M 6107719447 check finetuner status

aixplain/enums/asset_status.py

@@ -0,0 +1,43 @@
+__author__ = "thiagocastroferreira"
+
+"""
+Copyright 2024 The aiXplain SDK authors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Author: Duraikrishna Selvaraju, Thiago Castro Ferreira, Shreyas Sharma and Lucas Pavanelli
+Date: February 21st 2024
+Description:
+    Asset Enum
+"""
+
+from enum import Enum
+from typing import Text
+
+class AssetStatus(Text, Enum):
+    HIDDEN = 'hidden'
+    SCHEDULED = 'scheduled'
+    ONBOARDING = 'onboarding'
+    ONBOARDED = 'onboarded'
+    PENDING = 'pending'
+    FAILED = 'failed'
+    TRAINING = 'training'
+    REJECTED = 'rejected'
+    ENABLING = 'enabling'
+    DELETING = 'deleting'
+    DISABLED = 'disabled'
+    DELETED = 'deleted'
+    IN_PROGRESS = 'in_progress'
+    COMPLETED = 'completed'
+    CANCELING = 'canceling'
+    CANCELED = 'canceled'

aixplain/modules/__init__.py

@@ -29,5 +29,6 @@
 from .model import Model
 from .pipeline import Pipeline
 from .finetune import Finetune, FinetuneCost
+from .finetune.status import FinetuneStatus
 from .benchmark import Benchmark
 from .benchmark_job import BenchmarkJob

aixplain/modules/finetune/status.py

@@ -0,0 +1,36 @@
+__author__ = "thiagocastroferreira"
+
+"""
+Copyright 2024 The aiXplain SDK authors
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Author: Duraikrishna Selvaraju, Thiago Castro Ferreira, Shreyas Sharma and Lucas Pavanelli
+Date: February 21st 2024
+Description:
+    FinetuneCost Class
+"""
+
+from aixplain.enums.asset_status import AssetStatus
+from dataclasses import dataclass
+from dataclasses_json import dataclass_json
+from typing import Optional, Text
+
+@dataclass_json
+@dataclass
+class FinetuneStatus(object):
+    status: "AssetStatus"
+    model_status: "AssetStatus"
+    epoch: Optional[float] = None
+    training_loss: Optional[float] = None
+    validation_loss: Optional[float] = None

aixplain/modules/metric.py

@@ -23,6 +23,7 @@
 
 from typing import Optional, Text, List, Union
 from aixplain.modules.asset import Asset
+
 from aixplain.utils.file_utils import _request_with_retry
 # from aixplain.factories.model_factory import ModelFactory
 
@@ -92,6 +93,7 @@ def run(self, hypothesis: Optional[Union[str, List[str]]]=None, source: Optional
             source (Optional[Union[str, List[str]]], optional): Can give a single source or a list of sources for metric calculation. Defaults to None.
             reference (Optional[Union[str, List[str]]], optional): Can give a single reference or a list of references for metric calculation. Defaults to None.
         """
+        from aixplain.factories.model_factory import ModelFactory
         model = ModelFactory.get(self.id)
         payload = {
             "function": self.function,

aixplain/modules/model.py

@@ -20,7 +20,6 @@
 Description:
     Model Class
 """
-
 import time
 import json
 import logging
@@ -251,23 +250,65 @@ def run_async(self, data: Union[Text, Dict], name: Text = "model_process", param
                 response["error"] = msg
         return response
 
-    def check_finetune_status(self):
+    def check_finetune_status(self, after_epoch: Optional[int] = None):
         """Check the status of the FineTune model.
 
+        Args:
+            after_epoch (Optional[int], optional): status after a given epoch. Defaults to None.
+
         Raises:
             Exception: If the 'TEAM_API_KEY' is not provided.
 
         Returns:
-            str: The status of the FineTune model.
+            FinetuneStatus: The status of the FineTune model.
         """
+        from aixplain.enums.asset_status import AssetStatus
+        from aixplain.modules.finetune.status import FinetuneStatus
         headers = {"x-api-key": self.api_key, "Content-Type": "application/json"}
+        resp = None
         try:
-            url = urljoin(self.backend_url, f"sdk/models/{self.id}")
+            url = urljoin(self.backend_url, f"sdk/finetune/{self.id}/ml-logs")
             logging.info(f"Start service for GET Check FineTune status Model  - {url} - {headers}")
             r = _request_with_retry("get", url, headers=headers)
             resp = r.json()
-            status = resp["status"]
-            logging.info(f"Response for GET Check FineTune status Model - Id {self.id} / Status {status}.")
+            finetune_status = AssetStatus(resp["finetuneStatus"])
+            model_status = AssetStatus(resp["modelStatus"])
+            logs = sorted(resp["logs"], key=lambda x: float(x["epoch"]))
+
+            target_epoch = None
+            if after_epoch is not None:
+                logs = [log for log in logs if float(log["epoch"]) > after_epoch]
+                if len(logs) > 0:
+                    target_epoch = float(logs[0]["epoch"])
+            elif len(logs) > 0:
+                target_epoch = float(logs[-1]["epoch"])
+
+            if target_epoch is not None:
+                log = None
+                for log_ in logs:
+                    if int(log_["epoch"]) == target_epoch:
+                        if log is None:
+                            log = log_
+                        else:
+                            if log_["trainLoss"] is not None:
+                                log["trainLoss"] = log_["trainLoss"]
+                            if log_["evalLoss"] is not None:
+                                log["evalLoss"] = log_["evalLoss"]
+
+                status = FinetuneStatus(
+                    status=finetune_status,
+                    model_status=model_status,
+                    epoch=float(log["epoch"]) if "epoch" in log and log["epoch"] is not None else None,
+                    training_loss=float(log["trainLoss"]) if "trainLoss" in log and log["trainLoss"] is not None else None,
+                    validation_loss=float(log["evalLoss"]) if "evalLoss" in log and log["evalLoss"] is not None else None,
+                )
+            else:
+                status = FinetuneStatus(
+                    status=finetune_status,
+                    model_status=model_status,
+                )
+
+            logging.info(f"Response for GET Check FineTune status Model - Id {self.id} / Status {status.status.value}.")
             return status
         except Exception as e:
             message = ""

tests/unit/finetune_test.py

@@ -29,6 +29,7 @@
 from aixplain.modules import Model, Finetune
 from aixplain.modules.finetune import Hyperparameters
 from aixplain.enums import Function
+from urllib.parse import urljoin
 
 import pytest
 
@@ -37,6 +38,8 @@
 COST_ESTIMATION_FILE = "tests/unit/mock_responses/cost_estimation_response.json"
 FINETUNE_URL = f"{config.BACKEND_URL}/sdk/finetune"
 FINETUNE_FILE = "tests/unit/mock_responses/finetune_response.json"
+FINETUNE_STATUS_FILE = "tests/unit/mock_responses/finetune_status_response.json"
+FINETUNE_STATUS_FILE_2 = "tests/unit/mock_responses/finetune_status_response_2.json"
 PERCENTAGE_EXCEPTION_FILE = "tests/unit/data/create_finetune_percentage_exception.json"
 MODEL_FILE = "tests/unit/mock_responses/model_response.json"
 MODEL_URL = f"{config.BACKEND_URL}/sdk/models"
@@ -106,16 +109,27 @@ def test_start():
     assert fine_tuned_model is not None
     assert fine_tuned_model.id == model_map["id"]
 
-
-def test_check_finetuner_status():
-    model_map = read_data(MODEL_FILE)
+@pytest.mark.parametrize(
+    "input_path,after_epoch,training_loss,validation_loss", 
+    [
+        (FINETUNE_STATUS_FILE, None, 0.4, 0.0217), 
+        (FINETUNE_STATUS_FILE, 1, 0.2, 0.0482),
+        (FINETUNE_STATUS_FILE_2, None, 2.657801408034, 2.596168756485), 
+        (FINETUNE_STATUS_FILE_2, 0, None, 2.684150457382)
+    ]
+)
+def test_check_finetuner_status(input_path, after_epoch, training_loss, validation_loss):
+    model_map = read_data(input_path)
     asset_id = "test_id"
     with requests_mock.Mocker() as mock:
         test_model = Model(asset_id, "")
-        url = f"{MODEL_URL}/{asset_id}"
+        url = urljoin(config.BACKEND_URL, f"sdk/finetune/{asset_id}/ml-logs")
         mock.get(url, headers=FIXED_HEADER, json=model_map)
-        status = test_model.check_finetune_status()
-    assert status == model_map["status"]
+        status = test_model.check_finetune_status(after_epoch=after_epoch)
+    assert status.status.value == model_map["finetuneStatus"]
+    assert status.model_status.value == model_map["modelStatus"]
+    assert status.training_loss == training_loss
+    assert status.validation_loss == validation_loss
 
 
 @pytest.mark.parametrize("is_finetunable", [True, False])
@@ -132,4 +146,4 @@ def test_list_finetunable_models(is_finetunable):
     model_list = result["results"]
     assert len(model_list) > 0
     for model_index in range(len(model_list)):
-        assert model_list[model_index].id == list_map["items"][model_index]["id"]
+        assert model_list[model_index].id == list_map["items"][model_index]["id"]

tests/unit/mock_responses/finetune_status_response.json

@@ -0,0 +1,41 @@
+{
+    "finetuneStatus": "onboarding",
+    "modelStatus": "onboarded",
+    "logs": [
+        {
+            "epoch": 1,
+            "learningRate": 9.938725490196079e-05,
+            "trainLoss": 0.1,
+            "evalLoss": 0.1106,
+            "step": 10
+        },
+        {
+            "epoch": 2,
+            "learningRate": 9.877450980392157e-05,
+            "trainLoss": 0.2,
+            "evalLoss": 0.0482,
+            "step": 20
+        },
+        {
+            "epoch": 3,
+            "learningRate": 9.816176470588235e-05,
+            "trainLoss": 0.3,
+            "evalLoss": 0.0251,
+            "step": 30
+        },
+        {
+            "epoch": 4,
+            "learningRate": 9.754901960784314e-05,
+            "trainLoss": 0.9,
+            "evalLoss": 0.0228,
+            "step": 40
+        },
+        {
+            "epoch": 5,
+            "learningRate": 9.693627450980392e-05,
+            "trainLoss": 0.4,
+            "evalLoss": 0.0217,
+            "step": 50
+        }
+    ]
+}

tests/unit/mock_responses/finetune_status_response_2.json

@@ -0,0 +1,49 @@
+{
+    "id": "65fb26268fe9153a6c9c29c4",
+    "finetuneStatus": "in_progress",
+    "modelStatus": "training",
+    "logs": [
+        {
+            "epoch": 1,
+            "learningRate": null,
+            "trainLoss": null,
+            "validationLoss": null,
+            "step": null,
+            "evalLoss": 2.684150457382,
+            "totalFlos": null,
+            "evalRuntime": 12.4129,
+            "trainRuntime": null,
+            "evalStepsPerSecond": 0.322,
+            "trainStepsPerSecond": null,
+            "evalSamplesPerSecond": 16.112
+        },
+        {
+            "epoch": 2,
+            "learningRate": null,
+            "trainLoss": null,
+            "validationLoss": null,
+            "step": null,
+            "evalLoss": 2.596168756485,
+            "totalFlos": null,
+            "evalRuntime": 11.8249,
+            "trainRuntime": null,
+            "evalStepsPerSecond": 0.338,
+            "trainStepsPerSecond": null,
+            "evalSamplesPerSecond": 16.913
+        },
+        {
+            "epoch": 2,
+            "learningRate": null,
+            "trainLoss": 2.657801408034,
+            "validationLoss": null,
+            "step": null,
+            "evalLoss": null,
+            "totalFlos": 11893948284928,
+            "evalRuntime": null,
+            "trainRuntime": 221.7946,
+            "evalStepsPerSecond": null,
+            "trainStepsPerSecond": 0.117,
+            "evalSamplesPerSecond": null
+        }
+    ]
+}