@@ -39,7 +39,7 @@ sleep 60
3939
4040echo " Invoking batch endpoint with public URI (MNIST)"
4141# <start_batch_scoring_job>
42- JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata .blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
42+ JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2 .blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
4343# </start_batch_scoring_job>
4444
4545echo " Showing job detail"
7171echo " Invoke batch endpoint with specific output file name"
7272# <start_batch_scoring_job_configure_output_settings>
7373export OUTPUT_FILE_NAME=predictions_` echo $RANDOM ` .csv
74- JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata .blob.core.windows.net/data/mnist/sample --input-type uri_folder --output-path azureml://datastores/workspaceblobstore/paths/$ENDPOINT_NAME --set output_file_name=$OUTPUT_FILE_NAME --query name -o tsv)
74+ JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2 .blob.core.windows.net/data/mnist/sample --input-type uri_folder --output-path azureml://datastores/workspaceblobstore/paths/$ENDPOINT_NAME --set output_file_name=$OUTPUT_FILE_NAME --query name -o tsv)
7575# </start_batch_scoring_job_configure_output_settings>
7676
7777echo " Invoke batch endpoint with specific overwrites"
7878# <start_batch_scoring_job_overwrite>
7979export OUTPUT_FILE_NAME=predictions_` echo $RANDOM ` .csv
80- JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata .blob.core.windows.net/data/mnist/sample --input-type uri_folder --mini-batch-size 20 --instance-count 5 --query name -o tsv)
80+ JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2 .blob.core.windows.net/data/mnist/sample --input-type uri_folder --mini-batch-size 20 --instance-count 5 --query name -o tsv)
8181# </start_batch_scoring_job_overwrite>
8282
8383echo " Stream job detail"
@@ -114,7 +114,7 @@ az ml batch-deployment create --file endpoints/batch/deploy-models/mnist-classif
114114echo " Invoke batch endpoint with public data"
115115# <test_new_deployment>
116116DEPLOYMENT_NAME=" mnist-keras-dpl"
117- JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --deployment-name $DEPLOYMENT_NAME --input https://azuremlexampledata .blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
117+ JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --deployment-name $DEPLOYMENT_NAME --input https://azuremldata2 .blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
118118# </test_new_deployment>
119119
120120echo " Show job detail"
@@ -155,7 +155,7 @@ az ml batch-endpoint show --name $ENDPOINT_NAME --query "{Name:name, Defaults:de
155155
156156echo " Invoke batch endpoint with the new default deployment with public URI"
157157# <test_new_default_deployment>
158- JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremlexampledata .blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
158+ JOB_NAME=$( az ml batch-endpoint invoke --name $ENDPOINT_NAME --input https://azuremldata2 .blob.core.windows.net/data/mnist/sample --input-type uri_folder --query name -o tsv)
159159# </test_new_default_deployment>
160160
161161echo " Stream job logs to console"
@@ -198,7 +198,7 @@ RESPONSE=$(curl --location --request POST "$SCORING_URI" \
198198 \" properties\" : {
199199 \" dataset\" : {
200200 \" dataInputType\" : \" DataUrl\" ,
201- \" Path\" : \" https://azuremlexampledata .blob.core.windows.net/data/mnist/sample\"
201+ \" Path\" : \" https://azuremldata2 .blob.core.windows.net/data/mnist/sample\"
202202 }
203203 }
204204}" )
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