Amazon CLI v1 examples
The example in the preceding section was for Amazon CLI v2. The following request and response examples to and from the endpoint use Amazon CLI v1.
In the following code example, the request consists of a single record and the response is its probability value.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-sagemaker-xgboost-model \ --content-type text/csv \ --accept text/csv \ --body '1,2,3,4' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
0.6
In the following code example, the request consists of two records, and the response includes their probabilities, which are separated by a comma.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-sagemaker-xgboost-model \ --content-type text/csv \ --accept text/csv \ --body $'1,2,3,4\n5,6,7,8' \ /dev/stderr 1>/dev/null
From the previous code example, the $'content' expression in
the --body tells the command to interpret '\n' in
the content as a line break. The response output follows.
0.6,0.3
In the following code example, the request consists of two records, the response includes their probabilities, separated with a line break.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-csv-1 \ --content-type text/csv \ --accept text/csv \ --body $'1,2,3,4\n5,6,7,8' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
0.6 0.3
In the following code example, the request consists of a single record, and the response is probability values from a multiclass model containing three classes.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-csv-1 \ --content-type text/csv \ --accept text/csv \ --body '1,2,3,4' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
0.1,0.6,0.3
In the following code example, the request consists of two records, and the response includes their probability values from a multiclass model containing three classes.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-csv-1 \ --content-type text/csv \ --accept text/csv \ --body $'1,2,3,4\n5,6,7,8' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
0.1,0.6,0.3 0.2,0.5,0.3
In the following code example, the request consists of two records, and the response includes predicted label and probability.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-csv-2 \ --content-type text/csv \ --accept text/csv \ --body $'1,2,3,4\n5,6,7,8' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
1,0.6 0,0.3
In the following code example, the request consists of two records and the response includes label headers and probabilities.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-csv-3 \ --content-type text/csv \ --accept text/csv \ --body $'1,2,3,4\n5,6,7,8' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
"['cat','dog','fish']","[0.1,0.6,0.3]" "['cat','dog','fish']","[0.2,0.5,0.3]"
In the following code example, the request consists of a single record and the response is its probability value.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-jsonlines \ --content-type application/jsonlines \ --accept application/jsonlines \ --body '{"features":["This is a good product",5]}' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
{"score":0.6}
In the following code example, the request contains two records, and the response includes predicted label and probability.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-jsonlines-2 \ --content-type application/jsonlines \ --accept application/jsonlines \ --body $'{"features":[1,2,3,4]}\n{"features":[5,6,7,8]}' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
{"predicted_label":1,"probability":0.6} {"predicted_label":0,"probability":0.3}
In the following code example, the request contains two records, and the response includes label headers and probabilities.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-jsonlines-3 \ --content-type application/jsonlines \ --accept application/jsonlines \ --body $'{"data":{"features":[1,2,3,4]}}\n{"data":{"features":[5,6,7,8]}}' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
{"predicted_labels":["cat","dog","fish"],"probabilities":[0.1,0.6,0.3]} {"predicted_labels":["cat","dog","fish"],"probabilities":[0.2,0.5,0.3]}
In the following code example, the request is in CSV format and the response is in JSON Lines format.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-csv-in-jsonlines-out \ --content-type text/csv \ --accept application/jsonlines \ --body $'1,2,3,4\n5,6,7,8' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
{"probability":0.6} {"probability":0.3}
In the following code example, the request is in JSON Lines format and the response is in CSV format.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-jsonlines-in-csv-out \ --content-type application/jsonlines \ --accept text/csv \ --body $'{"features":[1,2,3,4]}\n{"features":[5,6,7,8]}' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
0.6 0.3
In the following code example, the request is in CSV format and the response is in JSON format.
aws sagemaker-runtime invoke-endpoint \ --endpoint-name test-endpoint-csv-in-jsonlines-out \ --content-type text/csv \ --accept application/jsonlines \ --body $'1,2,3,4\n5,6,7,8' \ /dev/stderr 1>/dev/null
From the previous code example, the response output follows.
{"predictions":[{"label":1,"score":0.6},{"label":0,"score":0.3}]}