ADS AQUA CLI Reference

The ads aqua CLI provides command-line access to all AI Quick Actions operations. It uses Python Fire under the hood.

Installation

pip install oracle-ads[aqua]
# OR for development
pip install -e ".[aqua]"

Authentication Setup

# For OCI Notebook Sessions (Resource Principal)
# No setup needed - automatic

# For local development with security token
export OCI_IAM_TYPE="security_token"
export OCI_CONFIG_PROFILE=<your-profile>

# For local development with API key
export OCI_IAM_TYPE="api_key"
export OCI_CONFIG_PROFILE=DEFAULT

Model Commands

List Models

ads aqua model list \
  --compartment_id "ocid1.compartment.oc1..xxx"

Get Model Details

ads aqua model get \
  --model_id "ocid1.datasciencemodel.oc1.iad.xxx"

Register Model from HuggingFace

ads aqua model register \
  --model "meta-llama/Llama-3.1-8B-Instruct" \
  --os_path "oci://my-bucket@my-namespace/models/llama-3.1-8b/" \
  --compartment_id "ocid1.compartment.oc1..xxx" \
  --project_id "ocid1.datascienceproject.oc1.iad.xxx" \
  --inference_container "odsc-vllm-serving" \
  --download_from_hf True

Full parameter reference: references/params.md

Register from Object Storage

ads aqua model register \
  --model "my-custom-model" \
  --os_path "oci://my-bucket@my-namespace/models/custom-model/" \
  --inference_container "odsc-vllm-serving"

Register GGUF Model

ads aqua model register \
  --model "TheBloke/Llama-2-7B-Chat-GGUF" \
  --os_path "oci://my-bucket@my-namespace/models/llama2-gguf/" \
  --inference_container "odsc-llama-cpp-serving" \
  --download_from_hf True

Register with BYOC (Bring Your Own Container)

ads aqua model register \
  --model "my-custom-model" \
  --os_path "oci://my-bucket@my-namespace/models/custom/" \
  --inference_container_uri "<region>.ocir.io/<namespace>/<repo>:<tag>"

Convert Legacy Fine-Tuned Model

ads aqua model convert_fine_tune \
  --model_id "ocid1.datasciencemodel.oc1.iad.xxx"

Deployment Commands

Create Single Model Deployment

ads aqua deployment create \
  --model_id "ocid1.datasciencemodel.oc1.iad.xxx" \
  --instance_shape "VM.GPU.A10.2" \
  --display_name "my-deployment" \
  --compartment_id "ocid1.compartment.oc1..xxx" \
  --project_id "ocid1.datascienceproject.oc1.iad.xxx" \
  --log_group_id "ocid1.loggroup.oc1.iad.xxx" \
  --log_id "ocid1.log.oc1.iad.xxx"

Full parameter reference: references/params.md

Create with Custom vLLM Parameters

ads aqua deployment create \
  --model_id "ocid1.datasciencemodel.oc1.iad.xxx" \
  --instance_shape "VM.GPU.A10.2" \
  --display_name "my-deployment" \
  --env_var '{"MODEL_DEPLOY_PREDICT_ENDPOINT": "/v1/chat/completions", "PARAMS": "--max-model-len 8192 --gpu-memory-utilization 0.95"}'

Create Multi-Model Deployment

ads aqua deployment create \
  --models '[
    {"model_id": "ocid1...model1", "model_name": "llama-8b", "gpu_count": 1},
    {"model_id": "ocid1...model2", "model_name": "mistral-7b", "gpu_count": 1}
  ]' \
  --instance_shape "VM.GPU.A10.2" \
  --display_name "multi-model-deployment"

Create Stacked Deployment

ads aqua deployment create \
  --models '[
    {
      "model_id": "ocid1...base_model",
      "model_name": "llama-3.1-8b",
      "fine_tune_weights": [
        {"model_id": "ocid1...ft1", "model_name": "ft-customer-support"},
        {"model_id": "ocid1...ft2", "model_name": "ft-summarization"}
      ]
    }
  ]' \
  --instance_shape "VM.GPU.A10.2" \
  --display_name "stacked-deployment" \
  --deployment_type "STACKED"

Deploy with Tool Calling

ads aqua deployment create \
  --model_id "ocid1.datasciencemodel.oc1.iad.xxx" \
  --instance_shape "VM.GPU.A10.2" \
  --display_name "tool-calling-deployment" \
  --env_var '{"MODEL_DEPLOY_PREDICT_ENDPOINT": "/v1/chat/completions", "PARAMS": "--enable-auto-tool-choice --tool-call-parser llama3_json --max-model-len 4096"}'

Deploy GGUF Model on CPU

ads aqua deployment create \
  --model_id "ocid1.datasciencemodel.oc1.iad.xxx" \
  --instance_shape "VM.Standard.A1.Flex" \
  --display_name "cpu-gguf-deployment" \
  --env_var '{"PARAMS": "--quantization Q4_0"}'

Shape Recommendation

# Table output (human-friendly default)
ads aqua deployment recommend_shape \
  --model_id "meta-llama/Llama-3.3-70B-Instruct"

# By model OCID
ads aqua deployment recommend_shape \
  --model_id "ocid1.datasciencemodel.oc1.iad.xxx"

# JSON output (programmatic use)
ads aqua deployment recommend_shape \
  --model_id "meta-llama/Llama-3.3-70B-Instruct" \
  --generate_table False

Full parameter reference: references/params.md

List Deployments

ads aqua deployment list \
  --compartment_id "ocid1.compartment.oc1..xxx"

Get Deployment Details

ads aqua deployment get \
  --model_deployment_id "ocid1.datasciencemodeldeployment.oc1.iad.xxx"

Fine-Tuning Commands

Create Fine-Tuning Job

ads aqua fine_tuning create \
  --ft_source_id "ocid1.datasciencemodel.oc1.iad.xxx" \
  --ft_name "llama-3.1-8b-custom" \
  --dataset_path "oci://my-bucket@my-namespace/datasets/train.jsonl" \
  --report_path "oci://my-bucket@my-namespace/ft-output/" \
  --shape_name "VM.GPU.A10.2" \
  --replica 1 \
  --compartment_id "ocid1.compartment.oc1..xxx" \
  --project_id "ocid1.datascienceproject.oc1.iad.xxx" \
  --log_group_id "ocid1.loggroup.oc1.iad.xxx" \
  --log_id "ocid1.log.oc1.iad.xxx" \
  --ft_parameters '{"epochs": 3, "learning_rate": 0.00002}'

Full parameter reference: references/params.md

Advanced Hyperparameters

ads aqua fine_tuning create \
  --ft_source_id "ocid1.datasciencemodel.oc1.iad.xxx" \
  --ft_name "llama-custom-advanced" \
  --dataset_path "oci://bucket@ns/train.jsonl" \
  --report_path "oci://bucket@ns/output/" \
  --shape_name "BM.GPU.A10.4" \
  --replica 1 \
  --ft_parameters '{
    "epochs": 5,
    "learning_rate": 1e-5,
    "batch_size": 4,
    "sequence_len": 2048,
    "pad_to_sequence_len": true,
    "sample_packing": "auto",
    "lora_r": 64,
    "lora_alpha": 32,
    "lora_dropout": 0.1,
    "lora_target_linear": true
  }'

Evaluation Commands

Create Evaluation

ads aqua evaluation create \
  --evaluation_source_id "ocid1.datasciencemodeldeployment.oc1.iad.xxx" \
  --evaluation_name "llama-eval-bertscore" \
  --dataset_path "oci://my-bucket@my-namespace/datasets/eval.jsonl" \
  --report_path "oci://my-bucket@my-namespace/eval-reports/" \
  --model_parameters '{"max_tokens": 500, "temperature": 0.7}' \
  --shape_name "VM.Standard.E4.Flex" \
  --block_storage_size 50 \
  --compartment_id "ocid1.compartment.oc1..xxx" \
  --project_id "ocid1.datascienceproject.oc1.iad.xxx" \
  --metrics '[{"name": "bertscore"}, {"name": "rouge"}]'

Full parameter reference: references/params.md

Evaluate Specific Model in Multi/Stacked Deployment

ads aqua evaluation create \
  --evaluation_source_id "ocid1.datasciencemodeldeployment.oc1.iad.xxx" \
  --evaluation_name "stacked-ft1-eval" \
  --dataset_path "oci://bucket@ns/eval.jsonl" \
  --report_path "oci://bucket@ns/eval-reports/" \
  --model_parameters '{"max_tokens": 500, "temperature": 0.7, "model": "ft-customer-support"}' \
  --shape_name "VM.Standard.E4.Flex" \
  --block_storage_size 50 \
  --metrics '[{"name": "bertscore"}]'

List Evaluations

ads aqua evaluation list \
  --compartment_id "ocid1.compartment.oc1..xxx"

Get Evaluation Details

ads aqua evaluation get \
  --eval_id "ocid1.datasciencemodel.oc1.iad.xxx"

Policy Verification

ads aqua verify_policies

Watching Logs

# Model deployment logs
ads opctl watch <model_deployment_ocid> --auth resource_principal

# Job run logs (fine-tuning / evaluation)
ads opctl watch <job_run_ocid> --auth resource_principal

Key Source Files

• ads/aqua/cli.py — AquaCommand entry point (model, deployment, evaluation, fine_tuning)
• ads/aqua/app.py — CLIBuilderMixin for CLI parameter handling