API Reference

Complete reference for using the AICR API Server.

Overview

The AICR API Server provides HTTP REST access to recipe generation and bundle creation for GPU-accelerated infrastructure. Use the API for programmatic access to configuration recommendations and deployment artifacts.

┌──────────────┐      ┌──────────────┐
│ GET /recipe  │─────▶│   Recipe     │
└──────────────┘      └──────────────┘
        │
        ▼
┌──────────────┐      ┌──────────────┐
│ POST /bundle │─────▶│  bundles.zip │
└──────────────┘      └──────────────┘

API vs CLI:

Use the API for remote recipe generation and bundle creation
Use the CLI for local operations, snapshot capture, and ConfigMap integration

Feature	API	CLI
Recipe generation	✅ GET /v1/recipe	✅ `aicr recipe`
Bundle creation	✅ POST /v1/bundle	✅ `aicr bundle`
Snapshot capture	❌ Use CLI	✅ `aicr snapshot`
ConfigMap I/O	❌ Use CLI	✅ `cm://` URIs
Agent deployment	❌ Use CLI	✅ `aicr snapshot`

Base URL

Local development (example):

http://localhost:8080

Start the local server:

docker pull ghcr.io/nvidia/aicrd:latest
docker run -p 8080:8080 ghcr.io/nvidia/aicrd:latest

Quick Start

Get a Recipe

Generate an optimized configuration recipe for your environment:

# GET: Basic recipe for H100 on EKS (query parameters)
curl "http://localhost:8080/v1/recipe?accelerator=h100&service=eks"

# GET: Training workload on Ubuntu
curl "http://localhost:8080/v1/recipe?accelerator=h100&service=eks&intent=training&os=ubuntu"

# POST: Recipe from criteria file (YAML body)
curl -X POST "http://localhost:8080/v1/recipe" \
  -H "Content-Type: application/x-yaml" \
  -d 'kind: RecipeCriteria
apiVersion: aicr.nvidia.com/v1alpha1
metadata:
  name: my-config
spec:
  service: eks
  accelerator: h100
  intent: training'

# Save recipe to file
curl -s "http://localhost:8080/v1/recipe?accelerator=h100&service=eks" -o recipe.json

Generate Bundles

Create deployment bundles from a recipe:

# Pipe recipe directly to bundle endpoint
curl -s "http://localhost:8080/v1/recipe?accelerator=h100&service=eks" | \
  curl -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator" \
    -H "Content-Type: application/json" -d @- -o bundles.zip

# Extract the bundles
unzip bundles.zip -d ./bundles

Endpoints

GET /

Service information and available routes.

curl "http://localhost:8080/"

Response:

{
  "service": "aicrd",
  "version": "v0.7.6",
  "routes": ["/v1/recipe", "/v1/bundle"]
}

GET /v1/recipe

Generate an optimized configuration recipe based on environment parameters.

Query Parameters:

Parameter	Type	Default	Description
`service`	string	any	K8s service: `eks`, `gke`, `aks`, `oke`, `any`
`accelerator`	string	any	GPU type: `h100`, `gb200`, `a100`, `l40`, `any`
`gpu`	string	any	Alias for `accelerator`
`intent`	string	any	Workload: `training`, `inference`, `any`
`os`	string	any	Node OS: `ubuntu`, `rhel`, `cos`, `amazonlinux`, `any`
`platform`	string	any	Platform/framework: `kubeflow`, `any`
`nodes`	integer	0	GPU node count (0 = any)

Examples:

# Minimal request
curl "http://localhost:8080/v1/recipe"

# Specify accelerator
curl "http://localhost:8080/v1/recipe?accelerator=h100"

# Full specification
curl "http://localhost:8080/v1/recipe?service=eks&accelerator=h100&intent=training&os=ubuntu&nodes=8"

# Using gpu alias
curl "http://localhost:8080/v1/recipe?gpu=gb200&service=gke"

# Pretty print with jq
curl -s "http://localhost:8080/v1/recipe?accelerator=h100" | jq '.'

POST /v1/recipe

Generate an optimized configuration recipe from a criteria file body. This endpoint provides an alternative to query parameters, accepting a Kubernetes-style RecipeCriteria resource in the request body.

Content Types:

application/json - JSON format
application/x-yaml - YAML format

Request Body:

The request body must be a RecipeCriteria resource:

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

Examples:

# POST with YAML body
curl -X POST "http://localhost:8080/v1/recipe" \
  -H "Content-Type: application/x-yaml" \
  -d 'kind: RecipeCriteria
apiVersion: aicr.nvidia.com/v1alpha1
metadata:
  name: training-config
spec:
  service: eks
  accelerator: h100
  intent: training'

# POST with JSON body
curl -X POST "http://localhost:8080/v1/recipe" \
  -H "Content-Type: application/json" \
  -d '{
    "kind": "RecipeCriteria",
    "apiVersion": "aicr.nvidia.com/v1alpha1",
    "metadata": {"name": "training-config"},
    "spec": {
      "service": "eks",
      "accelerator": "h100",
      "intent": "training"
    }
  }'

# POST with criteria file
curl -X POST "http://localhost:8080/v1/recipe" \
  -H "Content-Type: application/yaml" \
  -d @criteria.yaml

# Pretty print response
curl -s -X POST "http://localhost:8080/v1/recipe" \
  -H "Content-Type: application/json" \
  -d '{"kind":"RecipeCriteria","apiVersion":"aicr.nvidia.com/v1alpha1","spec":{"service":"eks","accelerator":"h100"}}' \
  | jq '.'

Response:

Same as GET /v1/recipe - returns a recipe JSON response.

Error Responses:

400 Bad Request - Invalid criteria format, missing required fields, or invalid enum values
405 Method Not Allowed - Only GET and POST are supported

Response:

{
  "apiVersion": "aicr.nvidia.com/v1alpha1",
  "kind": "Recipe",
  "metadata": {
    "version": "v1.0.0",
    "created": "2026-01-11T10:30:00Z",
    "appliedOverlays": [
      "base",
      "eks",
      "eks-training",
      "gb200-eks-training"
    ]
  },
  "criteria": {
    "service": "eks",
    "accelerator": "gb200",
    "intent": "training",
    "os": "any",
    "platform": "any"
  },
  "componentRefs": [
    {
      "name": "gpu-operator",
      "version": "v25.3.3",
      "order": 1,
      "repository": "https://helm.ngc.nvidia.com/nvidia"
    },
    {
      "name": "network-operator",
      "version": "v25.4.0",
      "order": 2,
      "repository": "https://helm.ngc.nvidia.com/nvidia"
    }
  ],
  "constraints": {
    "driver": {
      "version": "580.82.07",
      "cudaVersion": "13.1"
    }
  }
}

POST /v1/bundle

Generate deployment bundles from a recipe.

Query Parameters:

Parameter	Type	Default	Description
`bundlers`	string	(all)	Comma-delimited list of bundler types to execute
`set`	string[]		Value overrides (format: `bundler:path.to.field=value`). Repeat for multiple.
`system-node-selector`	string[]		Node selectors for system components (format: `key=value`). Repeat for multiple.
`system-node-toleration`	string[]		Tolerations for system components (format: `key=value:effect`). Repeat for multiple.
`accelerated-node-selector`	string[]		Node selectors for GPU nodes (format: `key=value`). Repeat for multiple.
`accelerated-node-toleration`	string[]		Tolerations for GPU nodes (format: `key=value:effect`). Repeat for multiple.
`nodes`	int	0	Estimated number of GPU nodes (0 = unset). Written to Helm value paths declared in the registry under `nodeScheduling.nodeCountPaths`.
`deployer`	string	helm	Deployment method: `helm` or `argocd`

Request Body:

The request body is the recipe (RecipeResult) directly. No wrapper object needed.

Supported Bundlers:

Bundler names correspond to component names in recipes/registry.yaml. Any component registered there can be passed as a bundler. Current components:

Bundler	Description
`gpu-operator`	NVIDIA GPU Operator — driver and runtime lifecycle
`network-operator`	NVIDIA Network Operator — RDMA, SR-IOV, host networking
`aws-efa`	AWS Elastic Fabric Adapter device plugin (EKS)
`cert-manager`	TLS certificate management
`skyhook-operator`	OS-level node tuning and kernel configuration
`skyhook-customizations`	Environment-specific node tuning profiles
`nvsentinel`	GPU health monitoring and automated remediation
`nvidia-dra-driver-gpu`	Dynamic Resource Allocation driver for GPUs
`kube-prometheus-stack`	Prometheus, Grafana, Alertmanager monitoring stack
`prometheus-adapter`	Custom metrics for HPA scaling
`aws-ebs-csi-driver`	Amazon EBS CSI driver (EKS)
`k8s-ephemeral-storage-metrics`	Ephemeral storage usage metrics
`kai-scheduler`	DRA-aware gang scheduler with topology-aware placement
`dynamo-crds`	NVIDIA Dynamo inference serving CRDs
`dynamo-platform`	NVIDIA Dynamo inference serving platform
`kgateway-crds`	Kubernetes Gateway API CRDs
`kgateway`	Kubernetes Gateway API implementation
`kubeflow-trainer`	Kubeflow Training Operator for distributed training

Examples:

# Basic: pipe recipe to bundle (GPU Operator only)
curl -s "http://localhost:8080/v1/recipe?accelerator=h100&service=eks" | \
  curl -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator" \
    -H "Content-Type: application/json" -d @- -o bundles.zip

# Advanced: with value overrides and ArgoCD deployer
curl -s "http://localhost:8080/v1/recipe?accelerator=h100&service=eks" | \
  curl -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator&deployer=argocd&repo=https://github.com/my-org/my-gitops-repo.git&set=gpuoperator:gds.enabled=true" \
    -H "Content-Type: application/json" -d @- -o bundles.zip

# With node scheduling for system and GPU nodes
curl -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator&system-node-selector=nodeGroup=system&system-node-toleration=dedicated=system:NoSchedule&accelerated-node-selector=nvidia.com/gpu.present=true&accelerated-node-toleration=nvidia.com/gpu=present:NoSchedule" \
  -H "Content-Type: application/json" \
  -d @recipe.json \
  -o bundles.zip

# Generate GPU Operator bundle from saved recipe
curl -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator" \
  -H "Content-Type: application/json" \
  -d @recipe.json \
  -o bundles.zip

# Generate all available bundles (no bundlers param)
curl -X POST "http://localhost:8080/v1/bundle" \
  -H "Content-Type: application/json" \
  -d '{
    "apiVersion": "aicr.nvidia.com/v1alpha1",
    "kind": "Recipe",
    "componentRefs": [
      {"name": "gpu-operator", "version": "v25.3.3", "type": "helm"},
      {"name": "network-operator", "version": "v25.4.0", "type": "helm"}
    ]
  }' \
  -o bundles.zip

# Generate multiple specific bundles
curl -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator,network-operator" \
  -H "Content-Type: application/json" \
  -d '{
    "apiVersion": "aicr.nvidia.com/v1alpha1",
    "kind": "Recipe",
    "componentRefs": [
      {"name": "gpu-operator", "version": "v25.3.3", "type": "helm"},
      {"name": "network-operator", "version": "v25.4.0", "type": "helm"}
    ]
  }' \
  -o bundles.zip

Response Headers:

Header	Description	Example
`Content-Type`	Always `application/zip`	`application/zip`
`Content-Disposition`	Download filename	`attachment; filename="bundles.zip"`
`X-Bundle-Files`	Total files in archive	`10`
`X-Bundle-Size`	Uncompressed size (bytes)	`45678`
`X-Bundle-Duration`	Generation time	`1.234s`

Bundle Structure:

bundles.zip
├── gpu-operator/
│   ├── values.yaml              # Helm chart values
│   ├── manifests/
│   │   ├── clusterpolicy.yaml   # ClusterPolicy CR
│   │   └── dcgm-exporter.yaml   # DCGM Exporter config
│   ├── scripts/
│   │   ├── install.sh           # Installation script
│   │   └── uninstall.sh         # Cleanup script
│   ├── README.md                # Deployment instructions
│   └── checksums.txt            # SHA256 checksums
└── network-operator/
    └── ...

GET /health

Service health check (liveness probe).

curl "http://localhost:8080/health"

Response:

{
  "status": "healthy",
  "timestamp": "2026-01-11T10:30:00Z"
}

GET /ready

Service readiness check (readiness probe).

curl "http://localhost:8080/ready"

Response:

{
  "status": "ready",
  "timestamp": "2026-01-11T10:30:00Z"
}

GET /metrics

Prometheus metrics endpoint.

curl "http://localhost:8080/metrics"

Key Metrics:

Metric	Type	Description
`aicr_http_requests_total`	counter	Total HTTP requests by method, path, status
`aicr_http_request_duration_seconds`	histogram	Request latency distribution
`aicr_http_requests_in_flight`	gauge	Current concurrent requests
`aicr_rate_limit_rejects_total`	counter	Rate limit rejections

Complete Workflow Example

Fetch a recipe and generate bundles in one workflow:

#!/bin/bash

# Step 1: Get recipe for H100 on EKS for training
echo "Fetching recipe..."
curl -s "http://localhost:8080/v1/recipe?accelerator=h100&service=eks&intent=training" \
  -o recipe.json

# Display recipe summary
echo "Recipe components:"
jq -r '.componentRefs[] | "  - \(.name): \(.version)"' recipe.json

# Step 2: Generate bundles from recipe (pipe directly)
echo "Generating bundles..."
curl -s -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator" \
  -H "Content-Type: application/json" \
  -d @recipe.json \
  -o bundles.zip

# Alternative: one-liner without intermediate file
# curl -s "http://localhost:8080/v1/recipe?accelerator=h100&service=eks" | \
#   curl -X POST "http://localhost:8080/v1/bundle?bundlers=gpu-operator" \
#     -H "Content-Type: application/json" -d @- -o bundles.zip

# Step 3: Extract and verify
echo "Extracting bundles..."
unzip -q bundles.zip -d ./deployment

# Verify checksums
echo "Verifying checksums..."
cd deployment/gpu-operator
sha256sum -c checksums.txt

# Step 4: Deploy (example)
echo "Bundle ready for deployment:"
ls -la

Error Handling

Error Response Format:

{
  "code": "ERROR_CODE",
  "message": "Human-readable error description",
  "details": { ... },
  "requestId": "550e8400-e29b-41d4-a716-446655440000",
  "timestamp": "2026-01-11T10:30:00Z",
  "retryable": true
}

Error Codes:

Code	HTTP Status	Description	Retryable
`INVALID_REQUEST`	400	Invalid query parameters, request body, or disallowed criteria value	No
`METHOD_NOT_ALLOWED`	405	Wrong HTTP method	No
`NO_MATCHING_RULE`	404	No configuration found	No
`RATE_LIMIT_EXCEEDED`	429	Too many requests	Yes
`INTERNAL_ERROR`	500	Server error	Yes

Handling Rate Limits:

# Check rate limit headers
curl -I "http://localhost:8080/v1/recipe?accelerator=h100"

# Response headers:
# X-RateLimit-Limit: 100
# X-RateLimit-Remaining: 95
# X-RateLimit-Reset: 1736589000

When rate limited (HTTP 429), use the Retry-After header:

# Retry with backoff
response=$(curl -s -w "%{http_code}" "http://localhost:8080/v1/recipe?accelerator=h100")
if [ "${response: -3}" = "429" ]; then
  retry_after=$(curl -sI "http://localhost:8080/v1/recipe" | grep -i "Retry-After" | awk '{print $2}')
  echo "Rate limited. Retrying after ${retry_after}s..."
  sleep "$retry_after"
fi

Rate Limiting

Limit: 100 requests per second per IP
Burst: 200 requests
Headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset
429 Response: Includes Retry-After header

Criteria Allowlists

The API server can be configured to restrict which criteria values are allowed. This enables operators to limit the API to specific accelerators, services, intents, or OS types.

Configuration

Allowlists are configured via environment variables when starting the server:

Environment Variable	Description	Example
`AICR_ALLOWED_ACCELERATORS`	Comma-separated list of allowed GPU types	`h100,l40`
`AICR_ALLOWED_SERVICES`	Comma-separated list of allowed K8s services	`eks,gke`
`AICR_ALLOWED_INTENTS`	Comma-separated list of allowed workload intents	`training`
`AICR_ALLOWED_OS`	Comma-separated list of allowed OS types	`ubuntu,rhel`

Behavior:

If an environment variable is not set, all values for that criteria are allowed
If an environment variable is set, only the specified values are permitted
The any value is always allowed regardless of allowlist configuration
Allowlists apply to both /v1/recipe and /v1/bundle endpoints

Example Configuration

# Start server allowing only H100 and L40 GPUs on EKS
docker run -p 8080:8080 \
  -e AICR_ALLOWED_ACCELERATORS=h100,l40 \
  -e AICR_ALLOWED_SERVICES=eks \
  ghcr.io/nvidia/aicrd:latest

Error Response

When a disallowed criteria value is requested:

curl "http://localhost:8080/v1/recipe?accelerator=gb200&service=eks"

Response (HTTP 400):

{
  "code": "INVALID_REQUEST",
  "message": "accelerator type not allowed",
  "details": {
    "requested": "gb200",
    "allowed": ["h100", "l40"]
  },
  "requestId": "550e8400-e29b-41d4-a716-446655440000",
  "timestamp": "2026-01-27T10:30:00Z",
  "retryable": false
}

CLI Behavior

The CLI (aicr) is not affected by allowlists. Allowlists only apply to the API server, allowing operators to restrict API access while maintaining full CLI functionality for administrative tasks.

Programming Language Examples

Python

import requests
import zipfile
import io

BASE_URL = "http://localhost:8080"

# Get recipe
params = {
    "accelerator": "h100",
    "service": "eks",
    "intent": "training",
    "os": "ubuntu"
}

resp = requests.get(f"{BASE_URL}/v1/recipe", params=params)
resp.raise_for_status()
recipe = resp.json()

print(f"Recipe has {len(recipe['componentRefs'])} components")

# Generate bundles — recipe is the request body, bundlers are query params
resp = requests.post(
    f"{BASE_URL}/v1/bundle",
    params={"bundlers": "gpu-operator"},
    json=recipe,
)
resp.raise_for_status()

# Extract zip
with zipfile.ZipFile(io.BytesIO(resp.content)) as zf:
    zf.extractall("./deployment")
    print(f"Extracted {len(zf.namelist())} files")

Go

package main

import (
    "encoding/json"
    "fmt"
    "io"
    "net/http"
    "net/url"
    "os"
)

func main() {
    baseURL := "http://localhost:8080"

    // Get recipe
    params := url.Values{}
    params.Add("accelerator", "h100")
    params.Add("service", "eks")
    
    resp, err := http.Get(baseURL + "/v1/recipe?" + params.Encode())
    if err != nil {
        panic(err)
    }
    defer resp.Body.Close()

    var recipe map[string]interface{}
    json.NewDecoder(resp.Body).Decode(&recipe)
    
    fmt.Printf("Got recipe with %d components\n", 
        len(recipe["componentRefs"].([]interface{})))
}

JavaScript/Node.js

const BASE_URL = "http://localhost:8080";

async function main() {
    // Get recipe
    const params = new URLSearchParams({
        accelerator: "h100",
        service: "eks",
        intent: "training"
    });
    
    const recipeResp = await fetch(`${BASE_URL}/v1/recipe?${params}`);
    const recipe = await recipeResp.json();
    
    console.log(`Recipe has ${recipe.componentRefs.length} components`);
    
    // Generate bundles — recipe is the request body, bundlers are query params
    const bundleResp = await fetch(`${BASE_URL}/v1/bundle?bundlers=gpu-operator`, {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify(recipe),
    });
    
    // Save zip
    const buffer = await bundleResp.arrayBuffer();
    require("fs").writeFileSync("bundles.zip", Buffer.from(buffer));
    console.log("Bundles saved to bundles.zip");
}

main();

Shell Script (Batch Processing)

#!/bin/bash
# Generate recipes for multiple environments

environments=(
  "os=ubuntu&accelerator=h100&service=eks"
  "os=ubuntu&accelerator=gb200&service=gke"
  "os=rhel&accelerator=a100&service=aks"
)

for env in "${environments[@]}"; do
  echo "Fetching recipe for: $env"

  curl -s "http://localhost:8080/v1/recipe?${env}" \
    | jq -r '.componentRefs[] | "\(.name): \(.version)"'

  echo ""
done

OpenAPI Specification

The full OpenAPI 3.1 specification is available at: api/aicr/v1/server.yaml

Generate client SDKs:

# Download spec
curl https://raw.githubusercontent.com/NVIDIA/aicr/main/api/aicr/v1/server.yaml \
  -o openapi.yaml

# Generate Python client
openapi-generator-cli generate -i openapi.yaml -g python -o ./python-client

# Generate Go client
openapi-generator-cli generate -i openapi.yaml -g go -o ./go-client

# Generate TypeScript client
openapi-generator-cli generate -i openapi.yaml -g typescript-fetch -o ./ts-client

Troubleshooting

Common Issues

"Invalid accelerator type" error:

# Use valid values: h100, gb200, a100, l40, any
curl "http://localhost:8080/v1/recipe?accelerator=h100"

"Recipe is required" error:

# Ensure recipe is in request body
curl -X POST "http://localhost:8080/v1/bundle" \
  -H "Content-Type: application/json" \
  -d '{"recipe": {...}}'  # recipe must not be null

Empty zip file:

# Check recipe has componentRefs
curl -s "http://localhost:8080/v1/recipe?accelerator=h100" | jq '.componentRefs'

Connection refused (local):

# Start local server first
make server

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

API Reference

Overview

Base URL

Quick Start

Get a Recipe

Generate Bundles

Endpoints

GET /

GET /v1/recipe

POST /v1/recipe

POST /v1/bundle

GET /health

GET /ready

GET /metrics

Complete Workflow Example

Error Handling

Rate Limiting

Criteria Allowlists

Configuration

Example Configuration

Error Response

CLI Behavior

Programming Language Examples

Python

Go

JavaScript/Node.js

Shell Script (Batch Processing)

OpenAPI Specification

Troubleshooting

Common Issues

See Also

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File metadata and controls

API Reference

Overview

Base URL

Quick Start

Get a Recipe

Generate Bundles

Endpoints

GET /

GET /v1/recipe

POST /v1/recipe

POST /v1/bundle

GET /health

GET /ready

GET /metrics

Complete Workflow Example

Error Handling

Rate Limiting

Criteria Allowlists

Configuration

Example Configuration

Error Response

CLI Behavior

Programming Language Examples

Python

Go

JavaScript/Node.js

Shell Script (Batch Processing)

OpenAPI Specification

Troubleshooting

Common Issues

See Also