CLI Reference

Complete reference for the aicr command-line interface.

Overview

AICR provides a four-step workflow for optimizing GPU infrastructure:

┌──────────────┐      ┌──────────────┐      ┌──────────────┐      ┌──────────────┐
│   Snapshot   │─────▶│    Recipe    │─────▶│   Validate   │─────▶│    Bundle    │
└──────────────┘      └──────────────┘      └──────────────┘      └──────────────┘

Step 1: Capture system configuration
Step 2: Generate optimization recipes
Step 3: Validate constraints against cluster
Step 4: Create deployment bundles

Global Flags

Available for all commands:

Flag	Short	Type	Default	Description
`--debug`	`-d`	bool	false	Enable debug logging (text mode with full metadata)
`--log-json`		bool	false	Enable JSON logging (structured output for machine parsing)
`--help`	`-h`	bool	false	Show help
`--version`	`-v`	bool	false	Show version

Logging Modes

AICR supports three logging modes:

CLI Mode (default): Minimal user-friendly output
- Just message text without timestamps or metadata
- Error messages display in red (ANSI color)
- Example: Snapshot captured successfully
Text Mode (--debug): Debug output with full metadata
- Key=value format with time, level, source location
- Example: time=2025-01-06T10:30:00.123Z level=INFO module=aicr version=v1.0.0 msg="snapshot started"
JSON Mode (--log-json): Structured JSON for automation
- Machine-readable format for log aggregation
- Example: {"time":"2025-01-06T10:30:00.123Z","level":"INFO","msg":"snapshot started"}

Examples:

# Default: Clean CLI output
aicr snapshot

# Debug mode: Full metadata
aicr --debug snapshot

# JSON mode: Structured logs
aicr --log-json snapshot

# Combine with other flags
aicr --debug --output system.yaml snapshot

Commands

aicr snapshot

Capture comprehensive system configuration including OS, GPU, Kubernetes, and SystemD settings.

Synopsis:

aicr snapshot [flags]

Flags:

Flag	Short	Type	Default	Description
`--output`	`-o`	string	stdout	Output destination: file path, ConfigMap URI (cm://namespace/name), or stdout
`--format`	`-f`	string	yaml	Output format: json, yaml, table
`--kubeconfig`	`-k`	string	~/.kube/config	Path to kubeconfig file (overrides KUBECONFIG env)
`--namespace`	`-n`	string	gpu-operator	Kubernetes namespace for agent deployment
`--image`		string	ghcr.io/nvidia/aicr:latest	Container image for agent Job
`--job-name`		string	aicr	Name for the agent Job
`--service-account-name`		string	aicr	ServiceAccount name for agent Job
`--node-selector`		string[]		Node selector for agent scheduling (key=value, repeatable)
`--toleration`		string[]	all taints	Tolerations for agent scheduling (key=value:effect, repeatable). Default: all taints tolerated (uses `operator: Exists`). Only specify to restrict which taints are tolerated.
`--timeout`		duration	5m	Timeout for agent Job completion
`--no-cleanup`		bool	false	Skip removal of Job and RBAC resources on completion. Warning: leaves a cluster-admin ClusterRoleBinding active.
`--privileged`		bool	true	Run agent in privileged mode (required for GPU/SystemD collectors). Set to false for PSS-restricted namespaces.
`--template`		string		Path to Go template file for custom output formatting (requires YAML format)
`--max-nodes-per-entry`		int	0	Maximum node names per taint/label entry in topology collection (0 = unlimited)

Output Destinations:

stdout: Default when no -o flag specified
File: Local file path (/path/to/snapshot.yaml)
ConfigMap: Kubernetes ConfigMap URI (cm://namespace/configmap-name)

What it captures:

SystemD Services: containerd, docker, kubelet configurations
OS Configuration: grub, kmod, sysctl, release info
Kubernetes: server version, images, ClusterPolicy
GPU: driver version, CUDA, MIG settings, hardware info
NodeTopology: node topology (cluster-wide taints and labels across all nodes)

Examples:

# Output to stdout (YAML)
aicr snapshot

# Save to file (JSON)
aicr snapshot --output system.json --format json

# Save to Kubernetes ConfigMap (requires cluster access)
aicr snapshot --output cm://gpu-operator/aicr-snapshot

# Debug mode
aicr --debug snapshot

# Table format (human-readable)
aicr snapshot --format table

# With custom kubeconfig
aicr snapshot --kubeconfig ~/.kube/prod-cluster

# Targeting specific nodes
aicr snapshot \
  --namespace gpu-operator \
  --node-selector accelerator=nvidia-h100 \
  --node-selector zone=us-west1-a

# With tolerations for tainted nodes
# (By default all taints are tolerated - only needed to restrict tolerations)
aicr snapshot \
  --toleration nvidia.com/gpu=present:NoSchedule

# Full example with all options
aicr snapshot \
  --kubeconfig ~/.kube/config \
  --namespace gpu-operator \
  --image ghcr.io/nvidia/aicr:v0.8.0 \
  --job-name snapshot-gpu-nodes \
  --service-account-name aicr \
  --node-selector accelerator=nvidia-h100 \
  --toleration nvidia.com/gpu:NoSchedule \
  --timeout 10m \
  --output cm://gpu-operator/aicr-snapshot \
  --no-cleanup

# Custom template formatting
aicr snapshot --template examples/templates/snapshot-template.md.tmpl

# Template with file output
aicr snapshot --template examples/templates/snapshot-template.md.tmpl --output report.md

# With custom template
aicr snapshot \
  --namespace gpu-operator \
  --template examples/templates/snapshot-template.md.tmpl \
  --output cluster-report.yaml

Custom Templates:

The --template flag enables custom output formatting using Go templates with Sprig functions. Templates receive the full Snapshot struct:

# Available template data structure:
.Kind           # Resource kind ("Snapshot")
.APIVersion     # API version string
.Metadata       # Map of key-value pairs (timestamp, version, source-node)
.Measurements   # Array of Measurement objects
  .Type         # Measurement type (K8s, GPU, OS, SystemD, NodeTopology)
  .Subtypes     # Array of Subtype objects
    .Name       # Subtype name (e.g., "server", "smi", "grub")
    .Data       # Map of readings (key -> Reading with .String method)

# NodeTopology measurement type has subtypes: summary, taint, label
# Taint encoding: effect|value|node1,node2,...  (parseable with Sprig splitList "|")
# Label encoding: value|node1,node2,...

Example template extracting key cluster info:

cluster:
  kubernetes: {{ with index .Measurements 0 }}{{ range .Subtypes }}{{ if eq .Name "server" }}
    version: {{ (index .Data "version").String }}{{ end }}{{ end }}{{ end }}
  gpu: {{ range .Measurements }}{{ if eq .Type.String "GPU" }}{{ range .Subtypes }}{{ if eq .Name "smi" }}
    model: {{ (index .Data "gpu.model").String }}
    count: {{ (index .Data "gpu-count").String }}{{ end }}{{ end }}{{ end }}{{ end }}

See examples/templates/snapshot-template.md.tmpl for a complete example template that generates a concise cluster report.

Agent Deployment Mode:

When running against a cluster, AICR deploys a Kubernetes Job to capture the snapshot:

Deploys RBAC: ServiceAccount, Role, RoleBinding, ClusterRole, ClusterRoleBinding
Creates Job: Runs aicr snapshot as a container on the target node
Waits for completion: Monitors Job status with configurable timeout
Retrieves snapshot: Reads snapshot from ConfigMap after Job completes
Writes output: Saves snapshot to specified output destination
Cleanup: Deletes Job and RBAC resources (use --no-cleanup to keep for debugging)

Benefits of agent deployment:

Capture configuration from actual cluster nodes (not local machine)
No need to run kubectl manually
Programmatic deployment for automation/CI/CD
Reusable RBAC resources across multiple runs

Agent deployment requirements:

Kubernetes cluster access (via kubeconfig)
Cluster admin permissions (for RBAC creation)
GPU nodes with nvidia-smi (for GPU metrics)


**ConfigMap Output:**

When using ConfigMap URIs (`cm://namespace/name`), the snapshot is stored directly in Kubernetes:

```yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: aicr-snapshot
  namespace: gpu-operator
  labels:
    app.kubernetes.io/name: aicr
    app.kubernetes.io/component: snapshot
    app.kubernetes.io/version: v0.17.0
data:
  snapshot.yaml: |
    # Full snapshot content
  format: yaml
  timestamp: "2025-12-31T10:30:00Z"

Snapshot Structure:

apiVersion: aicr.nvidia.com/v1alpha1
kind: Snapshot
metadata:
  created: "2025-12-31T10:30:00Z"
  hostname: gpu-node-1
measurements:
  - type: SystemD
    subtypes: [...]
  - type: OS
    subtypes: [...]
  - type: K8s
    subtypes: [...]
  - type: GPU
    subtypes: [...]

aicr recipe

Generate optimized configuration recipes from query parameters or captured snapshots.

Synopsis:

aicr recipe [flags]

Modes:

Criteria File Mode (Recommended)

Generate recipes using a Kubernetes-style criteria file:

Flags:

Flag	Short	Type	Description
`--criteria`	`-c`	string	Path to criteria file (YAML/JSON), alternative to individual flags
`--output`	`-o`	string	Output file (default: stdout)
`--format`	`-f`	string	Format: json, yaml (default: yaml)
`--data`		string	External data directory to overlay on embedded data (see External Data)

The criteria file uses a Kubernetes-style format:

kind: RecipeCriteria
apiVersion: aicr.nvidia.com/v1alpha1
metadata:
  name: gb200-eks-ubuntu-training
spec:
  service: eks
  os: ubuntu
  accelerator: gb200
  intent: training
  nodes: 8

Individual CLI flags can override criteria file values:

# Load criteria from file
aicr recipe --criteria criteria.yaml

# Override service from file
aicr recipe --criteria criteria.yaml --service gke

# Save output to file
aicr recipe -c criteria.yaml -o recipe.yaml

Query Mode

Generate recipes using direct system parameters:

Flags:

Flag	Short	Type	Description
`--service`		string	K8s service: eks, gke, aks, oke
`--accelerator`	`--gpu`	string	Accelerator/GPU type: h100, gb200, a100, l40
`--intent`		string	Workload intent: training, inference
`--os`		string	OS family: ubuntu, rhel, cos, amazonlinux
`--platform`		string	Platform/framework type: kubeflow
`--nodes`		int	Number of GPU nodes in the cluster
`--output`	`-o`	string	Output file (default: stdout)
`--format`	`-f`	string	Format: json, yaml (default: yaml)
`--data`		string	External data directory to overlay on embedded data (see External Data)

Examples:

# Basic recipe for Ubuntu on EKS with H100
aicr recipe --os ubuntu --service eks --accelerator h100

# Training workload with multiple GPU nodes
aicr recipe \
  --service eks \
  --accelerator gb200 \
  --intent training \
  --os ubuntu \
  --nodes 8 \
  --format yaml

# Kubeflow training workload
aicr recipe \
  --service eks \
  --accelerator h100 \
  --intent training \
  --os ubuntu \
  --platform kubeflow

# Save to file (--gpu is an alias for --accelerator)
aicr recipe --os ubuntu --gpu h100 --output recipe.yaml

Snapshot Mode

Generate recipes from captured snapshots:

Flags:

Flag	Short	Type	Description
`--snapshot`	`-s`	string	Path/URI to snapshot (file path, URL, or cm://namespace/name)
`--intent`	`-i`	string	Workload intent: training, inference
`--output`	`-o`	string	Output destination (file, ConfigMap URI, or stdout)
`--format`		string	Format: json, yaml (default: yaml)
`--kubeconfig`	`-k`	string	Path to kubeconfig file (for ConfigMap URIs, overrides KUBECONFIG env)

Snapshot Sources:

File: Local file path (./snapshot.yaml)
URL: HTTP/HTTPS URL (https://example.com/snapshot.yaml)
ConfigMap: Kubernetes ConfigMap URI (cm://namespace/configmap-name)

Examples:

# Generate recipe from local snapshot file
aicr recipe --snapshot system.yaml --intent training

# From ConfigMap (requires cluster access)
aicr recipe --snapshot cm://gpu-operator/aicr-snapshot --intent training

# From ConfigMap with custom kubeconfig
aicr recipe \
  --snapshot cm://gpu-operator/aicr-snapshot \
  --kubeconfig ~/.kube/prod-cluster \
  --intent training

# Output to ConfigMap
aicr recipe -s system.yaml -o cm://gpu-operator/aicr-recipe

# Chain snapshot → recipe with ConfigMaps
aicr snapshot -o cm://default/snapshot
aicr recipe -s cm://default/snapshot -o cm://default/recipe

# With custom output
aicr recipe -s system.yaml -i inference -o recipe.yaml --format yaml

Output structure:

apiVersion: aicr.nvidia.com/v1alpha1
kind: Recipe
metadata:
  version: v1.0.0
  created: "2025-12-31T10:30:00Z"
  appliedOverlays:
    - base
    - eks
    - eks-training
    - gb200-eks-training
criteria:
  service: eks
  accelerator: gb200
  intent: training
  os: any
componentRefs:
  - name: gpu-operator
    version: v25.3.3
    order: 1
    repository: https://helm.ngc.nvidia.com/nvidia
constraints:
  driver:
    version: "580.82.07"
    cudaVersion: "13.1"

aicr validate

Validate a system snapshot against the constraints defined in a recipe to verify cluster compatibility. Supports multi-phase validation with different validation stages.

Synopsis:

aicr validate [flags]

Flags:

Flag	Short	Type	Description
`--recipe`	`-r`	string	Path/URI to recipe file containing constraints (required)
`--snapshot`	`-s`	string	Path/URI to snapshot file containing measurements (required)
`--phase`		string	Validation phase to run: readiness (default), deployment, performance, conformance, all
`--fail-on-error`		bool	Exit with non-zero status if any constraint fails (default: true)
`--output`	`-o`	string	Output destination (file or stdout, default: stdout)
`--format`	`-t`	string	Output format: json, yaml, table (default: yaml)
`--kubeconfig`	`-k`	string	Path to kubeconfig file (for ConfigMap URIs)

Input Sources:

File: Local file path (./recipe.yaml, ./snapshot.yaml)
URL: HTTP/HTTPS URL (https://example.com/recipe.yaml)
ConfigMap: Kubernetes ConfigMap URI (cm://namespace/configmap-name)

Validation Phases:

Validation can be run in different phases to validate different aspects of the deployment:

Phase	Description	When to Run
`readiness`	Evaluates constraints inline against snapshot (K8s version, OS, kernel) — no checks or Jobs	Before deploying any components
`deployment`	Validates component deployment health and expected resources	After deploying components
`performance`	Validates system performance and network fabric health	After components are running
`conformance`	Validates workload-specific requirements and conformance	Before running production workloads
`all`	Runs all phases sequentially with dependency logic	Complete end-to-end validation

Phase Dependencies:

Phases run sequentially when using --phase all
If a phase fails, subsequent phases are skipped
Use individual phases for targeted validation during specific deployment stages

Constraint Format:

Constraints use fully qualified measurement paths: {Type}.{Subtype}.{Key}

Constraint Path	Description
`K8s.server.version`	Kubernetes server version
`OS.release.ID`	Operating system identifier (ubuntu, rhel)
`OS.release.VERSION_ID`	OS version (24.04, 22.04)
`OS.sysctl./proc/sys/kernel/osrelease`	Kernel version
`GPU.info.type`	GPU hardware type

Supported Operators:

Operator	Example	Description
`>=`	`>= 1.30`	Greater than or equal (version comparison)
`<=`	`<= 1.33`	Less than or equal (version comparison)
`>`	`> 1.30`	Greater than (version comparison)
`<`	`< 2.0`	Less than (version comparison)
`==`	`== ubuntu`	Explicit equality
`!=`	`!= rhel`	Not equal
(none)	`ubuntu`	Exact string match

Examples:

# Validate snapshot against recipe (default: readiness phase)
aicr validate --recipe recipe.yaml --snapshot snapshot.yaml

# Validate specific phase
aicr validate \
  --recipe recipe.yaml \
  --snapshot snapshot.yaml \
  --phase deployment

# Run all validation phases
aicr validate \
  --recipe recipe.yaml \
  --snapshot snapshot.yaml \
  --phase all

# Load snapshot from ConfigMap
aicr validate \
  --recipe recipe.yaml \
  --snapshot cm://gpu-operator/aicr-snapshot

# Save results to file
aicr validate \
  --recipe recipe.yaml \
  --snapshot cm://gpu-operator/aicr-snapshot \
  --output validation-results.yaml

# Validate readiness phase before installing components
aicr validate \
  --recipe recipe.yaml \
  --snapshot snapshot.yaml \
  --phase readiness \
  --fail-on-error

# Validate deployment phase after components are installed
aicr validate \
  --recipe recipe.yaml \
  --snapshot snapshot.yaml \
  --phase deployment

# Run performance validation
aicr validate \
  --recipe recipe.yaml \
  --snapshot snapshot.yaml \
  --phase performance

# JSON output format
aicr validate \
  --recipe recipe.yaml \
  --snapshot snapshot.yaml \
  --format json

# With custom kubeconfig
aicr validate \
  --recipe recipe.yaml \
  --snapshot cm://gpu-operator/aicr-snapshot \
  --kubeconfig ~/.kube/prod-cluster

Output Structure (Readiness Phase):

apiVersion: aicr.nvidia.com/v1alpha1
kind: ValidationResult
metadata:
  timestamp: "2025-12-31T10:30:00Z"
  version: v0.14.0
recipeSource: recipe.yaml
snapshotSource: cm://gpu-operator/aicr-snapshot
summary:
  passed: 5
  failed: 0
  skipped: 0
  total: 5
  status: pass
  duration: 20.5µs
phases:
  readiness:
    status: pass
    constraints:
      - name: K8s.server.version
        expected: '>= 1.30'
        actual: v1.30.14-eks-3025e55
        status: passed
      - name: OS.release.ID
        expected: ubuntu
        actual: ubuntu
        status: passed
    duration: 20.5µs

Output Structure (All Phases):

apiVersion: aicr.nvidia.com/v1alpha1
kind: ValidationResult
metadata:
  timestamp: "2025-12-31T10:30:00Z"
  version: v0.14.0
recipeSource: recipe.yaml
snapshotSource: snapshot.yaml
summary:
  passed: 3
  failed: 0
  skipped: 1
  total: 4
  status: pass
  duration: 58.4µs
phases:
  readiness:
    status: pass
    constraints:
      - name: K8s.server.version
        expected: '>= 1.32.4'
        actual: v1.35.0
        status: passed
      - name: OS.release.ID
        expected: ubuntu
        actual: ubuntu
        status: passed
    duration: 20.7µs
  deployment:
    status: pass
    checks:
      - name: gpu-operator.version
        status: pass
      - name: expected-resources
        status: pass
    duration: 1.2µs
  performance:
    status: pass
    checks:
      - name: nccl-bandwidth-test
        status: pass
      - name: fabric-health-check
        status: pass
    duration: 1.2µs
  conformance:
    status: skipped
    reason: conformance phase not configured in recipe
    duration: 0.8µs

Validation Statuses:

Status	Description
`passed`	Constraint satisfied
`failed`	Constraint not satisfied
`skipped`	Constraint could not be evaluated (missing data, invalid path)

Summary Status:

Status	Description
`pass`	All constraints passed
`fail`	One or more constraints failed
`partial`	Some constraints skipped, none failed

aicr bundle

Generate deployment-ready bundles from recipes containing Helm values, manifests, scripts, and documentation.

Synopsis:

aicr bundle [flags]

Flags:

Flag	Short	Type	Description
`--recipe`	`-r`	string	Path to recipe file (required)
`--output`	`-o`	string	Output directory (default: current dir)
`--deployer`		string	Deployment method: helm (default), argocd
`--repo`		string	Git repository URL for ArgoCD applications (only used with `--deployer argocd`)
`--set`		string[]	Override values in bundle files (repeatable)
`--data`		string	External data directory to overlay on embedded data (see External Data)
`--system-node-selector`		string[]	Node selector for system components (format: key=value, repeatable)
`--system-node-toleration`		string[]	Toleration for system components (format: key=value:effect, repeatable)
`--accelerated-node-selector`		string[]	Node selector for accelerated/GPU nodes (format: key=value, repeatable)
`--accelerated-node-toleration`		string[]	Toleration for accelerated/GPU nodes (format: key=value:effect, repeatable)
`--workload-gate`		string	Taint for skyhook-operator runtime required (format: key=value:effect or key:effect). This is a day 2 option for cluster scaling operations.
`--workload-selector`		string[]	Label selector for skyhook-customizations to prevent eviction of running training jobs (format: key=value, repeatable). Required when skyhook-customizations is enabled with training intent.
`--nodes`		int	Estimated number of GPU nodes (default: 0 = unset). At bundle time, written to Helm value paths declared in the registry under `nodeScheduling.nodeCountPaths`.
`--attest`		bool	Enable bundle attestation and binary provenance verification. Requires OIDC authentication. See Bundle Attestation.
`--certificate-identity-regexp`		string	Override the certificate identity pattern for binary attestation verification. Must contain `"NVIDIA/aicr"`. For testing only.

Node Scheduling:

The --accelerated-node-selector and --accelerated-node-toleration flags control scheduling for GPU-specific components:

Flag	GPU Daemonsets	NFD Workers
`--accelerated-node-selector`	Applied (restricts to GPU nodes)	Not applied (NFD runs on all nodes)
`--accelerated-node-toleration`	Applied	Applied
`--system-node-selector`	Not applied	Not applied
`--system-node-toleration`	Not applied	Not applied

NFD (Node Feature Discovery) workers must run on all nodes (GPU, CPU, and system) to detect hardware features. This matches the gpu-operator default behavior where NFD workers also run on control-plane nodes. The --accelerated-node-selector is intentionally not applied to NFD workers so they are not restricted to GPU nodes.

Note: When no --accelerated-node-toleration is specified, a default toleration (operator: Exists) is applied to both GPU daemonsets and NFD workers, allowing them to run on nodes with any taint.

Example:

aicr bundle --recipe recipe.yaml \
  --accelerated-node-selector nodeGroup=gpu-worker \
  --accelerated-node-toleration dedicated=worker-workload:NoSchedule \
  --accelerated-node-toleration dedicated=worker-workload:NoExecute \
  --system-node-selector nodeGroup=system-worker \
  --system-node-toleration dedicated=system-workload:NoSchedule \
  --system-node-toleration dedicated=system-workload:NoExecute \
  --output bundle

Cluster node requirements: This example assumes the cluster has nodes labeled nodeGroup=system-worker with taints dedicated=system-workload:NoSchedule,NoExecute for system infrastructure, and GPU nodes labeled nodeGroup=gpu-worker with taints dedicated=worker-workload:NoSchedule,NoExecute.

This results in:

GPU daemonsets (driver, device-plugin, toolkit, dcgm): nodeSelector=nodeGroup=gpu-worker + tolerations for dedicated=worker-workload with both NoSchedule and NoExecute
NFD workers: no nodeSelector (runs on all nodes) + tolerations for dedicated=worker-workload with both NoSchedule and NoExecute
System components (gpu-operator controller, NFD gc/master, dynamo grove, kgateway proxy): nodeSelector=nodeGroup=system-worker + tolerations for dedicated=system-workload with both NoSchedule and NoExecute

Behavior:

All components from the recipe are bundled automatically
Each component creates a subdirectory in the output directory
Components are deployed in the order specified by deploymentOrder in the recipe

Deployment Methods (--deployer):

The --deployer flag controls how deployment artifacts are generated:

Method	Description
`helm`	(Default) Generates Helm charts with values for deployment
`argocd`	Generates ArgoCD Application manifests for GitOps deployment

Deployment Order:

All deployers respect the deploymentOrder field from the recipe, ensuring components are installed in the correct sequence:

Helm: Components listed in README in deployment order
ArgoCD: Uses argocd.argoproj.io/sync-wave annotation (0 = first, 1 = second, etc.)

Value Overrides (--set):

Override any value in the generated bundle files using dot notation:

--set bundler:path.to.field=value

Format: bundler:path=value where:

bundler - Bundler name (e.g., gpuoperator, networkoperator, certmanager, skyhook-operator, nvsentinel)
path - Dot-separated path to the field
value - New value to set

Behavior:

Duplicate keys: When the same bundler:path is specified multiple times, the last value wins
Array values: Individual array elements cannot be overridden (no [0] index syntax). Arrays can only be replaced entirely via recipe overrides, not via --set flags. Use recipe-level overrides in componentRefs[].overrides if you need to replace an entire array.
Type conversion: String values are automatically converted to appropriate types (true/false → bool, numeric strings → numbers)

Examples:

# Generate all bundles
aicr bundle --recipe recipe.yaml --output ./bundles

# Override values in GPU Operator bundle
aicr bundle -r recipe.yaml \
  --set gpuoperator:gds.enabled=true \
  --set gpuoperator:driver.version=570.86.16 \
  -o ./bundles

# Override multiple components
aicr bundle -r recipe.yaml \
  --set gpuoperator:mig.strategy=mixed \
  --set networkoperator:rdma.enabled=true \
  --set networkoperator:sriov.enabled=true \
  -o ./bundles

# Override cert-manager resources
aicr bundle -r recipe.yaml \
  --set certmanager:controller.resources.memory.limit=512Mi \
  --set certmanager:webhook.resources.cpu.limit=200m \
  -o ./bundles

# Override Skyhook manager resources
aicr bundle -r recipe.yaml \
  --set skyhook-operator:manager.resources.cpu.limit=500m \
  --set skyhook-operator:manager.resources.memory.limit=256Mi \
  -o ./bundles

# Schedule system components on specific node pool
aicr bundle -r recipe.yaml \
  --system-node-selector nodeGroup=system-pool \
  --system-node-toleration dedicated=system:NoSchedule \
  -o ./bundles

# Schedule GPU workloads on labeled GPU nodes
aicr bundle -r recipe.yaml \
  --accelerated-node-selector nvidia.com/gpu.present=true \
  --accelerated-node-toleration nvidia.com/gpu=present:NoSchedule \
  -o ./bundles

# Combined: separate system and GPU scheduling
aicr bundle -r recipe.yaml \
  --system-node-selector nodeGroup=system-pool \
  --system-node-toleration dedicated=system:NoSchedule \
  --accelerated-node-selector accelerator=nvidia-h100 \
  --accelerated-node-toleration nvidia.com/gpu=present:NoSchedule \
  -o ./bundles

# Set estimated GPU node count (writes to nodeCountPaths in registry)
aicr bundle -r recipe.yaml --nodes 8 -o ./bundles

# Day 2 options: workload-gate and workload-selector for skyhook
aicr bundle -r recipe.yaml \
  --workload-gate skyhook.io/runtime-required=true:NoSchedule \
  --workload-selector workload-type=training \
  -o ./bundles

# Generate an attested bundle (opens browser for OIDC auth)
aicr bundle -r recipe.yaml --attest -o ./bundles

# In GitHub Actions (OIDC token detected automatically)
aicr bundle -r recipe.yaml --attest -o ./bundles

# Generate ArgoCD Application manifests for GitOps
aicr bundle -r recipe.yaml --deployer argocd -o ./bundles

# ArgoCD with Git repository URL (avoids placeholder in app-of-apps.yaml)
aicr bundle -r recipe.yaml --deployer argocd \
  --repo https://github.com/my-org/my-gitops-repo.git \
  -o ./bundles

# Combine deployer with value overrides
aicr bundle -r recipe.yaml \
  --deployer argocd \
  -o ./bundles

Bundle structure (with default Helm deployer):

bundles/
├── README.md                      # Deployment guide with ordered steps
├── deploy.sh                      # One-command deployment script
├── recipe.yaml                    # Recipe used to generate bundle
├── checksums.txt                  # SHA256 checksums
├── attestation/                   # Present when --attest is used
│   ├── bundle-attestation.sigstore.json   # SLSA Build Provenance v1
│   └── aicr-attestation.sigstore.json     # Binary SLSA provenance chain
├── gpu-operator/
│   ├── values.yaml                # Component-specific Helm values
│   ├── README.md                  # Per-component install/upgrade/uninstall
│   └── manifests/                 # Additional manifests (if any)
│       └── dcgm-exporter.yaml
└── cert-manager/
    ├── values.yaml
    └── README.md

ArgoCD bundle structure (with --deployer argocd):

bundles/
├── app-of-apps.yaml               # Parent Application (bundle root)
├── recipe.yaml                    # Recipe used to generate bundle
├── gpu-operator/
│   ├── values.yaml                # Helm values for GPU Operator
│   ├── manifests/                 # Additional manifests (ClusterPolicy, etc.)
│   └── argocd/
│       └── application.yaml       # ArgoCD Application (sync-wave: 0)
├── network-operator/
│   ├── values.yaml                # Helm values for Network Operator
│   └── argocd/
│       └── application.yaml       # ArgoCD Application (sync-wave: 1)
└── README.md                      # ArgoCD deployment guide

Day 2 Options:

The --workload-gate and --workload-selector flags are day 2 operational options for cluster scaling operations:

--workload-gate: Specifies a taint for skyhook-operator's runtime required feature. This ensures nodes are properly configured before workloads can schedule on them during cluster scaling. The taint is configured in the skyhook-operator Helm values file at controllerManager.manager.env.runtimeRequiredTaint. For more information about runtime required, see the skyhook documentation.
--workload-selector: Specifies a label selector for skyhook-customizations to prevent skyhook from evicting running training jobs. This is critical for training workloads where job eviction would cause significant disruption. The selector is set in the Skyhook CR manifest (tuning.yaml) in the spec.workloadSelector.matchLabels field.

Estimated node count (--nodes):

The --nodes flag is a bundle-time option: it is applied when you run aicr bundle, not when you run aicr recipe. The value is written to each component's Helm values at the paths declared in the registry under nodeScheduling.nodeCountPaths.

When to use: Pass the expected or typical number of GPU nodes (e.g. size of your node pool). Use 0 (default) to leave the value unset.
Where it goes: Components that define nodeCountPaths in the registry receive the value at those paths in their generated values.yaml.
Example: aicr bundle -r recipe.yaml --nodes 8 -o ./bundles writes 8 to every path listed in each component's nodeScheduling.nodeCountPaths.

Component Validation System:

AICR includes a component-driven validation system that automatically checks bundle configuration and displays warnings or errors during bundle generation. Validations are defined in the component registry and run automatically when components are included in a recipe.

How Validations Work:

Automatic Execution: When generating a bundle, validations are automatically executed for each component in the recipe
Condition-Based: Validations can be configured to run only when specific conditions are met (e.g., intent, service, accelerator)
Severity Levels: Each validation can be configured as a "warning" (non-blocking) or "error" (blocking)
Custom Messages: Each validation can include an optional detail message that provides actionable guidance

Validation Warnings:

When generating bundles with skyhook-customizations enabled, validation warnings are displayed for missing configuration:

Workload Selector Warning: When skyhook-customizations is enabled with training intent, if --workload-selector is not set, a warning will be displayed:

Warning: skyhook-customizations is enabled but --workload-selector is not set. 
This may cause skyhook to evict running training jobs. Consider setting --workload-selector to prevent eviction.

Accelerated Selector Warning: When skyhook-customizations is enabled with training or inference intent, if --accelerated-node-selector is not set, a warning will be displayed:

Warning: skyhook-customizations is enabled but --accelerated-node-selector is not set. 
Without this selector, the customization will run on all nodes. Consider setting --accelerated-node-selector to target specific nodes.

Viewing Validation Warnings:

Validation warnings are displayed in the bundle output after successful generation:

Note:
  ⚠ Warning: skyhook-customizations is enabled but --workload-selector is not set. This may cause skyhook to evict running training jobs. Consider setting --workload-selector to prevent eviction.
  ⚠ Warning: skyhook-customizations is enabled but --accelerated-node-selector is not set. Without this selector, the customization will run on all nodes. Consider setting --accelerated-node-selector to target specific nodes.

Resolving Validation Warnings:

To resolve the warnings, include the appropriate flags when generating the bundle:

# Resolve workload selector warning
aicr bundle -r recipe.yaml \
  --workload-selector workload-type=training \
  -o ./bundle

# Resolve accelerated selector warning
aicr bundle -r recipe.yaml \
  --accelerated-node-selector nodeGroup=gpu-worker \
  -o ./bundle

# Resolve both warnings
aicr bundle -r recipe.yaml \
  --workload-selector workload-type=training \
  --accelerated-node-selector nodeGroup=gpu-worker \
  -o ./bundle

Examples:

# Generate bundle with day 2 options for training workloads
aicr bundle -r recipe.yaml \
  --workload-gate skyhook.io/runtime-required=true:NoSchedule \
  --workload-selector workload-type=training \
  --workload-selector intent=training \
  --accelerated-node-selector accelerator=nvidia-h100 \
  -o ./bundles

# Generate bundle for inference workloads with accelerated selector
aicr bundle -r recipe.yaml \
  --accelerated-node-selector accelerator=nvidia-h100 \
  -o ./bundles

ArgoCD Applications use multi-source to:

Pull Helm charts from upstream repositories
Apply values.yaml from your GitOps repository
Deploy additional manifests from component's manifests/ directory (if present)

Bundle Attestation

Prerequisite: The --attest flag requires a binary installed using the install script, which includes a cryptographic attestation from NVIDIA. Binaries installed via go install or manual download do not include this file and cannot use --attest.

When --attest is passed, the bundle command performs four steps:

Verifies the binary attestation file exists — The running aicr binary must have a valid SLSA provenance file (aicr-attestation.sigstore.json) alongside it, included by the install script from a release archive. If missing, the command fails immediately with guidance on how to install correctly.
Acquires an OIDC token — In GitHub Actions the ambient OIDC token is used automatically. Locally, a browser window opens for Sigstore OIDC authentication.
Verifies the binary's own attestation — Cryptographically verifies the SLSA provenance binds to the running binary and was signed by NVIDIA CI. This ensures only NVIDIA-built binaries can produce attested bundles.
Signs the bundle — Creates a SLSA Build Provenance v1 in-toto statement binding the creator's identity to the bundle content (via checksums.txt digest) and the binary that produced it.
Writes attestation files — attestation/bundle-attestation.sigstore.json and attestation/aicr-attestation.sigstore.json are added to the bundle output.

Attestation is opt-in; bundles are unsigned by default. Signing uses Sigstore keyless signing (Fulcio CA + Rekor transparency log). For verification, see aicr verify.

Deploying a bundle:

# Navigate to bundle
cd bundles/gpu-operator

# Review configuration
cat values.yaml
cat README.md

# Verify integrity
sha256sum -c checksums.txt

# Deploy to cluster
chmod +x deploy.sh && ./deploy.sh

Note: deploy.sh and undeploy.sh are convenience scripts — not the only deployment path. Each component subdirectory contains a README.md with the exact helm upgrade --install command for manual or pipeline-driven deployment.

Deploy Script Behavior (`deploy.sh`)

The deploy script installs components in the order specified by deploymentOrder in the recipe.

Flags:

Flag	Description
`--no-wait`	Skip `helm --wait` for each component (faster, no readiness check)
`--best-effort`	Continue past individual component failures instead of exiting

Unknown flags are rejected with an error to catch typos (e.g., --best-effrot).

Pre-install manifests and CRD ordering:

Some components have pre-install manifests (CRDs, namespaces, ConfigMaps) that must exist before helm install. The script applies these with kubectl apply before the Helm install. On first deploy, CRD-dependent resources may produce no matches for kind warnings because the CRD hasn't been registered yet — these warnings are suppressed. All other kubectl apply errors (auth failures, webhook denials, bad manifests) fail the script immediately.

After helm install, the same manifests are re-applied as post-install to ensure CRD-dependent resources are created.

Async components:

Components that use operator patterns with custom resources that reconcile asynchronously (e.g., kai-scheduler) are installed without --wait to avoid Helm timing out on CR readiness.

DRA kubelet plugin registration:

After installing nvidia-dra-driver-gpu, the script automatically restarts the DRA kubelet plugin daemonset. This is a best-effort mitigation for a known issue: after uninstall/reinstall, the kubelet's plugin watcher (fsnotify) may not detect new registration sockets, causing DRA driver gpu.nvidia.com is not registered errors.

If DRA pods fail with this error after redeployment, the daemonset restart alone may not be sufficient — a node reboot is required to reset the kubelet's plugin registration state. To reboot GPU nodes:

# Cordon, drain, and reboot the affected node
kubectl cordon <node-name>
kubectl drain <node-name> --ignore-daemonsets --delete-emptydir-data
# Reboot via cloud provider (e.g., AWS EC2 console or CLI)
aws ec2 reboot-instances --instance-ids <instance-id>
# Uncordon after node returns
kubectl uncordon <node-name>

Undeploy Script Behavior (`undeploy.sh`)

The undeploy script removes components in reverse deployment order.

Flags:

Flag	Description
`--keep-namespaces`	Skip namespace deletion after component removal
`--delete-pvcs`	Delete all PVCs in component namespaces (default: off)
`--timeout SECONDS`	Helm uninstall timeout per component (default: 120)

PVC preservation (default):

PVCs are not deleted by default. This preserves historical data (Prometheus metrics, Alertmanager state, etcd data) across redeploys. If an EBS-backed PV has an AZ mismatch after redeployment, the PVC will stay Pending with a clear error — the operator can then decide to delete it manually.

Pass --delete-pvcs to delete all PVCs. Protected namespaces (kube-system, kube-public, kube-node-lease, default) are always excluded from PVC deletion to prevent accidental removal of non-bundle PVCs.

Shared namespace ordering:

When multiple components share a namespace (e.g., monitoring contains kube-prometheus-stack, prometheus-adapter, and k8s-ephemeral-storage-metrics), all components are uninstalled first, then PVC and namespace cleanup runs once. This prevents hangs caused by kubernetes.io/pvc-protection finalizers — if a StatefulSet owner is still running when PVC deletion is attempted, the delete blocks indefinitely.

Stuck release handling:

If a Helm release is in a pending-install or pending-upgrade state (from an interrupted deploy), the script retries with --no-hooks to force removal.

Orphaned webhook cleanup:

After uninstalling each component, the script checks for orphaned validating/mutating webhooks whose backing service no longer exists. Fail-closed webhooks with missing services block all pod creation, so these are deleted proactively.

aicr verify

Verify the integrity and attestation chain of a bundle. Verification is fully offline — no network calls are made.

Synopsis:

aicr verify <bundle-dir> [flags]

Flags:

Flag	Type	Default	Description
`--min-trust-level`	string	`max`	Minimum required trust level. `max` auto-detects the highest achievable level and verifies against it. Explicit levels: `verified`, `attested`, `unverified`, `unknown`.
`--require-creator`	string		Require a specific creator identity, matched against the bundle attestation signing certificate.
`--cli-version-constraint`	string		Version constraint for the aicr CLI version in the attestation predicate. Supports `>=`, `>`, `<=`, `<`, `==`, `!=`. A bare version (e.g. `"0.8.0"`) defaults to `>=`.
`--certificate-identity-regexp`	string		Override the certificate identity pattern for binary attestation verification. Must contain `"NVIDIA/aicr"`. For testing only.
`--format`	string	`text`	Output format: `text` or `json`.

Trust Levels:

Level	Name	Criteria
4	`verified`	Full chain: checksums + bundle attestation + binary attestation pinned to NVIDIA CI
3	`attested`	Chain verified but binary attestation missing or external data (`--data`) was used
2	`unverified`	Checksums valid, `--attest` was not used when creating the bundle
1	`unknown`	Missing or invalid checksums

Verification steps:

Checksums — verifies all content files match checksums.txt
Bundle attestation — cryptographic signature verified against Sigstore trusted root
Binary attestation — provenance chain verified with identity pinned to NVIDIA CI (on-tag.yaml workflow)

Examples:

# Auto-detect maximum trust level
aicr verify ./my-bundle

# Enforce a minimum trust level
aicr verify ./my-bundle --min-trust-level verified

# Require a specific bundle creator
aicr verify ./my-bundle --require-creator jdoe@company.com

# Require minimum CLI version used to create the bundle
aicr verify ./my-bundle --cli-version-constraint ">= 0.8.0"

# JSON output for CI pipelines
aicr verify ./my-bundle --format json

Stale root: If verification fails with certificate chain errors, run aicr trust update to refresh the Sigstore trusted root.

aicr trust update

Fetch the latest Sigstore trusted root from the TUF CDN and update the local cache at ~/.sigstore/root/. This is needed when Sigstore rotates signing keys (a few times per year).

Synopsis:

aicr trust update

No flags. This command contacts tuf-repo-cdn.sigstore.dev, verifies the update chain against the embedded TUF root, and writes the result to ~/.sigstore/root/.

When to run:

After initial installation (the install script runs this automatically)
When aicr verify reports a stale or expired trusted root
When Sigstore announces key rotation

Example:

aicr trust update

Complete Workflow Examples

File-Based Workflow

# Step 1: Capture system configuration
aicr snapshot --output snapshot.yaml

# Step 2: Generate optimized recipe for training workloads
aicr recipe \
  --snapshot snapshot.yaml \
  --intent training \
  --output recipe.yaml

# Step 3: Validate recipe constraints against snapshot
aicr validate \
  --recipe recipe.yaml \
  --snapshot snapshot.yaml

# Step 4: Create deployment bundle
aicr bundle \
  --recipe recipe.yaml \
  --output ./deployment

# Step 5: Deploy to cluster
cd deployment && chmod +x deploy.sh && ./deploy.sh

# Step 6: Verify deployment
kubectl get pods -n gpu-operator
kubectl logs -n gpu-operator -l app=nvidia-operator-validator

ConfigMap-Based Workflow (Kubernetes-Native)

# Step 1: Agent captures snapshot to ConfigMap (using CLI deployment)
aicr snapshot --output cm://gpu-operator/aicr-snapshot

# The CLI handles agent deployment automatically
# No manual kubectl steps needed

# Step 2: Generate recipe from ConfigMap
aicr recipe \
  --snapshot cm://gpu-operator/aicr-snapshot \
  --intent training \
  --output recipe.yaml

# Alternative: Write recipe to ConfigMap as well
aicr recipe \
  --snapshot cm://gpu-operator/aicr-snapshot \
  --intent training \
  --output cm://gpu-operator/aicr-recipe

# With custom kubeconfig (if not using default)
aicr recipe \
  --snapshot cm://gpu-operator/aicr-snapshot \
  --kubeconfig ~/.kube/prod-cluster \
  --intent training \
  --output recipe.yaml

# Step 3: Validate recipe constraints against cluster snapshot
aicr validate \
  --recipe recipe.yaml \
  --snapshot cm://gpu-operator/aicr-snapshot

# For CI/CD pipelines: exit non-zero on validation failure
aicr validate \
  --recipe recipe.yaml \
  --snapshot cm://gpu-operator/aicr-snapshot \
  --fail-on-error

# Step 4: Create bundle from recipe
aicr bundle \
  --recipe recipe.yaml \
  --output ./deployment

# Step 5: Deploy to cluster
cd deployment && chmod +x deploy.sh && ./deploy.sh

# Step 6: Verify deployment
kubectl get pods -n gpu-operator
kubectl logs -n gpu-operator -l app=nvidia-operator-validator

E2E Testing

Validate the complete workflow:

# Run all CLI integration tests (no cluster needed)
make e2e

# Run a single chainsaw test
AICR_BIN=$(find dist -maxdepth 2 -type f -name aicr | head -n 1)
chainsaw test --no-cluster --test-dir tests/chainsaw/cli/recipe-generation

Shell Completion

Generate shell completion scripts:

# Bash
aicr completion bash

# Zsh
aicr completion zsh

# Fish
aicr completion fish

# PowerShell
aicr completion pwsh

Installation:

Bash:

source <(aicr completion bash)
# Or add to ~/.bashrc for persistence
echo 'source <(aicr completion bash)' >> ~/.bashrc

Zsh:

source <(aicr completion zsh)
# Or add to ~/.zshrc
echo 'source <(aicr completion zsh)' >> ~/.zshrc

Environment Variables

AICR respects standard environment variables:

Variable	Description	Default
`KUBECONFIG`	Path to Kubernetes config file	`~/.kube/config`
`LOG_LEVEL`	Logging level: debug, info, warn, error	info
`NO_COLOR`	Disable colored output	false

Exit Codes

Code	Meaning
0	Success
1	General error
2	Invalid arguments
3	File I/O error
4	Kubernetes connection error
5	Recipe generation error

Common Usage Patterns

Quick Recipe Generation

aicr recipe --os ubuntu --accelerator h100 | jq '.componentRefs[]'

Save All Steps

aicr snapshot -o snapshot.yaml
aicr recipe -s snapshot.yaml -i training -o recipe.yaml
aicr bundle -r recipe.yaml -o ./bundles

JSON Processing

# Extract GPU Operator version from recipe
aicr recipe --os ubuntu --accelerator h100 --format json | \
  jq -r '.componentRefs[] | select(.name=="gpu-operator") | .version'

# Get all component versions
aicr recipe --os ubuntu --accelerator h100 --format json | \
  jq -r '.componentRefs[] | "\(.name): \(.version)"'

Multiple Environments

# Generate recipes for different cloud providers
for service in eks gke aks; do
  aicr recipe --os ubuntu --service $service --gpu h100 \
    --output recipe-${service}.yaml
done

Troubleshooting

Snapshot Fails

# Check GPU drivers
nvidia-smi

# Check Kubernetes access
kubectl cluster-info

# Run with debug
aicr --debug snapshot

Recipe Not Found

# Query parameters may not match any overlay
# Try broader query:
aicr recipe --os ubuntu --gpu h100

Bundle Generation Fails

# Verify recipe file
cat recipe.yaml

# Check bundler is valid
aicr bundle --help  # Shows available bundlers

# Run with debug
aicr --debug bundle -r recipe.yaml

External Data Directory

The --data flag enables extending or overriding the embedded recipe data with external files. This allows customization without rebuilding the CLI.

Overview

AICR embeds recipe data (overlays, component values, registry) at compile time. The --data flag layers an external directory on top, enabling:

Custom components: Add new components to the registry
Override values: Replace default component values files
Custom overlays: Add new recipe overlays for specific environments
Registry extensions: Add custom components while preserving embedded ones

Directory Structure

The external directory must mirror the embedded data structure:

my-data/
├── registry.yaml          # REQUIRED - merged with embedded registry
├── overlays/
│   └── base.yaml              # Optional - replaces embedded base.yaml
│   └── custom-overlay.yaml    # Optional - adds new overlay
└── components/
    └── gpu-operator/
        └── values.yaml        # Optional - replaces embedded values

Requirements

registry.yaml is required: The external directory must contain a registry.yaml file
Security validations: Symlinks are rejected, file size is limited (10MB default)
No path traversal: Paths containing .. are rejected

Merge Behavior

File Type	Behavior
`registry.yaml`	Merged - External components are added to embedded; same-named components are replaced
All other files	Replaced - External file completely replaces embedded if path matches

Usage Examples

# Use external data directory for recipe generation
aicr recipe --service eks --accelerator h100 --data ./my-data

# Use external data directory for bundle generation
aicr bundle --recipe recipe.yaml --data ./my-data --output ./bundles

# Combine with other flags
aicr recipe --service eks --gpu gb200 --intent training \
  --data ./custom-recipes \
  --output recipe.yaml

Example: Adding a Custom Component

Create external data directory:

mkdir -p my-data/components/my-operator

Create registry.yaml with custom component:

# my-data/registry.yaml
apiVersion: aicr.nvidia.com/v1alpha1
kind: ComponentRegistry
components:
  - name: my-operator
    displayName: My Custom Operator
    helm:
      defaultRepository: https://my-charts.example.com
      defaultChart: my-operator
      defaultVersion: v1.0.0

Create values file for the component:

# my-data/components/my-operator/values.yaml
replicaCount: 1
image:
  repository: my-registry/my-operator
  tag: v1.0.0

Create overlay that includes the component:

# my-data/overlays/my-custom-overlay.yaml
kind: RecipeMetadata
apiVersion: aicr.nvidia.com/v1alpha1
metadata:
  name: my-custom-overlay
spec:
  criteria:
    service: eks
    intent: training
  componentRefs:
    - name: my-operator
      type: Helm
      valuesFile: components/my-operator/values.yaml

Generate recipe with external data:

aicr recipe --service eks --intent training --data ./my-data

Debugging External Data

Use --debug flag to see detailed logging about external data loading:

aicr --debug recipe --service eks --data ./my-data

Debug logs include:

External files discovered and registered
File source resolution (embedded vs external)
Registry merge details (components added/overridden)

Example Files

The examples/ directory contains reference files for testing and learning:

Recipes (`examples/recipes/`)

File	Description
`kind.yaml`	Recipe for local Kind cluster with fake GPU
`eks-training.yaml`	EKS recipe optimized for training workloads
`eks-gb200-ubuntu-training-with-validation.yaml`	GB200 on EKS with Ubuntu and multi-phase validation

Usage:

# Generate bundle from example recipe
aicr bundle --recipe examples/recipes/eks-training.yaml --output ./bundles

Templates (`examples/templates/`)

File	Description
`snapshot-template.md.tmpl`	Go template for custom snapshot report formatting

Usage:

# Generate custom cluster report
aicr snapshot --template examples/templates/snapshot-template.md.tmpl --output report.md

FilesExpand file tree

cli-reference.md

Latest commit

History

cli-reference.md

File metadata and controls

CLI Reference

Overview

Global Flags

Logging Modes

Commands

aicr snapshot

aicr recipe

Criteria File Mode (Recommended)

Query Mode

Snapshot Mode

aicr validate

aicr bundle

Bundle Attestation

Deploy Script Behavior (deploy.sh)

Undeploy Script Behavior (undeploy.sh)

aicr verify

aicr trust update

Complete Workflow Examples

File-Based Workflow

ConfigMap-Based Workflow (Kubernetes-Native)

E2E Testing

Shell Completion

Environment Variables

Exit Codes

Common Usage Patterns

Quick Recipe Generation

Save All Steps

JSON Processing

Multiple Environments

Troubleshooting

Snapshot Fails

Recipe Not Found

Bundle Generation Fails

External Data Directory

Overview

Directory Structure

Requirements

Merge Behavior

Usage Examples

Example: Adding a Custom Component

Debugging External Data

Example Files

Recipes (examples/recipes/)

Templates (examples/templates/)

See Also

Deploy Script Behavior (`deploy.sh`)

Undeploy Script Behavior (`undeploy.sh`)

Recipes (`examples/recipes/`)

Templates (`examples/templates/`)