Eng 467 ai xplain sdk update finetune functional tests to cover all new finetunable models

aixplain/factories/model_factory.py

@@ -31,6 +31,7 @@
 from urllib.parse import urljoin
 from warnings import warn
 from aixplain.enums.function import FunctionInputOutput
+from datetime import datetime
 
 
 class ModelFactory:
@@ -67,6 +68,9 @@ def _create_model_from_response(cls, response: Dict) -> Model:
         if function == Function.TEXT_GENERATION:
             ModelClass = LLM
 
+        created_at = None
+        if "createdAt" in response and response["createdAt"]:
+            created_at = datetime.fromisoformat(response["createdAt"].replace("Z", "+00:00"))
         function_id = response["function"]["id"]
         function = Function(function_id)
         function_io = FunctionInputOutput.get(function_id, None)
@@ -80,6 +84,7 @@ def _create_model_from_response(cls, response: Dict) -> Model:
             api_key=response["api_key"],
             cost=response["pricing"],
             function=function,
+            created_at=created_at,
             parameters=parameters,
             input_params=input_params,
             output_params=output_params,

aixplain/modules/model/__init__.py

@@ -30,6 +30,7 @@
 from urllib.parse import urljoin
 from aixplain.utils.file_utils import _request_with_retry
 from typing import Union, Optional, Text, Dict
+from datetime import datetime
 
 
 class Model(Asset):
@@ -63,6 +64,7 @@ def __init__(
         function: Optional[Function] = None,
         is_subscribed: bool = False,
         cost: Optional[Dict] = None,
+        created_at: Optional[datetime] = None,
         input_params: Optional[Dict] = None,
         output_params: Optional[Dict] = None,
         **additional_info,
@@ -88,8 +90,9 @@ def __init__(
         self.backend_url = config.BACKEND_URL
         self.function = function
         self.is_subscribed = is_subscribed
-        self.input_params = input_params 
-        self.output_params = output_params 
+        self.created_at = created_at
+        self.input_params = input_params
+        self.output_params = output_params
 
     def to_dict(self) -> Dict:
         """Get the model info as a Dictionary
@@ -98,7 +101,14 @@ def to_dict(self) -> Dict:
             Dict: Model Information
         """
         clean_additional_info = {k: v for k, v in self.additional_info.items() if v is not None}
-        return {"id": self.id, "name": self.name, "supplier": self.supplier, "additional_info": clean_additional_info, "input_params": self.input_params,"output_params": self.output_params,}
+        return {
+            "id": self.id,
+            "name": self.name,
+            "supplier": self.supplier,
+            "additional_info": clean_additional_info,
+            "input_params": self.input_params,
+            "output_params": self.output_params,
+        }
 
     def __repr__(self):
         try:
@@ -263,7 +273,9 @@ def run_async(self, data: Union[Text, Dict], name: Text = "model_process", param
                     error = "Validation-related error: Please ensure all required fields are provided and correctly formatted."
                 else:
                     status_code = str(r.status_code)
-                    error = f"Status {status_code}: Unspecified error: An unspecified error occurred while processing your request."
+                    error = (
+                        f"Status {status_code}: Unspecified error: An unspecified error occurred while processing your request."
+                    )
                 response = {"status": "FAILED", "error_message": error}
                 logging.error(f"Error in request for {name} - {r.status_code}: {error}")
         except Exception:

tests/functional/finetune/finetune_functional_test.py

@@ -1,5 +1,4 @@
 __author__ = "lucaspavanelli"
-
 """
 Copyright 2022 The aiXplain SDK authors
 
@@ -26,6 +25,7 @@
 from aixplain.factories import FinetuneFactory
 from aixplain.modules.finetune.cost import FinetuneCost
 from aixplain.enums import Function, Language
+from datetime import datetime, timedelta, timezone
 
 import pytest
 
@@ -40,11 +40,6 @@ def read_data(data_path):
     return json.load(open(data_path, "r"))
 
 
-@pytest.fixture(scope="module", params=read_data(RUN_FILE))
-def run_input_map(request):
-    return request.param
-
-
 @pytest.fixture(scope="module", params=read_data(ESTIMATE_COST_FILE))
 def estimate_cost_input_map(request):
     return request.param
@@ -60,11 +55,32 @@ def validate_prompt_input_map(request):
     return request.param
 
 
-def test_end2end(run_input_map):
-    model = ModelFactory.get(run_input_map["model_id"])
-    dataset_list = [DatasetFactory.list(query=run_input_map["dataset_name"])["results"][0]]
+def pytest_generate_tests(metafunc):
+    if "input_map" in metafunc.fixturenames:
+        four_weeks_ago = datetime.now(timezone.utc) - timedelta(weeks=4)
+        models = ModelFactory.list(function=Function.TEXT_GENERATION, is_finetunable=True)["results"]
+
+        recent_models = [
+            {
+                "model_name": model.name,
+                "model_id": model.id,
+                "dataset_name": "Test text generation dataset",
+                "inference_data": "Hello!",
+                "required_dev": True,
+                "search_metadata": False,
+            }
+            for model in models
+            if model.created_at is not None and model.created_at >= four_weeks_ago
+        ]
+        recent_models += read_data(RUN_FILE)
+        metafunc.parametrize("input_map", recent_models)
+
+
+def test_end2end(input_map):
+    model = input_map["model_id"]
+    dataset_list = [DatasetFactory.list(query=input_map["dataset_name"])["results"][0]]
     train_percentage, dev_percentage = 100, 0
-    if run_input_map["required_dev"]:
+    if input_map["required_dev"]:
         train_percentage, dev_percentage = 80, 20
     finetune = FinetuneFactory.create(
         str(uuid.uuid4()), dataset_list, model, train_percentage=train_percentage, dev_percentage=dev_percentage
@@ -85,12 +101,12 @@ def test_end2end(run_input_map):
     assert finetune_model.check_finetune_status().model_status.value == "onboarded"
     time.sleep(30)
     print(f"Model dict: {finetune_model.__dict__}")
-    result = finetune_model.run(run_input_map["inference_data"])
+    result = finetune_model.run(input_map["inference_data"])
     print(f"Result: {result}")
     assert result is not None
-    if run_input_map["search_metadata"]:
+    if input_map["search_metadata"]:
         assert "details" in result
-        assert len(result["details"]) > 0  
+        assert len(result["details"]) > 0
         assert "metadata" in result["details"][0]
         assert len(result["details"][0]["metadata"]) > 0
     finetune_model.delete()