initial commit ➕➕➕

Author: alloc7260

0.png

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+channels:
+  - conda-forge
+dependencies:
+  - python=3.9
+  - pip=23.0.1
+  - pip:
+    - azureml-defaults==1.53.0
+    - torch
+    - transformers
+    - pillow
+    - pandas
+    - scipy

1.png

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+from transformers import pipeline
+from PIL import Image
+import torch
+import base64
+import json
+from io import BytesIO
+import logging
+
+def img_to_base64(image):
+    im_file = BytesIO()
+    image.save(im_file, format="JPEG")
+    im_bytes = im_file.getvalue()
+    im_b64 = base64.b64encode(im_bytes)
+    return im_b64
+
+def init():
+    global semantic_segmentation
+    semantic_segmentation = pipeline("image-segmentation", model="facebook/maskformer-swin-large-coco", 
+                                    device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
+    logging.info(f"pipeline device : {semantic_segmentation.device}")
+    logging.info("Init complete")
+
+def run(raw_data):
+    logging.info("model : request received")
+    data = json.loads(raw_data)
+    image = Image.open(BytesIO(base64.b64decode(data['img'])))
+    results = semantic_segmentation(image)
+    for i,j in enumerate(results):
+        img = j['mask']
+        wef = img_to_base64(img)
+        results[i]['mask'] = wef.decode("utf-8")
+    logging.info("Request processed")
+    return results

2.png

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+{
+    "img": "<base64 encoded img>"
+}

3.png

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+import urllib.request
+import json
+import os
+import ssl
+import time
+
+def allowSelfSignedHttps(allowed):
+    # bypass the server certificate verification on client side
+    if allowed and not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None):
+        ssl._create_default_https_context = ssl._create_unverified_context
+
+allowSelfSignedHttps(True) # this line is needed if you use self-signed certificate in your scoring service.
+
+# Request data goes here
+# The example below assumes JSON formatting which may be updated
+# depending on the format your endpoint expects.
+# More information can be found here:
+# https://docs.microsoft.com/azure/machine-learning/how-to-deploy-advanced-entry-script
+
+with open('test cpu/request.json') as f:
+    data = json.load(f)
+
+body = str.encode(json.dumps(data))
+
+url = 'https://custom-endpoint.centralindia.inference.ml.azure.com/score'
+# Replace this with the primary/secondary key, AMLToken, or Microsoft Entra ID token for the endpoint
+api_key = 'f8pjDedS03UCIOVtZdr9xPcO3ImPnXF4'
+if not api_key:
+    raise Exception("A key should be provided to invoke the endpoint")
+
+# The azureml-model-deployment header will force the request to go to a specific deployment.
+# Remove this header to have the request observe the endpoint traffic rules
+headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key), 'azureml-model-deployment': 'deployment-1' }
+
+req = urllib.request.Request(url, body, headers)
+
+try:
+    t1 = time.time()
+    response = urllib.request.urlopen(req)
+    print("Time taken to get response : ", time.time()-t1)
+    result = response.read()
+    # print(result)
+except urllib.error.HTTPError as error:
+    print("The request failed with status code: " + str(error.code))
+
+    # Print the headers - they include the requert ID and the timestamp, which are useful for debugging the failure
+    print(error.info())
+    print(error.read().decode("utf8", 'ignore'))