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Update notebook for SageMaker SDK v2 compatibility #14

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Update notebook for SageMaker SDK v2 compatibility #14

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40 changes: 31 additions & 9 deletions source/notebooks/sagemaker_fraud_detection.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -310,12 +310,9 @@
},
"outputs": [],
"source": [
"from sagemaker.serializers import CSVSerializer\n",
"from sagemaker.serializers import CSVSerializer \n",
"from sagemaker.deserializers import JSONDeserializer\n",
" \n",
"rcf_predictor.content_type = 'text/csv'\n",
"rcf_predictor.serializer = CSVSerializer()\n",
"rcf_predictor.accept = 'application/json'\n",
"rcf_predictor.serializer = CSVSerializer() \n",
"rcf_predictor.deserializer = JSONDeserializer()"
]
},
Expand Down Expand Up @@ -372,6 +369,7 @@
"metadata": {},
"outputs": [],
"source": [
"%pip install seaborn --q\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"sns.set(color_codes=True)"
Expand Down Expand Up @@ -655,12 +653,12 @@
"source": [
"from sagemaker.serializers import CSVSerializer\n",
"\n",
"\n",
"predictor = clf.deploy(initial_instance_count=1,\n",
" model_name=\"{}-xgb\".format(config.SOLUTION_PREFIX),\n",
" endpoint_name=\"{}-xgb\".format(config.SOLUTION_PREFIX),\n",
" instance_type=instance_type,\n",
" serializer=CSVSerializer(),\n",
" deserializer=None)"
" serializer=CSVSerializer())"
]
},
{
Expand Down Expand Up @@ -696,7 +694,7 @@
" split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))\n",
" predictions = ''\n",
" for array in split_array:\n",
" predictions = ','.join([predictions, current_predictor.predict(array).decode('utf-8')])\n",
" predictions = ','.join([predictions, current_predictor.predict(array)])\n",
"\n",
" return np.fromstring(predictions[1:], sep=',')"
]
Expand Down Expand Up @@ -868,6 +866,18 @@
"pip install aws_requests_auth"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"for name in (\"botocore.credentials\", \"botocore\", \"boto3\", \"s3transfer\", \"urllib3\"):\n",
" logging.getLogger(name).setLevel(logging.WARNING) # or logging.ERROR / logging.CRITICAL"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1065,13 +1075,25 @@
"# Specify input and output formats.\n",
"smote_predictor.content_type = 'text/csv'\n",
"csv_serializer = CSVSerializer()\n",
"smote_predictor.serializer = csv_serializer\n",
"\n",
"# Set the deserializer to handle the response from the inference endpoint\n",
"#csv_deserializer = CSVDeserializer()\n",
"#smote_predictor.deserializer = csv_deserializer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sagemaker.serializers import CSVSerializer\n",
"from sagemaker.deserializers import StringDeserializer # predictions often come back as text\n",
"\n",
"smote_predictor.serializer = CSVSerializer() # sets Content-Type to text/csv\n",
"smote_predictor.deserializer = StringDeserializer()"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down