Quick fixes for files referenced on docs (Azure#2164)

* batch endpoint CLI folders

* fixes

* docs sync

* black

---------

Co-authored-by: santiagxf <fasantia@microsfot.com>

cli/endpoints/batch/deploy-models/custom-outputs-parquet/deploy-and-run.sh

@@ -9,8 +9,8 @@ ENDPOINT_SUFIX=$(cat /dev/urandom | tr -dc 'a-zA-Z0-9' | fold -w ${1:-5} | head
 ENDPOINT_NAME="heart-classifier-$ENDPOINT_SUFIX"
 
 # <register_model>
-MODEL_NAME='heart-classifier'
-az ml model create --name $MODEL_NAME --type "mlflow_model" --path "model"
+MODEL_NAME='heart-classifier-sklpipe'
+az ml model create --name $MODEL_NAME --type "custom_model" --path "model"
 # <register_model>
 
 echo "Creating compute"
@@ -25,7 +25,7 @@ az ml batch-endpoint create -n $ENDPOINT_NAME -f endpoint.yml
 
 echo "Creating batch deployment $DEPLOYMENT_NAME for endpoint $ENDPOINT_NAME"
 # <create_batch_deployment_set_default>
-az ml batch-deployment create --file deployment-parquet/deployment.yml --endpoint-name $ENDPOINT_NAME --set-default
+az ml batch-deployment create --file deployment.yml --endpoint-name $ENDPOINT_NAME --set-default
 # </create_batch_deployment_set_default>
 
 echo "Update the batch deployment as default for the endpoint"

cli/endpoints/batch/deploy-models/custom-outputs-parquet/deployment.yml

@@ -2,9 +2,7 @@ $schema: https://azuremlschemas.azureedge.net/latest/batchDeployment.schema.json
 endpoint_name: heart-classifier-batch
 name: classifier-xgboost-custom
 description: A heart condition classifier based on XGBoost and Scikit-Learn pipelines that append predictions on parquet files.
-model:
-  path: model
-  name: heart-classifier-sklpipe
+model: azureml:heart-classifier-sklpipe@latest
 environment:
   name: batch-mlflow-xgboost
   image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest

cli/endpoints/batch/deploy-models/imagenet-classifier/deploy-and-run.sh

@@ -32,7 +32,7 @@ az ml batch-endpoint create --file endpoint.yml  --name $ENDPOINT_NAME
 
 echo "Creating batch deployment for endpoint $ENDPOINT_NAME"
 # <create_batch_deployment_set_default>
-az ml batch-deployment create --file deployment.yml --endpoint-name $ENDPOINT_NAME --set-default
+az ml batch-deployment create --file deployment-by-file.yml --endpoint-name $ENDPOINT_NAME --set-default
 # </create_batch_deployment_set_default>
 
 echo "Showing details of the batch endpoint"
@@ -46,9 +46,18 @@ DEPLOYMENT_NAME="imagenet-classifier-resnetv2"
 az ml batch-deployment show --name $DEPLOYMENT_NAME --endpoint-name $ENDPOINT_NAME
 # </check_batch_deployment_detail>
 
+# <download_sample_data>
+wget https://azuremlexampledata.blob.core.windows.net/data/imagenet-1000.zip
+unzip imagenet-1000.zip -d data
+# </download_sample_data>
+
+# <create_sample_data_asset>
+az ml data create -f imagenet-sample-unlabeled.yml
+# </create_sample_data_asset>
+
 echo "Invoking batch endpoint with local data"
 # <start_batch_scoring_job>
-JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input data --input-type uri_folder --query name -o tsv)
+JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input azureml:imagenet-sample-unlabeled@latest --query name -o tsv)
 # </start_batch_scoring_job>
 
 echo "Showing job detail"
@@ -77,9 +86,45 @@ else
 fi
 # </check_job_status>
 
+# <download_scores>
+az ml job download --name $JOB_NAME --output-name score --download-path .
+# </download_scores>
+
+echo "Creating batch deployment for endpoint $ENDPOINT_NAME with high throughput"
+# <create_batch_deployment_ht>
+az ml batch-deployment create --file deployment-by-batch.yml --endpoint-name $ENDPOINT_NAME --default
+# </create_batch_deployment_ht>
+
+echo "Invoking batch endpoint with local data"
+# <start_batch_scoring_job_ht>
+JOB_NAME=$(az ml batch-endpoint invoke --name $ENDPOINT_NAME --input azureml:imagenet-sample-unlabeled@latest --query name -o tsv)
+# </start_batch_scoring_job_ht>
+
+echo "Stream job logs to console"
+# <stream_job_logs_to_console_ht>
+az ml job stream -n $JOB_NAME
+# </stream_job_logs_to_console_ht>
+
+# <check_job_status_ht>
+STATUS=$(az ml job show -n $JOB_NAME --query status -o tsv)
+echo $STATUS
+if [[ $STATUS == "Completed" ]]
+then
+  echo "Job completed"
+elif [[ $STATUS ==  "Failed" ]]
+then
+  echo "Job failed"
+  exit 1
+else 
+  echo "Job status not failed or completed"
+  exit 2
+fi
+# </check_job_status_ht>
+
 # <delete_endpoint>
 az ml batch-endpoint delete --name $ENDPOINT_NAME --yes
 # </delete_endpoint>
 
 echo "Clean temp files"
 find ./model -exec rm -rf {} +
+find ./data -exec rm -rf {} +

cli/endpoints/batch/deploy-models/imagenet-classifier/deployment-by-batch.yml

@@ -13,6 +13,9 @@ code_configuration:
   scoring_script: batch_driver.py
 resources:
   instance_count: 2
+tags:
+  device_acceleration: CUDA
+  device_batching: 16
 max_concurrency_per_instance: 1
 mini_batch_size: 5
 output_action: append_row

cli/endpoints/batch/deploy-models/imagenet-classifier/imagenet-sample-unlabeled.yml

@@ -0,0 +1,5 @@
+$schema: https://azuremlschemas.azureedge.net/latest/data.schema.json
+name: imagenet-sample-unlabeled
+description: A sample of 1000 images from the original ImageNet dataset. Download content from https://azuremlexampledata.blob.core.windows.net/data/imagenet-1000.zip.
+type: uri_folder
+path: data

cli/endpoints/batch/deploy-models/mnist-classifier/deployment-keras/deployment.yml

@@ -3,6 +3,7 @@ name: mnist-keras-dpl
 description: A deployment using Keras with TensorFlow to solve the MNIST classification dataset.
 endpoint_name: mnist-batch
 model: 
+  name: mnist-classifier-keras
   path: model
 code_configuration:
   code: code

cli/endpoints/batch/deploy-models/mnist-classifier/deployment-torch/code/batch_driver.py

@@ -1,6 +1,3 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
 import os
 import pandas as pd
 import torch
@@ -47,8 +44,8 @@ def run(mini_batch: List[str]) -> pd.DataFrame:
             results.append(
                 {
                     "file": basename(image_path),
-                    "class": predicted_class.numpy(),
-                    "probability": predicted_prob.numpy(),
+                    "class": predicted_class.numpy()[0],
+                    "probability": predicted_prob.numpy()[0],
                 }
             )
 

cli/endpoints/batch/deploy-models/mnist-classifier/deployment-torch/deployment.yml

@@ -2,7 +2,8 @@ $schema: https://azuremlschemas.azureedge.net/latest/batchDeployment.schema.json
 name: mnist-torch-dpl
 description: A deployment using Torch to solve the MNIST classification dataset.
 endpoint_name: mnist-batch
-model: 
+model:
+  name: mnist-classifier-torch
   path: model
 code_configuration:
   code: code

sdk/python/endpoints/batch/deploy-models/custom-outputs-parquet/custom-output-batch.ipynb

@@ -120,7 +120,7 @@
     "\n",
     "### 2.1 About the model\n",
     "\n",
-    "This example shows how you can deploy an MLflow model to a batch endpoint to perform batch predictions. This example uses a model based on the UCI Heart Disease Data Set. The database contains 76 attributes, but we are using a subset of 14 of them. The model tries to predict the presence of heart disease in a patient. It is integer valued from 0 (no presence) to 1 (presence).\n",
+    "This example shows how you can deploy a model to a batch endpoint to perform batch predictions. This example uses a model based on the UCI Heart Disease Data Set. The database contains 76 attributes, but we are using a subset of 14 of them. The model tries to predict the presence of heart disease in a patient. It is integer valued from 0 (no presence) to 1 (presence).\n",
     "\n",
     "The model has been trained using an `XGBBoost` classifier and all the required preprocessing has been packaged as a `scikit-learn` pipeline, making this model an end-to-end pipeline that goes from raw data to predictions.\n",
     "\n",
@@ -145,7 +145,7 @@
    },
    "outputs": [],
    "source": [
-    "model_name = \"heart-classifier\"\n",
+    "model_name = \"heart-classifier-sklpipe\"\n",
     "model_description = \"A heart condition classifier trained with XGBoosts and Scikit-Learn for feature processing.\"\n",
     "model_local_path = \"model\""
    ]
@@ -490,6 +490,7 @@
    "outputs": [],
    "source": [
     "environment = Environment(\n",
+    "    name=\"batch-mlflow-xgboost\",\n",
     "    conda_file=\"environment/conda.yml\",\n",
     "    image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n",
     ")"