Merge branch 'main' into feature/forecasting/meta_models

Author: ahosler

ads/aqua/common/enums.py

@@ -58,6 +58,7 @@ class InferenceContainerTypeFamily(ExtendedEnum):
  AQUA_VLLM_LLAMA4_CONTAINER_FAMILY = "odsc-vllm-serving-llama4"
  AQUA_TGI_CONTAINER_FAMILY = "odsc-tgi-serving"
  AQUA_LLAMA_CPP_CONTAINER_FAMILY = "odsc-llama-cpp-serving"
+ AQUA_VLLM_OPENAI_CONTAINER_FAMILY = "odsc-vllm-serving-openai"
 
 
 class CustomInferenceContainerTypeFamily(ExtendedEnum):

ads/aqua/common/utils.py

@@ -997,6 +997,44 @@ def get_container_params_type(container_type_name: str) -> str:
  return UNKNOWN
 
 
+def get_container_env_type(container_type_name: Optional[str]) -> str:
+ """
+ Determine the container environment type based on the container type name.
+
+ This function matches the provided container type name against the known
+ values of `InferenceContainerType`. The check is case-insensitive and
+ allows for partial matches so that changes in container naming conventions
+ (e.g., prefixes or suffixes) will still be matched correctly.
+
+ Examples:
+ >>> get_container_env_type("odsc-vllm-serving")
+ 'vllm'
+ >>> get_container_env_type("ODSC-TGI-Serving")
+ 'tgi'
+ >>> get_container_env_type("custom-unknown-container")
+ 'UNKNOWN'
+
+ Args:
+ container_type_name (Optional[str]):
+ The deployment container type name (e.g., "odsc-vllm-serving").
+
+ Returns:
+ str:
+ - A matching `InferenceContainerType` value string (e.g., "VLLM", "TGI", "LLAMA-CPP").
+ - `"UNKNOWN"` if no match is found or the input is empty/None.
+ """
+ if not container_type_name:
+ return UNKNOWN
+
+ needle = container_type_name.strip().casefold()
+
+ for container_type in InferenceContainerType.values():
+ if container_type and container_type.casefold() in needle:
+ return container_type.upper()
+
+ return UNKNOWN
+
+
 def get_restricted_params_by_container(container_type_name: str) -> set:
  """The utility function accepts the deployment container type name and returns a set of restricted params
  for that container.

ads/aqua/modeldeployment/config_loader.py

@@ -88,6 +88,7 @@ class MultiModelConfig(Serializable):
  gpu_count (int, optional): Number of GPUs count to this model of this shape.
  parameters (Dict[str, str], optional): A dictionary of parameters (e.g., VLLM_PARAMS) to
  configure the behavior of a particular GPU shape.
+ env (Dict[str, Dict[str, str]]): Environment variables grouped by namespace (e.g., "VLLM": {"VAR": "VAL"}).
  """
 
  gpu_count: Optional[int] = Field(
@@ -97,6 +98,10 @@ class MultiModelConfig(Serializable):
  default_factory=dict,
  description="Key-value pairs for GPU shape parameters (e.g., VLLM_PARAMS).",
  )
+ env: Optional[Dict[str, Dict[str, str]]] = Field(
+ default_factory=dict,
+ description="Environment variables grouped by namespace",
+ )
 
  class Config:
  extra = "allow"
@@ -130,6 +135,7 @@ class ConfigurationItem(Serializable):
  configure the behavior of a particular GPU shape.
  multi_model_deployment (List[MultiModelConfig], optional): A list of multi model configuration details.
  shape_info (DeploymentShapeInfo, optional): The shape information to this model for specific CPU shape.
+ env (Dict[str, Dict[str, str]]): Environment variables grouped by namespace (e.g., "VLLM": {"VAR": "VAL"}).
  """
 
  parameters: Optional[Dict[str, str]] = Field(
@@ -143,6 +149,10 @@ class ConfigurationItem(Serializable):
  default_factory=DeploymentShapeInfo,
  description="The shape information to this model for specific shape",
  )
+ env: Optional[Dict[str, Dict[str, str]]] = Field(
+ default_factory=dict,
+ description="Environment variables grouped by namespace",
+ )
 
  class Config:
  extra = "allow"

ads/aqua/modeldeployment/deployment.py

@@ -27,6 +27,7 @@
  build_pydantic_error_message,
  find_restricted_params,
  get_combined_params,
+ get_container_env_type,
  get_container_params_type,
  get_ocid_substring,
  get_params_list,
@@ -199,7 +200,7 @@ def create(
  if create_deployment_details.instance_shape.lower() not in available_shapes:
  raise AquaValueError(
  f"Invalid Instance Shape. The selected shape '{create_deployment_details.instance_shape}' "
- f"is not available in the {self.region} region. Please choose another shape to deploy the model."
+ f"is not supported in the {self.region} region. Please choose another shape to deploy the model."
  )
 
  # Get container config
@@ -381,6 +382,7 @@ def _create(
  Tags.AQUA_SERVICE_MODEL_TAG,
  Tags.AQUA_FINE_TUNED_MODEL_TAG,
  Tags.AQUA_TAG,
+ Tags.BASE_MODEL_CUSTOM,
  ]:
  if tag in aqua_model.freeform_tags:
  tags[tag] = aqua_model.freeform_tags[tag]
@@ -1042,6 +1044,7 @@ def get_deployment_config(self, model_id: str) -> AquaDeploymentConfig:
  config = self.get_config_from_metadata(
  model_id, AquaModelMetadataKeys.DEPLOYMENT_CONFIGURATION
  ).config
+
  if config:
  logger.info(
  f"Fetched {AquaModelMetadataKeys.DEPLOYMENT_CONFIGURATION} from defined metadata for model: {model_id}."
@@ -1126,7 +1129,7 @@ def get_deployment_default_params(
  model_id: str,
  instance_shape: str,
  gpu_count: int = None,
- ) -> List[str]:
+ ) -> Dict:
  """Gets the default params set in the deployment configs for the given model and instance shape.
 
  Parameters
@@ -1148,6 +1151,7 @@ def get_deployment_default_params(
 
  """
  default_params = []
+ default_envs = {}
  config_params = {}
  model = DataScienceModel.from_id(model_id)
  try:
@@ -1157,19 +1161,15 @@ def get_deployment_default_params(
  except ValueError:
  container_type_key = UNKNOWN
  logger.debug(
- f"{AQUA_DEPLOYMENT_CONTAINER_METADATA_NAME} key is not available in the custom metadata field for model {model_id}."
+ f"{AQUA_DEPLOYMENT_CONTAINER_METADATA_NAME} key is not available in the "
+ f"custom metadata field for model {model_id}."
  )
 
- if (
- container_type_key
- and container_type_key in InferenceContainerTypeFamily.values()
- ):
+ if container_type_key:
  deployment_config = self.get_deployment_config(model_id)
-
  instance_shape_config = deployment_config.configuration.get(
  instance_shape, ConfigurationItem()
  )
-
  if instance_shape_config.multi_model_deployment and gpu_count:
  gpu_params = instance_shape_config.multi_model_deployment
 
@@ -1178,12 +1178,18 @@ def get_deployment_default_params(
  config_params = gpu_config.parameters.get(
  get_container_params_type(container_type_key), UNKNOWN
  )
+ default_envs = instance_shape_config.env.get(
+ get_container_env_type(container_type_key), {}
+ )
  break
 
  else:
  config_params = instance_shape_config.parameters.get(
  get_container_params_type(container_type_key), UNKNOWN
  )
+ default_envs = instance_shape_config.env.get(
+ get_container_env_type(container_type_key), {}
+ )
 
  if config_params:
  params_list = get_params_list(config_params)
@@ -1196,7 +1202,7 @@ def get_deployment_default_params(
  if params.split()[0] not in restricted_params_set:
  default_params.append(params)
 
- return default_params
+ return {"data": default_params, "env": default_envs}
 
  def validate_deployment_params(
  self,

ads/aqua/modeldeployment/entities.py

@@ -233,6 +233,7 @@ class CreateModelDeploymentDetails(BaseModel):
  None, description="The description of the deployment."
  )
  model_id: Optional[str] = Field(None, description="The model OCID to deploy.")
+
  models: Optional[List[AquaMultiModelRef]] = Field(
  None, description="List of models for multimodel deployment."
  )

ads/opctl/operator/lowcode/forecast/__init__.py

@@ -3,3 +3,6 @@
 
 # Copyright (c) 2023 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
+
+import numpy as np
+np.random.seed(42)

ads/opctl/operator/lowcode/forecast/model/automlx.py

@@ -158,7 +158,7 @@ def _build_model(self) -> pd.DataFrame:
  summary_frame = model.forecast(
  X=X_pred,
  periods=horizon,
- alpha=1 - (self.spec.confidence_interval_width / 100),
+ alpha=1 - self.spec.confidence_interval_width,
  )
 
  fitted_values = model.predict(data_i.drop(target, axis=1))[

docs/source/release_notes.rst

@@ -2,6 +2,12 @@
 Release Notes
 =============
 
+2.13.17
+-------
+Release date: Aug 10, 2025
+
+* AI Quick Actions enhancements.
+
 2.13.16
 -------
 Release date: Jul 16, 2025

pyproject.toml

@@ -21,7 +21,7 @@ build-backend = "flit_core.buildapi"
 
 # Required
 name = "oracle_ads" # the install (PyPI) name; name for local build in [tool.flit.module] section below
-version = "2.13.16"
+version = "2.13.17"
 
 # Optional
 description = "Oracle Accelerated Data Science SDK"

tests/unitary/with_extras/aqua/test_data/deployment/aqua_multi_model_deployment_config.json

@@ -1,20 +1,24 @@
 {
  "configuration": {
  "BM.GPU.A100-v2.8": {
+ "env": {},
  "multi_model_deployment": [
  {
+ "env": {},
  "gpu_count": 1,
  "parameters": {
  "VLLM_PARAMS": "--trust-remote-code --max-model-len 32000"
  }
  },
  {
+ "env": {},
  "gpu_count": 2,
  "parameters": {
  "VLLM_PARAMS": "--trust-remote-code --max-model-len 32000"
  }
  },
  {
+ "env": {},
  "gpu_count": 8,
  "parameters": {
  "VLLM_PARAMS": "--trust-remote-code --max-model-len 32000"
@@ -26,6 +30,7 @@
  }
  },
  "BM.GPU.H100.8": {
+ "env": {},
  "multi_model_deployment": [
  {
  "gpu_count": 1
@@ -44,6 +49,7 @@
  "VM.GPU.A10.2": {
  "multi_model_deployment": [
  {
+ "env": {},
  "gpu_count": 2,
  "parameters": {
  "VLLM_PARAMS": "--trust-remote-code --max-model-len 32000"
@@ -52,8 +58,10 @@
  ]
  },
  "VM.GPU.A10.4": {
+ "env": {},
  "multi_model_deployment": [
  {
+ "env": {},
  "gpu_count": 2,
  "parameters": {
  "VLLM_PARAMS": "--trust-remote-code --max-model-len 32000"