Merge branch 'main' into feature/ad_ODSC-62963

Author: prasankh

ads/aqua/config/evaluation/evaluation_service_config.py

@@ -224,6 +224,7 @@ def search_shapes(
 
     class Config:
         extra = "ignore"
+        protected_namespaces = ()
 
 
 class EvaluationServiceConfig(Serializable):

ads/aqua/extension/model_handler.py

@@ -13,7 +13,6 @@
 from ads.aqua.extension.base_handler import AquaAPIhandler
 from ads.aqua.extension.errors import Errors
 from ads.aqua.model import AquaModelApp
-from ads.aqua.model.constants import ModelTask
 from ads.aqua.model.entities import AquaModelSummary, HFModelSummary
 from ads.aqua.ui import ModelFormat
 
@@ -68,7 +67,7 @@ def read(self, model_id):
         return self.finish(AquaModelApp().get(model_id))
 
     @handle_exceptions
-    def delete(self):
+    def delete(self, id=""):
         """Handles DELETE request for clearing cache"""
         url_parse = urlparse(self.request.path)
         paths = url_parse.path.strip("/")
@@ -177,10 +176,8 @@ def _find_matching_aqua_model(model_id: str) -> Optional[AquaModelSummary]:
 
         return None
 
-
-
     @handle_exceptions
-    def get(self,*args, **kwargs):
+    def get(self, *args, **kwargs):
         """
         Finds a list of matching models from hugging face based on query string provided from users.
 
@@ -194,13 +191,11 @@ def get(self,*args, **kwargs):
             Returns the matching model ids string
         """
 
-        query=self.get_argument("query",default=None)
+        query = self.get_argument("query", default=None)
         if not query:
-            raise HTTPError(400,Errors.MISSING_REQUIRED_PARAMETER.format("query"))
-        models=list_hf_models(query)
-        return self.finish({"models":models})
-
-
+            raise HTTPError(400, Errors.MISSING_REQUIRED_PARAMETER.format("query"))
+        models = list_hf_models(query)
+        return self.finish({"models": models})
 
     @handle_exceptions
     def post(self, *args, **kwargs):
@@ -234,16 +229,17 @@ def post(self, *args, **kwargs):
                 "Please verify the model's status on the Hugging Face Model Hub or select a different model."
             )
 
-        # Check pipeline_tag, it should be `text-generation`
-        if (
-            not hf_model_info.pipeline_tag
-            or hf_model_info.pipeline_tag.lower() != ModelTask.TEXT_GENERATION
-        ):
-            raise AquaRuntimeError(
-                f"Unsupported pipeline tag for the chosen model: '{hf_model_info.pipeline_tag}'. "
-                f"AQUA currently supports the following tasks only: {', '.join(ModelTask.values())}. "
-                "Please select a model with a compatible pipeline tag."
-            )
+        # Commented the validation below to let users to register any model type.
+        # # Check pipeline_tag, it should be `text-generation`
+        # if not (
+        #     hf_model_info.pipeline_tag
+        #     and hf_model_info.pipeline_tag.lower() in ModelTask
+        # ):
+        #     raise AquaRuntimeError(
+        #         f"Unsupported pipeline tag for the chosen model: '{hf_model_info.pipeline_tag}'. "
+        #         f"AQUA currently supports the following tasks only: {', '.join(ModelTask.values())}. "
+        #         "Please select a model with a compatible pipeline tag."
+        #     )
 
         # Check if it is a service/verified model
         aqua_model_info: AquaModelSummary = self._find_matching_aqua_model(

ads/aqua/model/constants.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 # Copyright (c) 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
@@ -9,6 +8,7 @@
 
 This module contains constants/enums used in Aqua Model.
 """
+
 from ads.common.extended_enum import ExtendedEnumMeta
 
 
@@ -21,6 +21,8 @@ class ModelCustomMetadataFields(str, metaclass=ExtendedEnumMeta):
 
 class ModelTask(str, metaclass=ExtendedEnumMeta):
     TEXT_GENERATION = "text-generation"
+    IMAGE_TEXT_TO_TEXT = "image-text-to-text"
+    IMAGE_TO_TEXT = "image-to-text"
 
 
 class FineTuningMetricCategories(str, metaclass=ExtendedEnumMeta):

ads/opctl/operator/lowcode/forecast/MLoperator

@@ -2,12 +2,12 @@ type: forecast
 version: v1
 name: Forecasting Operator
 conda_type: service
-conda: forecast_p38_cpu_v1
+conda: forecast_p310_cpu_x86_64_v4
 gpu: no
 jobs_default_params:
   shape_name: VM.Standard.E4.Flex
-  ocpus: 32
-  memory_in_gbs: 512
+  ocpus: 16
+  memory_in_gbs: 256
   block_storage_size_in_GBs: 512
 keywords:
   - Prophet

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

@@ -49,7 +49,7 @@ def set_kwargs(self):
         time_budget = model_kwargs_cleaned.pop("time_budget", -1)
         model_kwargs_cleaned[
             "preprocessing"
-        ] = self.spec.preprocessing or model_kwargs_cleaned.get("preprocessing", True)
+        ] = self.spec.preprocessing.enabled or model_kwargs_cleaned.get("preprocessing", True)
         return model_kwargs_cleaned, time_budget
 
     def preprocess(self, data, series_id=None):  # TODO: re-use self.le for explanations

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

@@ -68,7 +68,7 @@ def __init__(self, spec, historical_data):
             add_dates.sort()
             if historical_data.get_max_time() > add_dates[-spec.horizon]:
                 raise DataMismatchError(
-                    f"The Historical Data ends on {historical_data.get_max_time()}. The additional data horizon starts on {add_dates[-spec.horizon]}. The horizon should have exactly {spec.horizon} dates after the Hisotrical at a frequency of {historical_data.freq}"
+                    f"The Historical Data ends on {historical_data.get_max_time()}. The additional data horizon starts on {add_dates[-spec.horizon]}. The horizon should have exactly {spec.horizon} dates after the Historical at a frequency of {historical_data.freq}"
                 )
             elif historical_data.get_max_time() != add_dates[-(spec.horizon + 1)]:
                 raise DataMismatchError(

docs/source/index.rst

@@ -20,15 +20,15 @@ Oracle Accelerated Data Science (ADS)
 .. toctree::
    :hidden:
    :maxdepth: 5
-   :caption: Getting Started:
+   :caption: Getting Started
 
    release_notes
    user_guide/quick_start/quick_start
 
 .. toctree::
    :hidden:
    :maxdepth: 5
-   :caption: Installation and Configuration:
+   :caption: Installation and Configuration
 
    user_guide/cli/quickstart
    user_guide/cli/authentication
@@ -38,19 +38,19 @@ Oracle Accelerated Data Science (ADS)
 .. toctree::
    :hidden:
    :maxdepth: 5
-   :caption: Low-Code AI Operators:
+   :caption: Low-Code AI Operators
 
    user_guide/operators/index
-   user_guide/operators/common/index
-   user_guide/operators/forecasting_operator/index
+   user_guide/operators/forecast_operator/index
    user_guide/operators/anomaly_detection_operator/index
    user_guide/operators/pii_operator/index
    user_guide/operators/recommender_operator/index
+   user_guide/operators/common/index
 
 .. toctree::
    :hidden:
    :maxdepth: 5
-   :caption: Tasks:
+   :caption: Tasks
 
    user_guide/loading_data/connect
    user_guide/data_labeling/index
@@ -62,7 +62,7 @@ Oracle Accelerated Data Science (ADS)
 .. toctree::
    :hidden:
    :maxdepth: 5
-   :caption: Integrations:
+   :caption: Integrations
 
    user_guide/apachespark/spark
    user_guide/big_data_service/index
@@ -76,7 +76,7 @@ Oracle Accelerated Data Science (ADS)
 .. toctree::
    :hidden:
    :maxdepth: 5
-   :caption: Classes:
+   :caption: Classes
 
    modules
 

docs/source/release_notes.rst

@@ -146,7 +146,7 @@ Release date: March 20, 2024
 Release date: February 7, 2024
 
 * Releasing v1 of the Anomaly Detection Operator! The Anomaly Detection Operator is a no-code Anomaly or Outlier Detection solution through the OCI Data Science Platform. It uses dozens of models from Oracle’s own proprietary research and the best of open source. See the ``Anomaly Detection`` Section of the ``AI Operators`` tab for full details (:doc:`link <./user_guide/operators/anomaly_detection_operator/index>`).
-* Releasing a new version of the Forecast Operator. This release has faster explainability, improved support for reading from databases, upgrades to the automatic reporting, improved parallelization across all models, and an ability to save models for deferred inference. See the ``Forecast`` Section of the ``AI Operators`` tab for full details (:doc:`link <./user_guide/operators/forecasting_operator/index>`).
+* Releasing a new version of the Forecast Operator. This release has faster explainability, improved support for reading from databases, upgrades to the automatic reporting, improved parallelization across all models, and an ability to save models for deferred inference. See the ``Forecast`` Section of the ``AI Operators`` tab for full details (:doc:`link <./user_guide/operators/forecast_operator/index>`).
 * Change to the default signer such that it now defaults to ``resource_prinicpal`` on any OCI Data Science resource (for example, jobs, notebooks, model deployments, dataflow).
 
 2.10.0

docs/source/user_guide/operators/anomaly_detection_operator/faq.rst

@@ -9,7 +9,3 @@ More details in the documentation here: https://docs.oracle.com/en-us/iaas/tools
 **How do I learn More about AutoTS?**
 
 More details in the documentation here: https://winedarksea.github.io/AutoTS/build/html/source/tutorial.html
-
-**Pip Install Failing with "ERROR: No matching distribution found for oracle-automlx==23.4.1; extra == "forecast""**
-
-Automlx only supports Python<=3.8, != 3.9, <= 3.10.7 . If you are builing in Python 3.9 or 3.10.8+, no automlx distribution will be available. It's recommended to use the conda pack available through Notebook Sessions, or to use Python 3.8

docs/source/user_guide/operators/anomaly_detection_operator/install.rst

@@ -12,7 +12,7 @@ Installing Through PyPi
 
 If you are running the operator from outside of a Notebook Session, you may download ``oracle_ads[anomaly]`` from pypi. 
 
-*Note: Due to our dependence on Automlx, ``oracle_ads[anomaly]``  only supports Python<=3.8, != 3.9, <= 3.10.7 . Python 3.8 is the recommended version.*
+*Note: Python 3.10 is recommended.*
 
 .. code-block:: bash