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Fix EOL base image and MLflow autolog in e2e-ml-workflow notebook #3860

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Fix EOL base image and MLflow autolog in e2e-ml-workflow notebook #3860

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2a88d23
Fix EOL base image and MLflow autolog in e2e-ml-workflow notebook
Chakradhar886 Apr 7, 2026
8fde1d4
Bump environment version to 0.1.2 to avoid conflict with existing reg…
Chakradhar886 Apr 7, 2026
076a9e4
Disable MLflow logged-models API to fix log_model() 404 error
Chakradhar886 Apr 7, 2026
52c0497
Replace log_model with save_model+log_artifacts+register_model to avo…
Chakradhar886 Apr 7, 2026
6b9a29a
Use MlflowClient for model registration to bypass logged-models API
Chakradhar886 Apr 7, 2026
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32 changes: 19 additions & 13 deletions tutorials/e2e-ds-experience/e2e-ml-workflow.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -344,8 +344,8 @@
" description=\"Custom environment for Credit Card Defaults pipeline\",\n",
" tags={\"scikit-learn\": \"0.24.2\"},\n",
" conda_file=os.path.join(dependencies_dir, \"conda.yaml\"),\n",
" image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest\",\n",
" version=\"0.1.1\",\n",
" image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu22.04:latest\",\n",
" version=\"0.1.2\",\n",
")\n",
"pipeline_job_env = ml_client.environments.create_or_update(pipeline_job_env)\n",
"\n",
Expand Down Expand Up @@ -610,6 +610,7 @@
"import os\n",
"import pandas as pd\n",
"import mlflow\n",
"from mlflow.exceptions import MlflowException\n",
"\n",
"\n",
"def select_first_file(path):\n",
Expand All @@ -627,7 +628,7 @@
"mlflow.start_run()\n",
"\n",
"# enable autologging\n",
"mlflow.sklearn.autolog()\n",
"mlflow.sklearn.autolog(log_models=False)\n",
"\n",
"os.makedirs(\"./outputs\", exist_ok=True)\n",
"\n",
Expand Down Expand Up @@ -674,26 +675,31 @@
"\n",
" print(classification_report(y_test, y_pred))\n",
"\n",
" # Registering the model to the workspace\n",
" print(\"Registering the model via MLFlow\")\n",
" mlflow.sklearn.log_model(\n",
" sk_model=clf,\n",
" registered_model_name=args.registered_model_name,\n",
" artifact_path=args.registered_model_name,\n",
" )\n",
"\n",
" # Saving the model to a file\n",
" model_path = os.path.join(args.model, \"trained_model\")\n",
" mlflow.sklearn.save_model(\n",
" sk_model=clf,\n",
" path=os.path.join(args.model, \"trained_model\"),\n",
" path=model_path,\n",
" )\n",
"\n",
" # Registering the model to the workspace\n",
" print(\"Registering the model via MLFlow\")\n",
" mlflow.log_artifacts(model_path, artifact_path=args.registered_model_name)\n",
" run_id = mlflow.active_run().info.run_id\n",
" model_uri = f\"runs:/{run_id}/{args.registered_model_name}\"\n",
" client = mlflow.tracking.MlflowClient()\n",
" try:\n",
" client.create_registered_model(args.registered_model_name)\n",
" except mlflow.exceptions.MlflowException:\n",
" pass # model already exists\n",
" client.create_model_version(args.registered_model_name, model_uri, run_id)\n",
"\n",
" # Stop Logging\n",
" mlflow.end_run()\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()"
" main()\n"
]
},
{
Expand Down
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