feature: http endpoint support

.github/workflows/osml-model-runner-test-build.yml

@@ -1,4 +1,4 @@
-name: "OSML Models Build Workflow"
+name: "OSML Model Runner Test Build Workflow"
 
 on:
   pull_request:

bin/process_image.py

@@ -40,16 +40,17 @@
 
 # set up a cli tool for the script using argparse
 parser = argparse.ArgumentParser("process_image")
-parser.add_argument("--image", help="The target image URL to process with OSML Model Runner.", type=str, default="small")
-parser.add_argument("--model", help="The target model to use for object detection.", type=str, default="centerpoint")
+parser.add_argument("--image", help="Target image URL to process with OSML Model Runner.", type=str, default="small")
+parser.add_argument("--model", help="Target model to use for object detection.", type=str, default="centerpoint")
 parser.add_argument("--skip_integ", help="Whether or not to compare image with known results.", action="store_true")
-parser.add_argument("--tile_format", help="The target tile format to use for tiling.", type=str)
-parser.add_argument("--tile_compression", help="The compression used for the target image.", type=str)
-parser.add_argument("--tile_size", help="The tile size to split the image into for model processing.", type=str)
-parser.add_argument("--tile_overlap", help="The tile overlap to consider when processing regions.", type=str)
-parser.add_argument("--feature_selection_options", help="The feature selection options JSON string.", type=str)
-parser.add_argument("--region", help="The AWS region OSML is deployed to.", type=str, default=default_region)
-parser.add_argument("--account", help="The AWS account OSML is deployed to.", type=str, default=default_account)
+parser.add_argument("--tile_format", help="Target tile format to use for tiling.", type=str)
+parser.add_argument("--tile_compression", help="Compression used for the target image.", type=str)
+parser.add_argument("--tile_size", help="Tile size to split the image into for model processing.", type=str)
+parser.add_argument("--tile_overlap", help="Tile overlap to consider when processing regions.", type=str)
+parser.add_argument("--feature_selection_options", help="Feature selection options JSON string.", type=str)
+parser.add_argument("--region", help="AWS region OSML is deployed to.", type=str, default=default_region)
+parser.add_argument("--account", help="AWS account OSML is deployed to.", type=str, default=default_account)
+parser.add_argument("--endpoint_type", help="Type of model endpoint to test, sm or http.", type=str, default="sm")
 args = parser.parse_args()
 
 # standard test images deployed by CDK
@@ -67,7 +68,7 @@
     "sicd_interferometric_hh_ntf": f"s3://{image_bucket}/sicd-interferometric-hh.nitf",
 }
 
-# call into root directory of this package so that we can run this script from anywhere.
+# call into the root directory of this package so that we can run this script from anywhere.
 os.chdir(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
 
 # set the python path to include the project source
@@ -95,6 +96,6 @@
     test = "src/aws/osml/process_image/test_process_image.py"
 else:
     # run integration test against known results
-    test = f"src/aws/osml/integ/{args.model}/test_{args.model}_model.py"
+    test = f"src/aws/osml/integ/{args.endpoint_type}_{args.model}/test_{args.endpoint_type}_{args.model}_model.py"
 
 subprocess.run(["python3", "-m", "pytest", "-o", "log_cli=true", "-vv", test])

environment-py311.yml

@@ -3,5 +3,5 @@ channels:
   - conda-forge
 dependencies:
   - conda-forge::python=3.11
-  - conda-forge::gdal=3.7.0
-  - conda-forge::proj=9.2.1
+  - conda-forge::gdal=3.7.2
+  - conda-forge::proj=9.3.0

environment.yml

@@ -1,6 +1,6 @@
-name: osml_model_runner_test
+name: osml_models
 channels:
   - conda-forge
 dependencies:
-  - conda-forge::gdal=3.7.0
-  - conda-forge::proj=9.2.1
+  - conda-forge::gdal=3.7.2
+  - conda-forge::proj=9.3.0

setup.cfg

@@ -44,6 +44,7 @@ install_requires =
     geojson==3.0.1
     pytest==7.3.1
     setuptools==68.0.0
+    toml==0.10.2
 
 
 [options.packages.find]

src/aws/osml/integ/http_centerpoint/test_http_centerpoint_model.py

@@ -0,0 +1,69 @@
+#  Copyright 2023 Amazon.com, Inc. or its affiliates.
+
+import logging
+
+from aws.osml.utils import (
+    OSMLConfig,
+    count_features,
+    count_region_request_items,
+    ddb_client,
+    elb_client,
+    kinesis_client,
+    run_model_on_image,
+    s3_client,
+    sqs_client,
+    validate_expected_region_request_items,
+    validate_features_match,
+)
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+
+def test_model_runner_centerpoint_http_model() -> None:
+    """
+    Run the test using the CenterPointModel and validate the number of features
+    and region requests using the HTTP endpoint
+
+    :return: None
+    """
+
+    if OSMLConfig.HTTP_CENTERPOINT_MODEL_URL:
+        http_endpoint_url = OSMLConfig.HTTP_CENTERPOINT_MODEL_URL
+    else:
+        http_endpoint_dns = get_load_balancer_dns_url(OSMLConfig.HTTP_CENTERPOINT_MODEL_ELB_NAME)
+        http_endpoint_url = f"http://{http_endpoint_dns}{OSMLConfig.HTTP_CENTERPOINT_MODEL_INFERENCE_PATH}"
+
+    # launch our image request and validate it completes
+    image_id, job_id, image_processing_request, shard_iter = run_model_on_image(
+        sqs_client(), http_endpoint_url, "HTTP_ENDPOINT", kinesis_client()
+    )
+
+    # count the created features in the table for this image
+    count_features(image_id=image_id, ddb_client=ddb_client())
+
+    # verify the results we created in the appropriate syncs
+    validate_features_match(
+        image_processing_request=image_processing_request,
+        job_id=job_id,
+        shard_iter=shard_iter,
+        s3_client=s3_client(),
+        kinesis_client=kinesis_client(),
+    )
+
+    # validate the number of region requests that were created in the process and check if they are succeeded
+    region_request_count = count_region_request_items(image_id=image_id, ddb_client=ddb_client())
+    validate_expected_region_request_items(region_request_count)
+
+
+def get_load_balancer_dns_url(load_balancer_name: str) -> str:
+    """
+    Get the DNS URL for the given load balancer
+    :param load_balancer_name: The name of the load balancer
+    :return: The DNS URL for the load balancer
+    """
+    logger.debug("Retrieving DNS name for '{}'...".format(load_balancer_name))
+    res = elb_client().describe_load_balancers(Names=[load_balancer_name])
+    dns_name = res.get("LoadBalancers", [])[0].get("DNSName")
+    logger.debug("Found DNS name: {}".format(dns_name))
+    return dns_name

src/aws/osml/integ/aircraft/test_aircraft_model.py → src/aws/osml/integ/sm_aircraft/test_sm_aircraft_model.py

@@ -25,7 +25,7 @@ def test_model_runner_aircraft_model() -> None:
 
     # Launch our image request and validate it completes
     image_id, job_id, image_processing_request, kinesis_shard = run_model_on_image(
-        sqs_client(), OSMLConfig.SM_AIRCRAFT_MODEL, kinesis_client()
+        sqs_client(), OSMLConfig.SM_AIRCRAFT_MODEL, "SM_ENDPOINT", kinesis_client()
     )
 
     # Count the features that were create in the table for this image

src/aws/osml/integ/centerpoint/test_centerpoint_model.py → src/aws/osml/integ/sm_centerpoint/test_sm_centerpoint_model.py

@@ -29,7 +29,7 @@ def test_model_runner_center_point_model() -> None:
 
     # launch our image request and validate it completes
     image_id, job_id, image_processing_request, shard_iter = run_model_on_image(
-        sqs_client(), OSMLConfig.SM_CENTERPOINT_MODEL, kinesis_client()
+        sqs_client(), OSMLConfig.SM_CENTERPOINT_MODEL, "SM_ENDPOINT", kinesis_client()
     )
 
     # count the features that were create in the table for this image

src/aws/osml/integ/flood/test_flood_model.py → src/aws/osml/integ/sm_flood/test_sm_flood_model.py

@@ -28,7 +28,7 @@ def test_model_runner_flood_model() -> None:
 
     # Launch our image request and validate it completes
     image_id, job_id, image_processing_request, kinesis_shard = run_model_on_image(
-        sqs_client(), OSMLConfig.SM_FLOOD_MODEL, kinesis_client()
+        sqs_client(), OSMLConfig.SM_FLOOD_MODEL, "SM_ENDPOINT", kinesis_client()
     )
 
     # Count the features that were create in the table for this image

src/aws/osml/utils/__init__.py

@@ -4,7 +4,7 @@
 # __init__.py file.
 # flake8: noqa
 
-from .clients import cw_client, ddb_client, kinesis_client, s3_client, sm_client, sqs_client
+from .clients import cw_client, ddb_client, elb_client, kinesis_client, s3_client, sm_client, sqs_client
 from .integ_utils import (
     build_image_processing_request,
     count_features,

src/aws/osml/utils/clients.py

@@ -75,3 +75,13 @@ def cw_client() -> boto3.client:
     """
     session = get_session_credentials()
     return session.client("cloudwatch", region_name=OSMLConfig.REGION)
+
+
+def elb_client() -> boto3.client:
+    """
+    Get resources from the default ElasticLoadBalancing session
+
+    :return: boto3.client = ELB client
+    """
+    session = get_session_credentials()
+    return session.client("elbv2", region_name=OSMLConfig.REGION)

src/aws/osml/utils/osml_config.py

@@ -26,6 +26,11 @@ class OSMLConfig:
     SM_FLOOD_MODEL: str = os.getenv("SM_FLOOD_MODEL", "flood")
     SM_AIRCRAFT_MODEL: str = os.getenv("SM_AIRCRAFT_MODEL", "aircraft")
 
+    # HTTP model config
+    HTTP_CENTERPOINT_MODEL_URL: str = os.getenv("HTTP_CENTER_POINT_MODEL_URL", None)
+    HTTP_CENTERPOINT_MODEL_ELB_NAME: str = os.getenv("HTTP_CENTER_POINT_MODEL_ELB_NAME", "test-http-model-endpoint")
+    HTTP_CENTERPOINT_MODEL_INFERENCE_PATH = os.getenv("HTTP_CENTERPOINT_MODEL_INFERENCE_PATH", "/invocations")
+
     # bucket name prefixes
     S3_RESULTS_BUCKET: str = os.getenv("S3_RESULTS_BUCKET")
     S3_RESULTS_BUCKET_PREFIX: str = os.getenv("S3_RESULTS_BUCKET_PREFIX", "test-results")
@@ -62,5 +67,5 @@ class OSMLLoadTestConfig:
     S3_LOAD_TEST_RESULT_BUCKET: str = os.getenv("S3_LOAD_TEST_RESULT_BUCKET")
 
     # processing workflow
-    PERIODIC_SLEEP_SECS: str = os.getenv("PERIODIC_SLEEP_SECS", "60")  # in seconds
-    PROCESSING_WINDOW_MIN: str = os.getenv("PROCESSING_WINDOW_MIN", "1")  # in hours
+    PERIODIC_SLEEP_SECS: str = os.getenv("PERIODIC_SLEEP_SECS", "60")
+    PROCESSING_WINDOW_MIN: str = os.getenv("PROCESSING_WINDOW_MIN", "1")