin token refreshed
$
# Function ID: func-abc123  (preserved)

Generate API Keys

api-key generate mints both a function key (NVCF_API_KEY) and a task key (NVCF_NVCT_API_KEY) in a single command. Use --for to generate only one.

$
# Generate both keys with defaults (24h expiration)
$
./nvcf-cli api-key generate
$
$
# Generate only the function key
$
./nvcf-cli api-key generate --for function
$
$
# Generate only the task key
$
./nvcf-cli api-key generate --for task
$
$
# Custom expiration and description
$
./nvcf-cli api-key generate --expires-in 48h --description "Production key"
$
$
# Generate with custom scopes (requires --for)
$
./nvcf-cli api-key generate --for function --scopes invoke_function,list_functions
$
$
# Generate and validate
$
./nvcf-cli api-key generate --validate

Default function key scopes:

Default task key scopes:

Command Reference

Self-hosted Deployment Commands

Use these commands to install and inspect self-hosted NVCF deployments. For the local k3d installation flow, see Quickstart.

Bundle source overrides:

• --control-plane-stack selects the control-plane stack bundle.
• --compute-plane-stack selects the compute-plane stack bundle.
• Both flags accept local paths, git URLs, and oci:// references.

self-hosted up supports only a single local k3d cluster. It requires --env local, a current k3d-* kube context, and no split-context flags. For separate control-plane and GPU clusters, use the explicit control-plane and compute-plane install primitives with Self-Managed Clusters.

Cluster Registration

Self-managed GPU clusters must be registered with the control plane before the NVCA operator can start an agent. Registration records the GPU cluster’s OIDC issuer and public JWKS with the control plane (ICMS) so the agent’s projected service account tokens (PSAT) validate at runtime. The cluster register command performs this registration and prints the Helm values the operator install needs.

$
# 1. Mint the admin token and discover the control-plane issuer
$
./nvcf-cli init
$
$
# 2. Register the GPU cluster (prints a summary and a Helm values block)
$
./nvcf-cli cluster register \
>
  --name <cluster-name> \
>
  --nca-id <nca-id> \
>
  --region <region> \
>
  --icms-url "http://<GATEWAY_ADDR>" \
>
  --ignore-existing

cluster register flags:

Issuer and JWKS discovery: cluster register detects the GPU cluster’s OIDC issuer and fetches its public JWKS, then sends them to ICMS. Detection precedence:

1. --oidc-issuer-url if provided (manual override).
2. A SPIRE OIDC discovery service in the cluster, if present.
3. The Kubernetes API server OIDC endpoint (default).

The detected source is recorded as identitySource in the output: psat for the Kubernetes API server, spire for SPIRE, or custom for a manual issuer.

Output: the command prints a summary (cluster group ID, cluster ID, OIDC issuer, region) followed by a --- Helm values for nvca-operator --- block. Copy that YAML block into a <cluster-name>-register-values.yaml file and pass it to the operator install. The values schema:

1
clusterID: <uuid>
2
clusterGroupID: <uuid>
3
ncaID: <nca-id>
4
region: <region>
5
selfManaged:
6
  identitySource: psat
7
  icmsServiceURL: "http://<GATEWAY_ADDR>"
8
  revalServiceURL: "http://<GATEWAY_ADDR>"
9
  natsURL: "nats://<GATEWAY_ADDR>:4222"

For load-balancer-fronted gateways that route by hostname, add the matching host-header overrides (selfManaged.icmsServiceHostHeaderOverride, selfManaged.revalServiceHostHeaderOverride, selfManaged.natsHostOverride) to these values. See self-managed-clusters for how the register values feed the operator install and when host-header overrides are required.

General Commands

API Key Commands

Task Commands

Task commands manage NVCT (NVIDIA Cloud Tasks) workloads. They require a task API key, which api-key generate mints automatically alongside the function key.

$
# Generate both keys (required before task commands)
$
./nvcf-cli api-key generate
$
$
# Submit a container task
$
./nvcf-cli task create \
>
  --name my-training-job \
>
  --gpu H100 \
>
  --instance-type GPU.H100_1x \
>
  --image my-registry/training:latest
$
$
# Check task details
$
./nvcf-cli task get
$
$
# Stream lifecycle events
$
./nvcf-cli task events
$
$
# Cancel a running task
$
./nvcf-cli task cancel
$
$
# List recent tasks
$
./nvcf-cli task list

Function Management Commands

Create Function

$
# Create from JSON file
$
./nvcf-cli function create --input-file examples/create-function.json
$
$
# Create with CLI flags
$
./nvcf-cli function create \
>
  --name "my-function" \
>
  --image "nvcr.io/your-org/your-image:tag" \
>
  --inference-url "/predict" \
>
  --inference-port 8000 \
>
  --health-uri "/health" \
>
  --health-port 8000
$
$
# Create with additional options
$
./nvcf-cli function create \
>
  --name "my-function" \
>
  --image "nvcr.io/your-org/your-image:tag" \
>
  --inference-url "/predict" \
>
  --inference-port 8000 \
>
  --function-type STREAMING \
>
  --container-env "KEY1=value1" \
>
  --secrets "API_KEY=secret123" \
>
  --tags "production,v2" \
>
  --rate-limit "100-S"
$
$
# Create an LLM function with model routing metadata
$
./nvcf-cli function create \
>
  --name "my-llm-function" \
>
  --image "nvcr.io/example/openai-compatible:latest" \
>
  --inference-url "/" \
>
  --inference-port 8000 \
>
  --function-type LLM \
>
  --llm-model "name=dummy-model,uris=/v1/chat/completions|/v1/responses|/v1/embeddings,routingMethod=round_robin,tokenRateLimit=1000-S"

All function create flags:

Example function JSON:

1
{
2
  "name": "my-inference-function",
3
  "containerImage": "nvcr.io/your-org/your-image:tag",
4
  "inferenceUrl": "/predict",
5
  "inferencePort": 8000,
6
  "health": {
7
    "protocol": "HTTP",
8
    "uri": "/health",
9
    "port": 8000,
10
    "timeout": "PT30S",
11
    "expectedStatusCode": 200
12
  }
13
}

LLM functions use functionType: "LLM" and define model routing metadata under models[].llmConfig:

1
{
2
  "name": "sample-llm-function",
3
  "containerImage": "nvcr.io/example/openai-compatible:latest",
4
  "inferenceUrl": "/",
5
  "inferencePort": 8000,
6
  "functionType": "LLM",
7
  "models": [
8
    {
9
      "name": "dummy-model",
10
      "llmConfig": {
11
        "uris": ["/v1/chat/completions", "/v1/responses", "/v1/embeddings"],
12
        "routingMethod": "round_robin",
13
        "tokenRateLimit": "1000-S"
14
      }
15
    }
16
  ]
17
}

For LLM models, llmConfig.routingMethod accepts round_robin, power_of_two, groq_multiregion, pulsar, or random. Supported LLM paths are /v1/chat/completions, /v1/responses, and /v1/embeddings. llmConfig.tokenRateLimit accepts one or more comma-separated positive integer token limits in <value>-<unit> format. Supported units are S (seconds), M (minutes), H (hours), D (days), and W (weeks). Use 1000-S for a single limit, or 1000-S,5000-M,100000-H,500000-D,1000000-W for a combined limit with distinct units. Use JSON input for combined limits because inline CLI model specs use commas as field separators.

Deploy Function

The function deploy command group manages deployments with the following subcommands:

$
# Deploy using saved context (from create)
$
./nvcf-cli function deploy create
$
$
# Deploy with explicit IDs and configuration
$
./nvcf-cli function deploy create \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --instance-type "NCP.GPU.A10G_1x" \
>
  --gpu "A10G" \
>
  --min-instances 1 \
>
  --max-instances 1
$
$
# Deploy from JSON file
$
./nvcf-cli function deploy create --input-file examples/deploy-function.json
$
$
# View deployment details
$
./nvcf-cli function deploy get \
>
  --function-id <function-id> \
>
  --version-id <version-id>
$
$
# Update an existing deployment
$
./nvcf-cli function deploy update \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --gpu "A10G" \
>
  --instance-type "NCP.GPU.A10G_1x" \
>
  --min-instances 2 \
>
  --max-instances 4
$
$
# Remove a deployment
$
./nvcf-cli function deploy remove \
>
  --function-id <function-id> \
>
  --version-id <version-id>

Key function deploy create flags:

Example deployment JSON:

1
{
2
  "deploymentSpecifications": [
3
    {
4
      "gpu": "A10G",
5
      "instanceType": "NCP.GPU.A10G_1x",
6
      "minInstances": 1,
7
      "maxInstances": 1
8
    }
9
  ]
10
}

List and Get Functions

$
# List all functions
$
./nvcf-cli function list
$
$
# List function IDs only
$
./nvcf-cli function list-ids
$
$
# List versions of a specific function
$
./nvcf-cli function list-versions <function-id>
$
$
# Get details of a specific function version
$
./nvcf-cli function get \
>
  --function-id <function-id> \
>
  --version-id <version-id>
$
$
# Get details as raw JSON
$
./nvcf-cli function get \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --json

Update Function

$
# Update function tags
$
./nvcf-cli function update \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --tags "production,v2"
$
$
# Update LLM model routing config
$
./nvcf-cli function update \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --llm-model-update "name=dummy-model,routingMethod=round_robin,tokenRateLimit=1000-S"
$
$
# Update from JSON file
$
./nvcf-cli function update \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --input-file metadata-update.json

LLM model updates can also be provided in the input file:

1
{
2
  "functionId": "<function-id>",
3
  "versionId": "<version-id>",
4
  "modelUpdates": [
5
    {
6
      "modelName": "dummy-model",
7
      "llmConfig": {
8
        "routingMethod": "round_robin",
9
        "tokenRateLimit": "1000-S,5000-M,100000-H,500000-D,1000000-W"
10
      }
11
    }
12
  ]
13
}

Invoke Function

$
# Invoke using saved context
$
./nvcf-cli function invoke --request-body '{"input": "Hello, World!"}'
$
$
# gRPC invocation
$
./nvcf-cli function invoke --grpc --request-body '{"input": "test"}'
$
$
# gRPC with custom service and method
$
./nvcf-cli function invoke --grpc \
>
  --grpc-service "MyService" \
>
  --grpc-method "Predict" \
>
  --request-body '{"input": "test"}'
$
$
# Invoke with explicit IDs and timeout
$
./nvcf-cli function invoke \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --request-body '{"input": "Hello!"}' \
>
  --timeout 120
$
$
# Invoke an LLM function with the chat completions path
$
./nvcf-cli function invoke \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --model-name dummy-model \
>
  --inference-url /v1/chat/completions \
>
  --request-body '{"messages":[{"role":"user","content":"Hello"}],"stream":true}'
$
$
# Invoke another OpenAI-compatible LLM path
$
./nvcf-cli function invoke \
>
  --function-id <function-id> \
>
  --version-id <version-id> \
>
  --model-name dummy-model \
>
  --inference-url /v1/embeddings \
>
  --request-body '{"input":"NVCF embeddings check"}'

Note: The CLI function invoke command detects LLM functions automatically. For LLM functions, --model-name and --inference-url are required. The CLI uses the LLM invocation route and sets the OpenAI model value to <function-id>/<model-name>.

For LLM Gateway endpoint behavior, routing, and session stickiness details, see LLM Gateway.

For raw HTTP invocation, HTTP streaming, gRPC metadata, and invocation error behavior, see Generic HTTP Function Invocation and gRPC Function Invocation.

Additional function invoke flags:

Queue Management

$
# Get queue status for a function
$
./nvcf-cli function queue status <function-id> <version-id>
$
$
# Get position for a specific request
$
./nvcf-cli function queue position <request-id>

Delete Function

$
# Delete current function from state
$
./nvcf-cli function delete
$
$
# Delete specific function
$
./nvcf-cli function delete --function-id <func-id> --version-id <ver-id>
$
$
# Delete deployment only (keep function definition)
$
./nvcf-cli function delete --deployment-only --graceful

Registry Credentials Commands

Manage container registry credentials for function images and Helm charts. For comprehensive setup instructions including IAM configuration for AWS ECR, see third-party-registries-self-hosted.

$
# Add registry credentials using base64 secret
$
./nvcf-cli registry-credential add \
>
  --hostname "nvcr.io" \
>
  --secret "<BASE64_ENCODED_USERNAME:PASSWORD>" \
>
  --artifact-type CONTAINER \
>
  --description "NGC Container Registry"
$
$
# Add registry credentials using username/password
$
./nvcf-cli registry-credential add \
>
  --hostname "nvcr.io" \
>
  --username "<USERNAME>" \
>
  --password "<PASSWORD>" \
>
  --artifact-type CONTAINER
$
$
# List registry credentials (with optional filters)
$
./nvcf-cli registry-credential list
$
./nvcf-cli registry-credential list --artifact-type CONTAINER
$
./nvcf-cli registry-credential list --provisioned-by USER
$
$
# Get details for a specific credential
$
./nvcf-cli registry-credential get <credential-id>
$
$
# Update a credential
$
./nvcf-cli registry-credential update <credential-id> \
>
  --username "<NEW_USERNAME>" \
>
  --password "<NEW_PASSWORD>"
$
$
# Delete registry credentials
$
./nvcf-cli registry-credential delete <credential-id>
$
./nvcf-cli registry-credential delete <credential-id> --force
$
$
# List recognized registries
$
./nvcf-cli registry-credential list-recognized

Troubleshooting

Authentication Errors

401 Unauthorized on function creation:

$
# Regenerate admin token
$
./nvcf-cli init --debug
$
$
# Verify token is being used
$
./nvcf-cli function create --debug --input-file function.json
$
# Look for: "Using FUNCTION TOKEN for POST"

403 Forbidden on invocation:

$
# Regenerate API key
$
./nvcf-cli api-key generate --validate
$
$
# Verify API key is being used
$
./nvcf-cli function invoke --debug --request-body '{"input": "test"}'
$
# Look for: "Using API KEY for POST"

Token Expiration

$
# Check token status
$
./nvcf-cli status
$
$
# Refresh admin token
$
./nvcf-cli refresh
$
$
# Regenerate API key
$
./nvcf-cli api-key generate

Connection Issues

$
# Enable debug mode to see request details
$
./nvcf-cli function list --debug
$
$
# Verify CLI state and configuration
$
./nvcf-cli status

Token Usage Summary

For additional troubleshooting, see self-hosted-troubleshooting.