From aaae2e60f196cbe0ee84f5d9076c4449a4ba0c28 Mon Sep 17 00:00:00 2001
From: Private Island Networks Inc <opensource@privateisland.tech>
Date: Mon, 24 Aug 2026 20:05:22 -0400
Subject: initial commit to match up with
 https://privateisland.tech/dev/pi-w-inference-server

---
 infer/http_infer_mlp.py | 97 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 infer/http_infer_mlp.py

(limited to 'infer/http_infer_mlp.py')

diff --git a/infer/http_infer_mlp.py b/infer/http_infer_mlp.py
new file mode 100644
index 0000000..f7fa227
--- /dev/null
+++ b/infer/http_infer_mlp.py
@@ -0,0 +1,97 @@
+#
+# http_infer_mlp
+#
+import sys
+import numpy as np
+import datetime
+import argparse
+import tritonclient.http as httpclient
+from sklearn.datasets import make_moons
+from sklearn.preprocessing import StandardScaler

+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Sends requests via KServe REST API using images in numpy format. '
+                                                 'It displays performance statistics and optionally the model accuracy')
+    parser.add_argument('--http_address', required=False, default='localhost', help='Specify url to http service. default:localhost')
+    parser.add_argument('--http_port', required=False, default=8000, help='Specify port to http service. default: 8000')
+    parser.add_argument('--input_name', required=False, default='input', help='Specify input tensor name. default: input')
+    parser.add_argument('--output_name', required=False, default='resnet_v1_50/predictions/Reshape_1',
+                        help='Specify output name. default: resnet_v1_50/predictions/Reshape_1')
+    parser.add_argument('--iterations', default=1000,
+                        help='Number of requests iterations',
+                        dest='iterations', type=int)
+    parser.add_argument('--model_name', default='resnet', help='Define model name, must be same as is in service. default: resnet',
+                        dest='model_name')
+    parser.add_argument('--pipeline_name', default='', help='Define pipeline name, must be same as is in service',
+                        dest='pipeline_name')
+    parser.add_argument('--binary_data', default=False, action='store_true', help='Send input data in binary format', dest='binary_data')
+    parser.add_argument('--tls', default=False, action='store_true', help='use TLS communication with gRPC endpoint')
+    parser.add_argument('--server_cert', required=False, help='Path to server certificate', default=None)
+    parser.add_argument('--client_cert', required=False, help='Path to client certificate', default=None)
+    parser.add_argument('--client_key', required=False, help='Path to client key', default=None)
+
+    args = vars(parser.parse_args())
+    iterations = args.get('iterations')
+    
+    # Create the data
+    X, y = make_moons(n_samples=iterations, noise=0.2, random_state=42)
+    scaler = StandardScaler()
+    X = scaler.fit_transform(X)
+
+    address = "{}:{}".format(args['http_address'], args['http_port'])
+    batch_size = 1
+
+    if args['tls']:
+        ssl_options = {
+            'keyfile':args['client_key'],
+            'cert_file':args['client_cert'],
+            'ca_certs':args['server_cert']
+        }
+    else:
+        ssl_options = None
+
+    triton_client = httpclient.InferenceServerClient(
+                url=address,
+                ssl=args['tls'],
+                ssl_options=ssl_options,
+                verbose=False)
+
+    processing_times = np.zeros((0), int)
+
+    print('Start processing:')
+    print('\tModel name: {}'.format(args.get('pipeline_name') if bool(args.get('pipeline_name')) else args.get('model_name')))
+    print('\tIterations: {}'.format(iterations))
+
+    iteration = 0
+    is_pipeline_request = bool(args.get('pipeline_name'))
+    y_test = []
+    num_correct = 0
+
+    while iteration < iterations:
+        inputs = []
+        inputs.append(httpclient.InferInput(args['input_name'], [1,2], "FP32"))
+        point = np.array([X[iteration]],dtype=np.float32)
+        inputs[0].set_data_from_numpy(point)
+        start_time = datetime.datetime.now()
+        results = triton_client.infer(
+            model_name=args.get('pipeline_name') if is_pipeline_request else args.get('model_name'),
+            inputs=inputs)
+        end_time = datetime.datetime.now()
+        duration = (end_time - start_time).total_seconds() * 1000
+        processing_times = np.append(processing_times, np.array([int(duration)]))
+        output = results.as_numpy(args['output_name'])
+        y_test.append(float(output[0][0]))
+        if y[iteration] == round(float(output[0][0])):
+            num_correct += 1
+                                
+        
+        # for object classification models show imagenet class
+        print('Iteration {}; Processing time: {:.2f} ms; speed {:.2f} fps'.format(iteration, round(np.average(duration), 2),
+                                                                                  round(1000 * batch_size / np.average(duration), 2)
+                                                                                  ))
+        iteration += 1
+        
+        
+        
+    print('finished with {} out of {} correct'.format(num_correct, iterations))
+
+
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