Intel® AI for Enterprise Solutions CLI Reference
All es_auto_installer.sh and model-manager commands for Intel® AI for Enterprise Solutions run from the repo root.
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./es_auto_installer.sh <action> [target] [--env <name>] [options]--env defaults to local when not specified.
Actions
configure
One-time machine setup. Installs Python 3.11+, yq, kubectl, and helm into /usr/local/bin. Skips tools already present. Requires sudo. Run once per machine.
bash
./es_auto_installer.sh configureinit <name>
Create a new environment directory at env/<name>/ and seed it with default configuration files.
bash
./es_auto_installer.sh init local # standard environment
./es_auto_installer.sh init prod --rag # seed with RAG config as wellCreates:
env/<name>/global_config.yaml— edit this before installingenv/<name>/nodes.yaml— node IPs and SSH credentials (edit for multi-node)env/<name>/inventory/hosts.yaml— targets localhost by default; edit for multi-nodeenv/<name>/models.yaml— model catalog, pre-seeded from the inference repo defaults
install
Deploy components. Dependencies are resolved automatically.
bash
# Full stack (infrastructure + platform + inference)
./es_auto_installer.sh install --all --env local
# A single layer
./es_auto_installer.sh install platform --env local
./es_auto_installer.sh install inference --env local
# A single component (with auto-pulled dependencies)
./es_auto_installer.sh install kserve --env local
# A single component, skipping dependencies
./es_auto_installer.sh install metallb --only --env local
# Opt-in application layer (RAG, eRAG UI)
./es_auto_installer.sh install application --env local
# Override a config value at runtime (no file edit needed)
./es_auto_installer.sh install kserve --env local -- -e kserve_version=0.15.0
# Dry run — show what would happen without making changes
./es_auto_installer.sh install --all --env local -- --check
# Pass additional Ansible flags (use -- to separate)
./es_auto_installer.sh install --all --env local -- -vvvTargets for install / teardown:
| Target | What it covers |
|---|---|
--all | infrastructure + platform + inference |
infrastructure | kubernetes, storage |
platform | cert_manager, istio, metallb, envoy_gateway, postgresql, keycloak, object_store, minio, observability |
inference | keycloak_config, envoy_ai_gateway, kserve, litellm, langfuse, llm_services, nri_cpu_balloons |
application | RAG pipeline, UI, vector DBs (opt-in, from ext repo) |
<component> | Any individual component name (e.g. kserve, grafana, metallb) |
teardown
Remove components in reverse dependency order. Configuration files and environment state are preserved.
bash
# Remove everything
./es_auto_installer.sh teardown --all --env local
# Remove a single component
./es_auto_installer.sh teardown keycloak --env local
# Remove the application layer (keeps platform and inference)
./es_auto_installer.sh teardown application --env localvalidate
Run post-install health checks.
bash
./es_auto_installer.sh validate --all --env local
./es_auto_installer.sh validate kserve --env localChecks are implemented per-component as tasks/validate.yaml (asserts, connectivity, replica counts).
status
Print a component status table.
bash
./es_auto_installer.sh status --env localshow
List all available layers and components, including which are opt-in.
bash
./es_auto_installer.sh showFlags
| Flag | Description |
|---|---|
--env <name> | Target environment (default: local) |
--all | Select the full stack (infra + platform + inference) |
--only | Skip dependency auto-inclusion — run the named target alone |
-- <ansible-flags> | Pass remaining args directly to ansible-playbook (e.g. -- -vvv, -- --check, -- -e key=value) |
Environment variables
| Variable | Purpose |
|---|---|
HF_TOKEN | Hugging Face token for gated models (Llama, Mistral, etc.) |
KEYCLOAK_ADMIN_PASSWORD | Set Keycloak admin password before install (auto-generated if unset) |
GRAFANA_ADMIN_PASSWORD | Set Grafana admin password before install (auto-generated if unset) |
KUBECONFIG | Override kubeconfig path (auto-detected from env/<name>/kubeconfig.yaml) |
model-manager
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./model-manager <command> [options] [--env <name>]| Command | Description |
|---|---|
list | List models in the catalog |
deploy <name> | Download weights and start serving (from catalog) |
deploy --id <hf/repo> | Deploy any Hugging Face model ad-hoc |
undeploy <name> | Stop serving (weights stay on PVC) |
undeploy all | Stop all models |
status | Show running models and their endpoints |
Key flags for deploy:
| Flag | Description |
|---|---|
--cpu <n> | CPU cores |
--memory | Memory limit |
--replicas <n> | Number of serving replicas |
--tp <n> | Tensor parallelism width (1, 2, 4, or 8) |
--runtime vllm|openvino | Serving runtime |
--wait | Block until the model is ready |
--dry-run | Print manifest without applying |
--skip-download | Skip weight download (use existing PVC data) |
See Deploy a Model for usage examples.
Logs
All Ansible output is captured per environment:
| File | Contents |
|---|---|
env/<name>/logs/install-all-<timestamp>.log | Full install output |
env/<name>/logs/teardown-all-<timestamp>.log | Full teardown output |
env/<name>/logs/install-<component>-<timestamp>.log | Per-component install output |
env/<name>/logs/ai-solutions-ca.crt | Self-signed CA certificate (import to browser) |
For more Ansible detail, append -- -vvv to any command.
Related Docs
| If you want to… | Go to |
|---|---|
Look up what each global_config.yaml field controls | Configuration Reference |
Deploy, access, and manage LLM inference with model-manager | Deploy an LLM |
| Set up multi-node or bring-your-own-cluster installs | Deployment Guide |
| See the full command dispatch flow this CLI triggers | Architecture & Design Document |