debug

mulit-model-endpoints/mme-on-gpu/cv/resnet50_mme_with_gpu.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "[Amazon SageMaker](https://aws.amazon.com/sagemaker/) multi-model endpoints(MME) provide a scalable and cost-effective way to deploy large number of deep learning models. Previously, customers had limited options to deploy 100s of deep learning models that need accelerated compute with GPUs. Now customers can deploy 1000s of deep learning models behind one SageMaker endpoint. Now, MME will run multiple models on a GPU core, share GPU instances behind an endpoint across multiple models and dynamically load/unload models based on the incoming traffic. With this, customers can significantly save cost and achieve best price performance.\n",
     "\n",
-    "<div class=\"alert alert-info\"> 💡 <strong> Note </strong>\n",
+    "<div class=\"alert alert-info\"><strong> Note </strong>\n",
     "This notebook was tested with the `conda_python3` kernel on an Amazon SageMaker notebook instance of type `g4dn.4xlarge`\n",
     "</div>"
    ]
@@ -175,7 +175,7 @@
     "This section presents overview of steps to prepare ResNet-50 pre-trained model to be deployed on SageMaker MME using Triton Inference server model configurations. \n",
     "\n",
     "\n",
-    "<div class=\"alert alert-info\"> 💡 <strong> Note </strong>\n",
+    "<div class=\"alert alert-info\"><strong> Note </strong>\n",
     "We are demonstrating deployment with 2 models. However, customers can prepare and 100s of models. The models may or may not share the same framework.\n",
     "</div>"
    ]
@@ -424,7 +424,7 @@
     "\n",
     "\n",
     "\n",
-    "<div class=\"alert alert-info\"> 💡 <strong> Note </strong>\n",
+    "<div class=\"alert alert-info\"><strong> Note </strong>\n",
     "you can deploy 100s of models. The models can use same framework. They can also use different frameworks as shown in this note.\n",
     "</div>\n",
     "\n",