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version upgrade #3870

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13e1a3f
version upgrade
Chakradhar886 Apr 11, 2026
adf4cf0
fix: pin mlflow-skinny==2.8.0 to prevent version mismatch with base i…
Chakradhar886 Apr 11, 2026
b8cc201
fix: add setuptools dep for pkg_resources required by mlflow 2.8.0
Chakradhar886 Apr 11, 2026
c6f9fe4
fix: add extra_pip_requirements to log_model for inference container …
Chakradhar886 Apr 11, 2026
8b23476
fix: use custom scoring script and inference env for deployment
Chakradhar886 Apr 11, 2026
4b818fe
fix: simplify inference env - remove inference-schema to fix image build
Chakradhar886 Apr 12, 2026
057de1e
fix: remove mlflow from inference env, use joblib to load model directly
Chakradhar886 Apr 12, 2026
9b7c04d
fix: use save_model+register_model instead of log_model, revert to no…
Chakradhar886 Apr 12, 2026
6725978
fix: register model via Azure ML SDK, avoid all mlflow remote APIs
Chakradhar886 Apr 12, 2026
8743fa4
fix: add setuptools as pip dep to ensure pkg_resources survives pip i…
Chakradhar886 Apr 12, 2026
3ece1ec
fix: save model to outputs/ dir and fix registration artifact path
Chakradhar886 Apr 12, 2026
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70 changes: 51 additions & 19 deletions tutorials/azureml-in-a-day/azureml-in-a-day.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -187,18 +187,21 @@
" - python=3.10\n",
" - numpy=1.26.4\n",
" - pip=24.0\n",
" - setuptools\n",
" - scikit-learn=1.5.1\n",
" - scipy=1.10\n",
" - pandas=1.5.3\n",
" - pip:\n",
" - setuptools\n",
" - inference-schema[numpy-support]==1.3.0\n",
" - xlrd==2.0.1\n",
" - mlflow\n",
" - azureml-mlflow\n",
" - mlflow==2.8.0\n",
" - mlflow-skinny==2.8.0\n",
" - azureml-mlflow==1.51.0\n",
" - psutil\n",
" - tqdm\n",
" - ipykernel~=6.29\n",
" - matplotlib"
" - matplotlib\n"
]
},
{
Expand Down Expand Up @@ -332,7 +335,7 @@
" mlflow.start_run()\n",
"\n",
" # enable autologging\n",
" mlflow.sklearn.autolog()\n",
" mlflow.sklearn.autolog(log_models=False)\n",
"\n",
" ###################\n",
" #<prepare the data>\n",
Expand Down Expand Up @@ -386,18 +389,12 @@
" ##########################\n",
" #<save and register model>\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",
" # Save model to the outputs directory (auto-captured by AzureML)\n",
" print(\"Saving the model via MLFlow\")\n",
" model_output_path = os.path.join(\"outputs\", args.registered_model_name)\n",
" mlflow.sklearn.save_model(\n",
" sk_model=clf,\n",
" path=os.path.join(args.registered_model_name, \"trained_model\"),\n",
" path=model_output_path,\n",
" )\n",
" ###########################\n",
" #</save and register model>\n",
Expand All @@ -407,7 +404,7 @@
" mlflow.end_run()\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()"
" main()\n"
]
},
{
Expand Down Expand Up @@ -477,7 +474,29 @@
},
"outputs": [],
"source": [
"ml_client.create_or_update(job)"
"returned_job = ml_client.create_or_update(job)\n",
"aml_url = returned_job.studio_url\n",
"print(\"Monitor your job at\", aml_url)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"name": "wait_for_job"
},
"outputs": [],
"source": [
"import time\n",
"\n",
"# Wait for the job to complete\n",
"status = ml_client.jobs.get(returned_job.name).status\n",
"while status not in [\"Completed\", \"Failed\", \"Canceled\"]:\n",
" time.sleep(30)\n",
" status = ml_client.jobs.get(returned_job.name).status\n",
" print(f\"Job status: {status}\")\n",
"\n",
"print(f\"Job completed with status: {status}\")\n"
]
},
{
Expand Down Expand Up @@ -620,10 +639,23 @@
},
"outputs": [],
"source": [
"# Let's pick the latest version of the model\n",
"from azure.ai.ml.entities import Model\n",
"from azure.ai.ml.constants import AssetTypes\n",
"\n",
"# Register the model from the job output\n",
"run_model = Model(\n",
" path=f\"azureml://jobs/{returned_job.name}/outputs/artifacts/paths/outputs/{registered_model_name}/\",\n",
" name=registered_model_name,\n",
" type=AssetTypes.MLFLOW_MODEL,\n",
" description=\"Model created from run.\",\n",
")\n",
"ml_client.models.create_or_update(run_model)\n",
"\n",
"# Get the latest version\n",
"latest_model_version = max(\n",
" [int(m.version) for m in ml_client.models.list(name=registered_model_name)]\n",
")"
")\n",
"print(f\"Registered model version: {latest_model_version}\")\n"
]
},
{
Expand Down Expand Up @@ -658,7 +690,7 @@
" instance_count=1,\n",
")\n",
"\n",
"blue_deployment = ml_client.begin_create_or_update(blue_deployment).result()"
"blue_deployment = ml_client.begin_create_or_update(blue_deployment).result()\n"
]
},
{
Expand Down
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