Add sdk and cli example for binary payloads (#1795)
* Init * Formatting * Remove testing data * Edit * Remove dev vars * Formatting * Replace paths and other fixes * Remove test data * Edit * Refresh workflow * Kernelspec * Untouch kernelspec in batch * Edits * Cleanup * Remove dev string and formatting * Paths... * Formatting * Paths Co-authored-by: Shohei Nagata <Shohei.Nagata@microsoft.com>
This commit is contained in:
Alex Wallace
GitHub
Родитель
5172b8b44d
Коммит
504cfdb5f5
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@ -0,0 +1,30 @@
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name: cli-scripts-deploy-moe-binary-payloads
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on:
|
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workflow_dispatch:
|
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schedule:
|
||||
- cron: "0 0/4 * * *"
|
||||
pull_request:
|
||||
branches:
|
||||
- main
|
||||
- sdk-preview
|
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paths:
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- cli/deploy-moe-binary-payloads.sh
|
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- .github/workflows/cli-scripts-deploy-moe-binary-payloads.yml
|
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- cli/setup.sh
|
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jobs:
|
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build:
|
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runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: check out repo
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uses: actions/checkout@v2
|
||||
- name: azure login
|
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uses: azure/login@v1
|
||||
with:
|
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creds: ${{secrets.AZ_CREDS}}
|
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- name: setup
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run: bash setup.sh
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working-directory: cli
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continue-on-error: true
|
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- name: test script script
|
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run: set -e; bash -x deploy-moe-binary-payloads.sh
|
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working-directory: cli
|
||||
89
.github/workflows/sdk-endpoints-online-managed-online-endpoints-binary-payloads.yml
поставляемый
Normal file
89
.github/workflows/sdk-endpoints-online-managed-online-endpoints-binary-payloads.yml
поставляемый
Normal file
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@ -0,0 +1,89 @@
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name: sdk-endpoints-online-managed-online-endpoints-binary-payloads
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# This file is created by sdk/python/readme.py.
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# Please do not edit directly.
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on:
|
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workflow_dispatch:
|
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schedule:
|
||||
- cron: "0 */8 * * *"
|
||||
pull_request:
|
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branches:
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- main
|
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paths:
|
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- sdk/python/endpoints/online/managed/**
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- .github/workflows/sdk-endpoints-online-managed-online-endpoints-binary-payloads.yml
|
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- sdk/python/dev-requirements.txt
|
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- infra/**
|
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- sdk/python/setup.sh
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concurrency:
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group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
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cancel-in-progress: true
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jobs:
|
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build:
|
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runs-on: ubuntu-latest
|
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steps:
|
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- name: check out repo
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uses: actions/checkout@v2
|
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- name: setup python
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uses: actions/setup-python@v2
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with:
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python-version: "3.8"
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- name: pip install notebook reqs
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run: pip install -r sdk/python/dev-requirements.txt
|
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- name: azure login
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uses: azure/login@v1
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with:
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creds: ${{secrets.AZUREML_CREDENTIALS}}
|
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- name: bootstrap resources
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run: |
|
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echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
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bash bootstrap.sh
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working-directory: infra
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continue-on-error: false
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- name: setup SDK
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run: |
|
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source "${{ github.workspace }}/infra/sdk_helpers.sh";
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source "${{ github.workspace }}/infra/init_environment.sh";
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bash setup.sh
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working-directory: sdk/python
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continue-on-error: true
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- name: setup-cli
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run: |
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source "${{ github.workspace }}/infra/sdk_helpers.sh";
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source "${{ github.workspace }}/infra/init_environment.sh";
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bash setup.sh
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working-directory: cli
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continue-on-error: true
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- name: run endpoints/online/managed/online-endpoints-binary-payloads.ipynb
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run: |
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source "${{ github.workspace }}/infra/sdk_helpers.sh";
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source "${{ github.workspace }}/infra/init_environment.sh";
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bash "${{ github.workspace }}/infra/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json";
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bash "${{ github.workspace }}/infra/sdk_helpers.sh" replace_template_values "online-endpoints-binary-payloads.ipynb";
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[ -f "../../.azureml/config" ] && cat "../../.azureml/config";
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papermill -k python online-endpoints-binary-payloads.ipynb online-endpoints-binary-payloads.output.ipynb
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working-directory: sdk/python/endpoints/online/managed
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- name: upload notebook's working folder as an artifact
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if: ${{ always() }}
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uses: actions/upload-artifact@v2
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with:
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name: online-endpoints-binary-payloads
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path: sdk/python/endpoints/online/managed
|
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|
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- name: Send IcM on failure
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||||
if: ${{ failure() && github.ref_type == 'branch' && (github.ref_name == 'main' || contains(github.ref_name, 'release')) }}
|
||||
uses: ./.github/actions/generate-icm
|
||||
with:
|
||||
host: ${{ secrets.AZUREML_ICM_CONNECTOR_HOST_NAME }}
|
||||
connector_id: ${{ secrets.AZUREML_ICM_CONNECTOR_CONNECTOR_ID }}
|
||||
certificate: ${{ secrets.AZUREML_ICM_CONNECTOR_CERTIFICATE }}
|
||||
private_key: ${{ secrets.AZUREML_ICM_CONNECTOR_PRIVATE_KEY }}
|
||||
args: |
|
||||
incident:
|
||||
Title: "[azureml-examples] Notebook validation failed on branch '${{ github.ref_name }}' for notebook 'endpoints/online/managed/online-endpoints-binary-payloads.ipynb'"
|
||||
Summary: |
|
||||
Notebook 'endpoints/online/managed/online-endpoints-binary-payloads.ipynb' is failing on branch '${{ github.ref_name }}': ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
|
||||
Severity: 4
|
||||
RoutingId: "github://azureml-examples"
|
||||
Status: Active
|
||||
Source:
|
||||
IncidentId: "endpoints/online/managed/online-endpoints-binary-payloads.ipynb[${{ github.ref_name }}]"
|
||||
|
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@ -58,6 +58,7 @@ path|status|
|
|||
[deploy-mlcompute-update-to-system-identity.sh](deploy-mlcompute-update-to-system-identity.sh)|[](https://github.com/Azure/azureml-examples/actions/workflows/cli-scripts-deploy-mlcompute-update-to-system-identity.yml)
|
||||
[deploy-mlcompute-update-to-user-identity.sh](deploy-mlcompute-update-to-user-identity.sh)|[](https://github.com/Azure/azureml-examples/actions/workflows/cli-scripts-deploy-mlcompute-update-to-user-identity.yml)
|
||||
[deploy-moe-autoscale.sh](deploy-moe-autoscale.sh)|[](https://github.com/Azure/azureml-examples/actions/workflows/cli-scripts-deploy-moe-autoscale.yml)
|
||||
[deploy-moe-binary-payloads.sh](deploy-moe-binary-payloads.sh)|[](https://github.com/Azure/azureml-examples/actions/workflows/cli-scripts-deploy-moe-binary-payloads.yml)
|
||||
[deploy-moe-inference-schema.sh](deploy-moe-inference-schema.sh)|[](https://github.com/Azure/azureml-examples/actions/workflows/cli-scripts-deploy-moe-inference-schema.yml)
|
||||
[deploy-moe-keyvault.sh](deploy-moe-keyvault.sh)|[](https://github.com/Azure/azureml-examples/actions/workflows/cli-scripts-deploy-moe-keyvault.yml)
|
||||
[deploy-moe-minimal-single-model-registered.sh](deploy-moe-minimal-single-model-registered.sh)|[](https://github.com/Azure/azureml-examples/actions/workflows/cli-scripts-deploy-moe-minimal-single-model-registered.yml)
|
||||
|
|
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@ -0,0 +1,82 @@
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#!/bin/bash
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set -e
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# <set_variables>
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ENDPOINT_NAME=endpt-moe-`echo $RANDOM`
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||||
ACR_NAME=$(az ml workspace show --query container_registry -o tsv | cut -d'/' -f9-)
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# </set_variables>
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|
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BASE_PATH="endpoints/online/managed/binary-payloads"
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|
||||
# <download_sample_data>
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||||
wget https://aka.ms/peacock-pic -O endpoints/online/managed/binary-payloads/input.jpg
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||||
# </download_sample_data>
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||||
|
||||
# <create_endpoint>
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az ml online-endpoint create -n $ENDPOINT_NAME
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||||
# </create_endpoint>
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||||
|
||||
# Check if endpoint was successful
|
||||
endpoint_status=`az ml online-endpoint show --name $ENDPOINT_NAME --query "provisioning_state" -o tsv `
|
||||
echo $endpoint_status
|
||||
if [[ $endpoint_status == "Succeeded" ]]
|
||||
then
|
||||
echo "Endpoint created successfully"
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||||
else
|
||||
echo "Endpoint creation failed"
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||||
exit 1
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||||
fi
|
||||
|
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# <create_deployment>
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az ml online-deployment create -e $ENDPOINT_NAME -f $BASE_PATH/binary-payloads-deployment.yml \
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--set code_configuration.scoring_script=single-file-to-file-score.py \
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--all-traffic
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||||
# </create_deployment>
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|
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# <get_endpoint_details>
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# Get key
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||||
echo "Getting access key..."
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KEY=$(az ml online-endpoint get-credentials -n $ENDPOINT_NAME --query primaryKey -o tsv )
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||||
# Get scoring url
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||||
echo "Getting scoring url..."
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||||
SCORING_URL=$(az ml online-endpoint show -n $ENDPOINT_NAME --query scoring_uri -o tsv )
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echo "Scoring url is $SCORING_URL"
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# </get_endpoint_details>
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|
||||
# <get_logs>
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az ml online-deployment get-logs -n binary-payload -e $ENDPOINT_NAME
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# </get_logs>
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||||
|
||||
# <check_deployment>
|
||||
# Check if deployment was successful
|
||||
deploy_status=`az ml online-deployment show --name binary-payload --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv `
|
||||
echo $deploy_status
|
||||
if [[ $deploy_status == "Succeeded" ]]
|
||||
then
|
||||
echo "Deployment completed successfully"
|
||||
else
|
||||
echo "Deployment failed"
|
||||
exit 1
|
||||
fi
|
||||
# </check_deployment>
|
||||
|
||||
# <test_online_endpoint_1>
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||||
curl -X POST -F "file=@endpoints/online/managed/binary-payloads/input.jpg" -H "Authorization: Bearer $KEY" $SCORING_URL \
|
||||
-o endpoints/online/managed/binary-payloads/output.jpg
|
||||
# <test_online_endpoint_1>
|
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|
||||
# <update_deployment2>
|
||||
az ml online-deployment update -e $ENDPOINT_NAME -n binary-payload \
|
||||
--set code_configuration.scoring_script="multi-file-to-json-score.py"
|
||||
# </updat _deployment2>
|
||||
|
||||
# <test_online_endpoint_2>
|
||||
curl -X POST -F "file[]=@endpoints/online/managed/binary-payloads/input.jpg" \
|
||||
-F "file[]=@endpoints/online/managed/binary-payloads/output.jpg" \
|
||||
-H "Authorization: Bearer $KEY" $SCORING_URL
|
||||
# <test_online_endpoint_2>
|
||||
|
||||
# <delete_assets>
|
||||
az ml online-endpoint delete -n $ENDPOINT_NAME --no-wait --yes
|
||||
# </delete_assets>
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
|
||||
name: binary-payload
|
||||
endpoint_name: <ENDPOINT_NAME>
|
||||
model:
|
||||
path: .
|
||||
code_configuration:
|
||||
code: code
|
||||
scoring_script: <SCORING_SCRIPT>
|
||||
environment:
|
||||
image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest
|
||||
conda_file: env.yml
|
||||
instance_type: Standard_DS2_v2
|
||||
instance_count: 1
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
from azureml.contrib.services.aml_request import AMLRequest, rawhttp
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def init():
|
||||
pass
|
||||
|
||||
|
||||
@rawhttp
|
||||
def run(req: AMLRequest):
|
||||
files = req.files.getlist("file[]")
|
||||
sizes = [{"filename": f.filename, "size": Image.open(f.stream).size} for f in files]
|
||||
return {"response": sizes}
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
from azureml.contrib.services.aml_request import AMLRequest, rawhttp
|
||||
from azureml.contrib.services.aml_response import AMLResponse
|
||||
from PIL import Image
|
||||
import io
|
||||
|
||||
default_resize = (128, 128)
|
||||
|
||||
|
||||
def init():
|
||||
pass
|
||||
|
||||
|
||||
@rawhttp
|
||||
def run(req: AMLRequest):
|
||||
try:
|
||||
data = req.files.getlist("file")[0]
|
||||
except IndexError:
|
||||
return AMLResponse("No file uploaded", status_code=422)
|
||||
|
||||
img = Image.open(data.stream)
|
||||
img = img.resize(default_resize)
|
||||
|
||||
output = io.BytesIO()
|
||||
img.save(output, format="JPEG")
|
||||
resp = AMLResponse(message=output.getvalue(), status_code=200)
|
||||
resp.mimetype = "image/jpg"
|
||||
|
||||
return resp
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
name: userenv
|
||||
dependencies:
|
||||
- python=3.10
|
||||
- pip
|
||||
- pip:
|
||||
- pillow~=9.3
|
||||
- azureml-defaults>=1.47,<2
|
||||
|
|
@ -55,6 +55,7 @@ Test Status is for branch - **_main_**
|
|||
|endpoints|online|[debug-online-endpoints-locally-in-visual-studio-code](endpoints/online/managed/debug-online-endpoints-locally-in-visual-studio-code.ipynb)|*no description* - _This sample is excluded from automated tests_|[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-online-managed-debug-online-endpoints-locally-in-visual-studio-code.yml)|
|
||||
|endpoints|online|[online-endpoints-managed-identity-sai](endpoints/online/managed/managed-identities/online-endpoints-managed-identity-sai.ipynb)|*no description* - _This sample is excluded from automated tests_|[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-online-managed-managed-identities-online-endpoints-managed-identity-sai.yml)|
|
||||
|endpoints|online|[online-endpoints-managed-identity-uai](endpoints/online/managed/managed-identities/online-endpoints-managed-identity-uai.ipynb)|*no description* - _This sample is excluded from automated tests_|[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-online-managed-managed-identities-online-endpoints-managed-identity-uai.yml)|
|
||||
|endpoints|online|[online-endpoints-binary-payloads](endpoints/online/managed/online-endpoints-binary-payloads.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-online-managed-online-endpoints-binary-payloads.yml)|
|
||||
|endpoints|online|[online-endpoints-inference-schema](endpoints/online/managed/online-endpoints-inference-schema.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-online-managed-online-endpoints-inference-schema.yml)|
|
||||
|endpoints|online|[online-endpoints-keyvault](endpoints/online/managed/online-endpoints-keyvault.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-online-managed-online-endpoints-keyvault.yml)|
|
||||
|endpoints|online|[online-endpoints-multimodel](endpoints/online/managed/online-endpoints-multimodel.ipynb)|*no description*|[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-online-managed-online-endpoints-multimodel.yml)|
|
||||
|
|
|
|||
|
|
@ -0,0 +1,16 @@
|
|||
from azureml.contrib.services.aml_request import AMLRequest, rawhttp
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def init():
|
||||
pass
|
||||
|
||||
|
||||
@rawhttp
|
||||
def run(req: AMLRequest):
|
||||
sizes = [
|
||||
{"filename": f.filename, "size": Image.open(f.stream).size}
|
||||
for f in req.files.getlist("file[]")
|
||||
]
|
||||
|
||||
return {"response": sizes}
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
from azureml.contrib.services.aml_request import AMLRequest, rawhttp
|
||||
from azureml.contrib.services.aml_response import AMLResponse
|
||||
from email.mime.multipart import MIMEMultipart
|
||||
from email.mime.image import MIMEImage
|
||||
from PIL import Image
|
||||
import io
|
||||
|
||||
default_resize = (128, 128)
|
||||
|
||||
|
||||
def init():
|
||||
pass
|
||||
|
||||
|
||||
@rawhttp
|
||||
def run(req: AMLRequest):
|
||||
try:
|
||||
data = req.files.getlist("file")[0]
|
||||
except IndexError:
|
||||
return AMLResponse("No file uploaded", status_code=422)
|
||||
|
||||
img = Image.open(data.stream)
|
||||
img = img.resize(default_resize)
|
||||
|
||||
output = io.BytesIO()
|
||||
img.save(output, format="JPEG")
|
||||
resp = AMLResponse(message=output.getvalue(), status_code=200)
|
||||
resp.mimetype = "image/jpg"
|
||||
|
||||
return resp
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
name: userenv
|
||||
dependencies:
|
||||
- python=3.10
|
||||
- pip
|
||||
- pip:
|
||||
- pillow
|
||||
- azureml-inference-server-http
|
||||
- azureml-contrib-services
|
||||
|
|
@ -0,0 +1,461 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Handle binary payloads from a Managed Online Endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In this example, receiving and sending binary payloads in scoring scripts is demonstrated using the `rawhttp` decorator as well as the `AMLRequest` and `AMLResponse` objects. Without `rawhttp`, the run function is called passed the serialized JSON from the payload. Using `rawhttp`, the run function is instead passed an `AMLRequest` object, which wraps the native Flask request object used internally by the Azure Inference Server. After handling binary payloads, one can either return a JSON-serializable object as usual or use the `AMLResponse` object to have full control over the response, including returning binary payloads. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Configure parameters, assets, and clients"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 1.1 Import required libraries"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "1-import-required-libraries"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azure.ai.ml import MLClient\n",
|
||||
"from azure.ai.ml.entities import (\n",
|
||||
" ManagedOnlineEndpoint,\n",
|
||||
" ManagedOnlineDeployment,\n",
|
||||
" Model,\n",
|
||||
" CodeConfiguration,\n",
|
||||
" Environment,\n",
|
||||
")\n",
|
||||
"from azure.identity import DefaultAzureCredential\n",
|
||||
"import random, os, requests"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"name": "1-import-libraries"
|
||||
},
|
||||
"source": [
|
||||
"### 1.2 Set workspace details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "1-set-workspace-details"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"subscription_id = \"<SUBSCRIPTION_ID>\"\n",
|
||||
"resource_group = \"<RESOURCE_GROUP>\"\n",
|
||||
"workspace_name = \"<AML_WORKSPACE_NAME>\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"name": "1-set-workspace"
|
||||
},
|
||||
"source": [
|
||||
"### 1.3 Set variables"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "1-set-variables"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rand = random.randint(0, 10000)\n",
|
||||
"\n",
|
||||
"endpoint_name = f\"endpt-moe-{rand}\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 1.4 Download sample data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "1-download-sample-data"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"url = \"https://aka.ms/peacock-pic\"\n",
|
||||
"agent = f\"Python Requests/{requests.__version__} (https://github.com/Azure/azureml-examples)\"\n",
|
||||
"r = requests.get(url, headers={\"User-Agent\": agent}, allow_redirects=True)\n",
|
||||
"open(\"binary-payloads/input.jpg\", \"wb\").write(r.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 1.5 Create an MLClient Instance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "1-create-mlclient-instance"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"credential = DefaultAzureCredential()\n",
|
||||
"ml_client = MLClient(\n",
|
||||
" credential,\n",
|
||||
" subscription_id=subscription_id,\n",
|
||||
" resource_group_name=resource_group,\n",
|
||||
" workspace_name=workspace_name,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Create endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "2-create-endpoint"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint = ManagedOnlineEndpoint(name=endpoint_name)\n",
|
||||
"endpoint = ml_client.online_endpoints.begin_create_or_update(endpoint).result()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. Create a Binary-to-Binary Deployment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This script receives an image as a binary file and returns a resized image as a binary file. Both scoring scripts use the `rawhttp` decorator to change the argument passed to the run function from JSON to the entire `AMLRequest` object. This script also uses the `AMLResponse` object "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
|
||||
"from azureml.contrib.services.aml_response import AMLResponse\n",
|
||||
"from email.mime.multipart import MIMEMultipart\n",
|
||||
"from email.mime.image import MIMEImage\n",
|
||||
"from PIL import Image\n",
|
||||
"import io \n",
|
||||
"\n",
|
||||
"default_resize = (128, 128)\n",
|
||||
"\n",
|
||||
"def init(): \n",
|
||||
" pass \n",
|
||||
"\n",
|
||||
"@rawhttp\n",
|
||||
"def run(req : AMLRequest):\n",
|
||||
" try:\n",
|
||||
" data = req.files.getlist(\"file\")[0]\n",
|
||||
" except IndexError:\n",
|
||||
" return AMLResponse(\"No file uploaded\", status_code=422)\n",
|
||||
" \n",
|
||||
" img = Image.open(data.stream)\n",
|
||||
" img = img.resize(default_resize)\n",
|
||||
"\n",
|
||||
" output = io.BytesIO()\n",
|
||||
" img.save(output, format=\"JPEG\")\n",
|
||||
" resp = AMLResponse(message = output.getvalue(), status_code=200)\n",
|
||||
" resp.mimetype = \"image/jpg\"\n",
|
||||
"\n",
|
||||
" return resp\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 3.1 Create the deployment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "3-create-deployment"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"deployment = ManagedOnlineDeployment(\n",
|
||||
" name=\"binarypayloads\",\n",
|
||||
" endpoint_name=endpoint_name,\n",
|
||||
" model=Model(path=\"binary-payloads\"),\n",
|
||||
" code_configuration=CodeConfiguration(\n",
|
||||
" code=\"binary-payloads/code\", scoring_script=\"single-file-to-file-score.py\"\n",
|
||||
" ),\n",
|
||||
" environment=Environment(\n",
|
||||
" conda_file=\"binary-payloads/env.yml\",\n",
|
||||
" image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n",
|
||||
" ),\n",
|
||||
" instance_type=\"Standard_DS2_v2\",\n",
|
||||
" instance_count=1,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 3.2 Create the deployment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "3-create-deployment"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"deployment = ml_client.online_deployments.begin_create_or_update(deployment).result()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 3.3 Update endpoint traffic"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "3-update-endpoint-traffic"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"endpoint.traffic = {\"binarypayloads\": 100}\n",
|
||||
"endpoint = ml_client.online_endpoints.begin_create_or_update(endpoint).result()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 3.4 Get endpoint details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "3-get-endpoint-details"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scoring_uri = endpoint.scoring_uri\n",
|
||||
"key = ml_client.online_endpoints.get_keys(endpoint_name).primary_key"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 3.5 Test the endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "3-test-endpoint"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"res = requests.post(\n",
|
||||
" url=scoring_uri,\n",
|
||||
" headers={\"Authorization\": f\"Bearer {key}\"},\n",
|
||||
" files=[(\"file\", open(\"binary-payloads/input.jpg\", \"rb\"))],\n",
|
||||
")\n",
|
||||
"open(\"binary-payloads/output.jpg\", \"wb\").write(res.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 5. Create a Binary-to-JSON Deployment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 5.1 Examine the scoring script\n",
|
||||
"This script accepts multiple image files uploaded as `file[]` and returns the sizes of the images as JSON. Both scoring scripts use the `rawhttp` decorator to change the argument passed to the run function from JSON to the entire `AMLRequest` object. However, unlike the first script this one returns a dictionary rather than an `AMLResponse` object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
|
||||
"from PIL import Image\n",
|
||||
"\n",
|
||||
"def init(): \n",
|
||||
" pass \n",
|
||||
"\n",
|
||||
"@rawhttp\n",
|
||||
"def run(req : AMLRequest):\n",
|
||||
" sizes = [{\"filename\" : f.filename,\n",
|
||||
" \"size\" : Image.open(f.stream).size}\n",
|
||||
" for f in req.files.getlist(\"file[]\")]\n",
|
||||
"\n",
|
||||
" return {\"response\" : sizes}\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 5.2 Update the deployment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "5-update-deployment"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"deployment.code_configuration = CodeConfiguration(\n",
|
||||
" code=deployment.code_configuration.code,\n",
|
||||
" scoring_script=\"code/multi-file-to-json-score.py\",\n",
|
||||
")\n",
|
||||
"deployment = ml_client.online_deployments.begin_create_or_update(deployment).result()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 6. Test the endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 6.1 Send a request"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "6-send-request"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"res = requests.post(\n",
|
||||
" url=scoring_uri,\n",
|
||||
" headers={\"Authorization\": f\"Bearer {key}\"},\n",
|
||||
" files=[\n",
|
||||
" (\"file[]\", open(\"binary-payloads/input.jpg\", \"rb\")),\n",
|
||||
" (\"file[]\", open(\"binary-payloads/output.jpg\", \"rb\")),\n",
|
||||
" ],\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 7. Delete assets"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 7.1 Delete the endpoint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"name": "7-delete-endpoint"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ml_client.online_endpoints.begin_delete(name=endpoint_name)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.10 - SDK V2",
|
||||
"language": "python",
|
||||
"name": "python310-sdkv2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.4"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "c54d4b4f21f908d21f1064b6d031502c08620e465e849bef5aa76d1f6a474870"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Загрузка…
Ссылка в новой задаче