applying the fix in more places

Author: timmarkhuff

test/conftest.py

@@ -1,10 +1,16 @@
 from datetime import datetime
+from uuid import uuid4
 
 import pytest
 from groundlight import ExperimentalApi, Groundlight
 from model import Detector, ImageQuery, ImageQueryTypeEnum, ResultTypeEnum
 
 
+def generate_test_detector_name(prefix: str = "Test") -> str:
+    """Generates a unique detector name with a timestamp and random suffix to avoid collisions in parallel CI."""
+    return f"{prefix} {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')}_{uuid4().hex[:8]}"
+
+
 def pytest_configure(config):  # pylint: disable=unused-argument
     # Run environment check before tests
     gl = Groundlight()
@@ -25,20 +31,18 @@ def fixture_gl() -> Groundlight:
 @pytest.fixture(name="detector")
 def fixture_detector(gl: Groundlight) -> Detector:
     """Creates a new Test detector."""
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
     query = "Is there a dog?"
     pipeline_config = "never-review"
-    return gl.create_detector(name=name, query=query, pipeline_config=pipeline_config)
+    return gl.create_detector(name=generate_test_detector_name(), query=query, pipeline_config=pipeline_config)
 
 
 @pytest.fixture(name="count_detector")
 def fixture_count_detector(gl_experimental: ExperimentalApi) -> Detector:
     """Creates a new Test detector."""
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
     query = "How many dogs?"
     pipeline_config = "never-review-multi"  # always predicts 0
     return gl_experimental.create_counting_detector(
-        name=name, query=query, class_name="dog", pipeline_config=pipeline_config
+        name=generate_test_detector_name(), query=query, class_name="dog", pipeline_config=pipeline_config
     )
 
 

test/integration/test_groundlight.py

@@ -5,10 +5,10 @@
 import random
 import string
 import time
-from datetime import datetime
 from typing import Any, Dict, Optional, Union
 
 import pytest
+from conftest import generate_test_detector_name
 from groundlight import Groundlight
 from groundlight.binary_labels import VALID_DISPLAY_LABELS, Label, convert_internal_label_to_display
 from groundlight.internalapi import ApiException, InternalApiError, NotFoundError
@@ -97,7 +97,7 @@ def test_create_groundlight_with_retries():
 
 
 def test_create_detector(gl: Groundlight):
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name()
     query = "Is there a dog?"
     _detector = gl.create_detector(name=name, query=query)
     assert str(_detector)
@@ -107,20 +107,20 @@ def test_create_detector(gl: Groundlight):
     ), "We expected the default confidence threshold to be used."
 
     # Test creating dectors with other modes
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
-    count_detector = gl.create_detector(name=name, query=query, mode=ModeEnum.COUNT, class_names="dog")
+    count_detector = gl.create_detector(
+        name=generate_test_detector_name(), query=query, mode=ModeEnum.COUNT, class_names="dog"
+    )
     assert str(count_detector)
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
     multiclass_detector = gl.create_detector(
-        name=name, query=query, mode=ModeEnum.MULTI_CLASS, class_names=["dog", "cat"]
+        name=generate_test_detector_name(), query=query, mode=ModeEnum.MULTI_CLASS, class_names=["dog", "cat"]
     )
     assert str(multiclass_detector)
 
 
 def test_create_detector_with_pipeline_config(gl: Groundlight):
     # "never-review" is a special model that always returns the same result with 100% confidence.
     # It's useful for testing.
-    name = f"Test never-review {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name("Test never-review")
     query = "Is there a dog (always-pass)?"
     pipeline_config = "never-review"
     _detector = gl.create_detector(name=name, query=query, pipeline_config=pipeline_config)
@@ -129,7 +129,7 @@ def test_create_detector_with_pipeline_config(gl: Groundlight):
 
 
 def test_create_detector_with_edge_pipeline_config(gl: Groundlight):
-    name = f"Test edge-pipeline-config {datetime.utcnow()}"
+    name = generate_test_detector_name("Test edge-pipeline-config")
     query = "Is there a dog (edge-config)?"
     _detector = gl.create_detector(
         name=name,
@@ -144,7 +144,7 @@ def test_create_detector_with_edge_pipeline_config(gl: Groundlight):
 def test_create_detector_with_confidence_threshold(gl: Groundlight):
     # "never-review" is a special model that always returns the same result with 100% confidence.
     # It's useful for testing.
-    name = f"Test with confidence {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name("Test with confidence")
     query = "Is there a dog in the image?"
     pipeline_config = "never-review"
     confidence_threshold = 0.825
@@ -198,7 +198,7 @@ def test_create_detector_with_confidence_threshold(gl: Groundlight):
 
 @pytest.mark.skip_for_edge_endpoint(reason="The edge-endpoint does not support passing detector metadata.")
 def test_create_detector_with_everything(gl: Groundlight):
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name()
     query = "Is there a dog?"
     group_name = "Test group"
     confidence_threshold = 0.825
@@ -234,7 +234,7 @@ def test_list_detectors(gl: Groundlight):
 
 def test_get_or_create_detector(gl: Groundlight):
     # With a unique name, we should be creating a new detector.
-    unique_name = f"Unique name {datetime.utcnow()}"
+    unique_name = generate_test_detector_name()
     query = "Is there a dog?"
     detector = gl.get_or_create_detector(name=unique_name, query=query)
     assert str(detector)
@@ -411,7 +411,7 @@ def test_submit_image_query_with_low_request_timeout(gl: Groundlight, detector:
 
 @pytest.mark.skip_for_edge_endpoint(reason="The edge-endpoint does not support passing detector metadata.")
 def test_create_detector_with_metadata(gl: Groundlight):
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name()
     query = "Is there a dog?"
     metadata = generate_random_dict(target_size_bytes=200)
     detector = gl.create_detector(name=name, query=query, metadata=metadata)
@@ -423,7 +423,7 @@ def test_create_detector_with_metadata(gl: Groundlight):
 
 @pytest.mark.skip_for_edge_endpoint(reason="The edge-endpoint does not support passing detector metadata.")
 def test_get_or_create_detector_with_metadata(gl: Groundlight):
-    unique_name = f"Unique name {datetime.utcnow()}"
+    unique_name = generate_test_detector_name()
     query = "Is there a dog?"
     metadata = generate_random_dict(target_size_bytes=200)
     detector = gl.get_or_create_detector(name=unique_name, query=query, metadata=metadata)
@@ -444,7 +444,7 @@ def test_get_or_create_detector_with_metadata(gl: Groundlight):
     ],
 )
 def test_create_detector_with_invalid_metadata(gl: Groundlight, metadata_list: Any):
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name()
     query = "Is there a dog?"
 
     for metadata in metadata_list:
@@ -629,7 +629,7 @@ def test_list_image_queries(gl: Groundlight):
 
 def test_list_image_queries_with_filter(gl: Groundlight):
     # We want a fresh detector so we know exactly what image queries are associated with it
-    detector = gl.create_detector(name=f"Test {datetime.utcnow()}", query="Is there a dog?")
+    detector = gl.create_detector(name=generate_test_detector_name(), query="Is there a dog?")
     image_query_yes = gl.ask_async(detector=detector.id, image="test/assets/dog.jpeg", human_review="NEVER")
     image_query_no = gl.ask_async(detector=detector.id, image="test/assets/cat.jpeg", human_review="NEVER")
     iq_ids = [image_query_yes.id, image_query_no.id]
@@ -859,7 +859,7 @@ def test_binary_detector(gl: Groundlight):
     """
     verify that we can create and submit to a binary detector
     """
-    name = f"Test {datetime.utcnow()}"
+    name = generate_test_detector_name()
     created_detector = gl.create_binary_detector(name, "Is there a dog", confidence_threshold=0.0)
     assert created_detector is not None
     binary_iq = gl.submit_image_query(created_detector, "test/assets/dog.jpeg")
@@ -870,7 +870,7 @@ def test_counting_detector(gl: Groundlight):
     """
     verify that we can create and submit to a counting detector
     """
-    name = f"Test {datetime.utcnow()}"
+    name = generate_test_detector_name()
     created_detector = gl.create_counting_detector(name, "How many dogs", "dog", confidence_threshold=0.0)
     assert created_detector is not None
     count_iq = gl.submit_image_query(created_detector, "test/assets/dog.jpeg")
@@ -881,7 +881,7 @@ def test_counting_detector_async(gl: Groundlight):
     """
     verify that we can create and submit to a counting detector
     """
-    name = f"Test {datetime.utcnow()}"
+    name = generate_test_detector_name()
     created_detector = gl.create_counting_detector(name, "How many dogs", "dog", confidence_threshold=0.0)
     assert created_detector is not None
     async_iq = gl.ask_async(created_detector, "test/assets/dog.jpeg")
@@ -900,7 +900,7 @@ def test_multiclass_detector(gl: Groundlight):
     """
     verify that we can create and submit to a multi-class detector
     """
-    name = f"Test {datetime.utcnow()}"
+    name = generate_test_detector_name()
     class_names = ["Golden Retriever", "Labrador Retriever", "Poodle"]
     created_detector = gl.create_multiclass_detector(
         name, "What kind of dog is this?", class_names=class_names, confidence_threshold=0.0
@@ -916,7 +916,7 @@ def test_delete_detector(gl: Groundlight):
     Test deleting a detector by both ID and object, and verify proper error handling.
     """
     # Create a detector to delete
-    name = f"Test delete detector {datetime.utcnow()}"
+    name = generate_test_detector_name("Test delete detector")
     query = "Is there a dog to delete?"
     pipeline_config = "never-review"
     detector = gl.create_detector(name=name, query=query, pipeline_config=pipeline_config)
@@ -929,7 +929,7 @@ def test_delete_detector(gl: Groundlight):
         gl.get_detector(detector.id)
 
     # Create another detector to test deletion by ID string and that an attached image query is deleted
-    name2 = f"Test delete detector 2 {datetime.utcnow()}"
+    name2 = generate_test_detector_name("Test delete detector 2")
     detector2 = gl.create_detector(name=name2, query=query, pipeline_config=pipeline_config)
     gl.submit_image_query(detector2, "test/assets/dog.jpeg")
 
@@ -959,7 +959,7 @@ def test_create_detector_with_invalid_priming_group_id(gl: Groundlight):
     Note: PrimingGroup IDs are provided by Groundlight representatives. If you would like to
     use a priming_group_id, please reach out to your Groundlight representative.
     """
-    name = f"Test invalid priming {datetime.utcnow()}"
+    name = generate_test_detector_name("Test invalid priming")
     query = "Is there a dog?"
     pipeline_config = "never-review"
     priming_group_id = "prgrp_nonexistent12345678901234567890"

test/integration/test_groundlight_expensive.py

@@ -7,9 +7,9 @@
 # pylint: disable=wildcard-import,unused-wildcard-import,redefined-outer-name,import-outside-toplevel
 import random
 import time
-from datetime import datetime
 
 import pytest
+from conftest import generate_test_detector_name
 from groundlight import Groundlight
 from groundlight.internalapi import iq_is_answered, iq_is_confident
 from groundlight.optional_imports import *
@@ -31,7 +31,7 @@ def fixture_gl() -> Groundlight:
 
 @pytest.mark.skip(reason="This test requires a human labeler who does not need to be in the testing loop")
 def test_human_label(gl: Groundlight):
-    detector = gl.create_detector(name=f"Test {datetime.utcnow()}", query="Is there a dog?")
+    detector = gl.create_detector(name=generate_test_detector_name(), query="Is there a dog?")
     img_query = gl.submit_image_query(
         detector=detector.id, image="test/assets/dog.jpeg", wait=60, human_review="ALWAYS"
     )
@@ -61,7 +61,7 @@ def test_detector_improvement(gl: Groundlight):
 
     random.seed(2741)
 
-    name = f"Test test_detector_improvement {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name("Test test_detector_improvement")
     query = "Is there a dog?"
     detector = gl.create_detector(name=name, query=query)
 
@@ -126,7 +126,7 @@ def test_ask_method_quality(gl: Groundlight, detector: Detector):
     # asks for some level of quality on how fast ask_ml is and that we will get a confident result from ask_confident
     fast_always_yes_iq = gl.ask_ml(detector=detector.id, image="test/assets/dog.jpeg", wait=0)
     assert iq_is_answered(fast_always_yes_iq)
-    name = f"Test {datetime.utcnow()}"  # Need a unique name
+    name = generate_test_detector_name()
     query = "Is there a dog?"
     detector = gl.create_detector(name=name, query=query, confidence_threshold=0.8)
     fast_iq = gl.ask_ml(detector=detector.id, image="test/assets/dog.jpeg", wait=0)

test/unit/test_cli.py

@@ -1,9 +1,10 @@
 import os
 import re
 import subprocess
-from datetime import datetime
 from unittest.mock import patch
 
+from conftest import generate_test_detector_name
+
 
 def test_whoami():
     completed_process = subprocess.run(
@@ -25,7 +26,7 @@ def test_list_detector():
 
 def test_detector_and_image_queries():
     # test creating a detector
-    test_detector_name = f"testdetector {datetime.utcnow()}"
+    test_detector_name = generate_test_detector_name("testdetector")
     completed_process = subprocess.run(
         [
             "groundlight",

test/unit/test_detector_reset.py

@@ -1,15 +1,15 @@
 import time
-from datetime import datetime
 
 import pytest
+from conftest import generate_test_detector_name
 from groundlight import ExperimentalApi
 from groundlight_openapi_client.exceptions import NotFoundException
 
 
 @pytest.mark.skip(reason="This is an expensive test, reset may take some time")
 def test_reset_retry(gl_experimental: ExperimentalApi):
     # Reset the detector, retrying in case the reset is still ongoing
-    det = gl_experimental.create_detector(f"Test {datetime.utcnow()}", "test_query")
+    det = gl_experimental.create_detector(generate_test_detector_name(), "test_query")
     iq = gl_experimental.submit_image_query(det, "test/assets/cat.jpeg")
     gl_experimental.reset_detector(det.id)
     success = False
@@ -30,7 +30,7 @@ def test_reset_retry(gl_experimental: ExperimentalApi):
 def test_reset_training(gl_experimental: ExperimentalApi):
     # If we reset a detector, we should have low confidence after the reset
     low_confidence_threshold = 0.6
-    det = gl_experimental.create_detector(f"Test {datetime.utcnow()}", "is this a cat")
+    det = gl_experimental.create_detector(generate_test_detector_name(), "is this a cat")
     gl_experimental.reset_detector(det.id)
     iq = gl_experimental.submit_image_query(det, "test/assets/cat.jpeg", human_review="NEVER")
     assert iq.result.confidence < low_confidence_threshold

test/unit/test_experimental.py

@@ -1,7 +1,7 @@
 import time
-from datetime import datetime, timezone
 
 import pytest
+from conftest import generate_test_detector_name
 from groundlight import ExperimentalApi
 from model import Detector, ImageQuery
 
@@ -10,7 +10,7 @@ def test_detector_groups(gl_experimental: ExperimentalApi):
     """
     verify that we can create a detector group and retrieve it
     """
-    name = f"Test {datetime.utcnow()}"
+    name = generate_test_detector_name()
     created_group = gl_experimental.create_detector_group(name)
     all_groups = gl_experimental.list_detector_groups()
     assert created_group in all_groups
@@ -34,7 +34,7 @@ def test_update_detector_name(gl_experimental: ExperimentalApi, detector: Detect
     """
     verify that we can update the name of a detector
     """
-    new_name = f"Test {datetime.utcnow()}"
+    new_name = generate_test_detector_name()
     gl_experimental.update_detector_name(detector.id, new_name)
     updated_detector = gl_experimental.get_detector(detector.id)
     assert updated_detector.name == new_name
@@ -44,7 +44,7 @@ def test_update_detector_status(gl_experimental: ExperimentalApi):
     """
     verify that we can update the status of a detector
     """
-    detector = gl_experimental.get_or_create_detector(f"test {datetime.utcnow()}", "Is there a dog?")
+    detector = gl_experimental.get_or_create_detector(generate_test_detector_name(), "Is there a dog?")
     gl_experimental.update_detector_status(detector.id, False)
     updated_detector = gl_experimental.get_detector(detector.id)
     assert updated_detector.status.value == "OFF"
@@ -57,7 +57,7 @@ def test_update_detector_escalation_type(gl_experimental: ExperimentalApi):
     """
     verify that we can update the escalation type of a detector
     """
-    detector = gl_experimental.get_or_create_detector(f"test {datetime.utcnow()}", "Is there a dog?")
+    detector = gl_experimental.get_or_create_detector(generate_test_detector_name(), "Is there a dog?")
     gl_experimental.update_detector_escalation_type(detector.id, "NO_HUMAN_LABELING")
     updated_detector = gl_experimental.get_detector(detector.id)
     updated_detector.escalation_type == "NO_HUMAN_LABELING"
@@ -94,7 +94,7 @@ def test_text_recognition_detector(gl_experimental: ExperimentalApi):
     """
     verify that we can create and submit to a text recognition detector
     """
-    name = f"Test {datetime.utcnow()}"
+    name = generate_test_detector_name()
     created_detector = gl_experimental.create_text_recognition_detector(
         name, "What is the date and time?", confidence_threshold=0.0
     )
@@ -107,7 +107,7 @@ def test_bounding_box_detector(gl_experimental: ExperimentalApi):
     """
     Verify that we can create and submit to a bounding box detector
     """
-    name = f"Test {datetime.now(timezone.utc)}"
+    name = generate_test_detector_name()
     created_detector = gl_experimental.create_bounding_box_detector(
         name, "Draw a bounding box around each dog in the image", "dog", confidence_threshold=0.0
     )
@@ -121,7 +121,7 @@ def test_bounding_box_detector_async(gl_experimental: ExperimentalApi):
     """
     Verify that we can create and submit to a bounding box detector with ask_async
     """
-    name = f"Test {datetime.now(timezone.utc)}"
+    name = generate_test_detector_name()
     created_detector = gl_experimental.create_bounding_box_detector(
         name, "Draw a bounding box around each dog in the image", "dog", confidence_threshold=0.0
     )

test/unit/test_http_retries.py

@@ -1,16 +1,15 @@
-from datetime import datetime
 from typing import Any, Callable
 from unittest import mock
-from uuid import uuid4
 
 import pytest
+from conftest import generate_test_detector_name
 from groundlight import Groundlight
 from groundlight.binary_labels import Label
 from groundlight.internalapi import InternalApiError
 from model import Detector
 
 DEFAULT_CONFIDENCE_THRESHOLD = 0.9
-DETECTOR_NAME = f"test detector_{datetime.utcnow().strftime('%Y=%m-%d %H:%M:%S')}_{uuid4().hex[:8]}"
+DETECTOR_NAME = generate_test_detector_name()
 TOTAL_RETRIES = 3
 STATUS_CODES = range(500, 505)
 IMAGE_FILE = "test/assets/dog.jpeg"