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Pin mlflow image and conda file and disable dynamic package install #3456

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Pin mlflow image and conda file and disable dynamic package install #3456

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Pin mlflow image and conda file and disable dynamic package install
arunsu Dec 5, 2024
413409e
Set environment var
arunsu Dec 5, 2024
93da41e
Set model folder env var
arunsu Dec 6, 2024
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2 changes: 1 addition & 1 deletion cli/deploy-managed-online-endpoint-ncd.sh
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Expand Up @@ -34,7 +34,7 @@ model_archive=$(az ml model archive -n $AML_LIGHTGBM_MODEL_NAME --version 1 || t


# <create_sklearn_deployment>
az ml online-deployment create --name sklearn-deployment --endpoint $ENDPOINT_NAME -f endpoints/online/ncd/sklearn-deployment.yaml --all-traffic
az ml online-deployment create --name sklearn-deployment --endpoint $ENDPOINT_NAME -f endpoints/online/ncd/sklearn-deployment-with-image.yaml --all-traffic
# </create_sklearn_deployment>

deploy_status=`az ml online-deployment show --name sklearn-deployment --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv`
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15 changes: 15 additions & 0 deletions cli/endpoints/online/ncd/sklearn-deployment-with-image.yaml
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@@ -0,0 +1,15 @@
$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
name: sklearn-image
endpoint_name: my-endpoint
model:
name: mir-sample-sklearn-mlflow-model
version: 1
path: sklearn-diabetes/model
type: mlflow_model
environment:
image: mcr.microsoft.com/azureml/mlflow-ubuntu20.04-py38-cpu-inference
conda_file: sklearn-diabetes/environment/conda.yaml
environment_variables:
MLFLOW_MODEL_FOLDER: model
instance_type: Standard_DS3_v2
instance_count: 1
1 change: 1 addition & 0 deletions cli/endpoints/online/ncd/sklearn-diabetes/environment/conda.yaml
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Expand Up @@ -9,5 +9,6 @@ dependencies:
- cloudpickle==2.2.1
- psutil==5.9.4
- pandas==2.0.0
- numpy==1.26.4
- azureml-inference-server-http
name: mlflow-env
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